Sample records for information materials image

  1. Materials Research in the Information Age

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Materials Research in the Information Age Accelerating Advanced Material Development NERSC Science Gateway a 'Google of Material Properties' October 31, 2011 | Tags: Materials...

  2. Supporting Information Materials and Methods

    E-Print Network [OSTI]

    Collins, Steven H.

    1 Supporting Information Materials and Methods Description of Energy-Recycling Artificial Foot The energy-recycling artificial foot was comprised of six component groups: the attachment interface, the toe The prosthesis simulator boot weighed 1.30 kg, and the lift shoe weighed 1.42 kg, with each adding approximately

  3. Information efficiency in hyperspectral imaging systems

    E-Print Network [OSTI]

    Reichenbach, Stephen E.

    Information efficiency in hyperspectral imaging systems Stephen E. Reichenbach University develop a method for assessing the in- formation density and efficiency of hyperspectral imaging systems width can efficiently gather information about a scene by allocating bandwidth among the bands according

  4. Enabling Informed Adaptation of Reformed Instructional Materials

    E-Print Network [OSTI]

    Elby, Andy

    Enabling Informed Adaptation of Reformed Instructional Materials Rachel E. Scherr and Andrew Elby 20742 USA Abstract. Instructors inevitably need to adapt even the best reform materials to suit instructors, and video clips of students working on the materials. Our materials thus facilitate their own

  5. Three Dimensional Molecular Imaging for Lignocellulosic Materials

    SciTech Connect (OSTI)

    Bohn, Paul W.; Sweedler, Jonathan V.

    2011-06-09T23:59:59.000Z

    The development of high efficiency, inexpensive processing protocols to render biomass components into fermentable substrates for the sequential processing of cell wall components into fuels and important feedstocks for the biorefinery of the future is a key goal of the national roadmap for renewable energy. Furthermore, the development of such protocols depends critically on detailed knowledge of the spatial and temporal infiltration of reagents designed to remove and separate the phenylpropenoid heteropolymer (lignin) from the processable sugar components sequestered in the rigid cell walls of plants. A detailed chemical and structural understanding of this pre-enzymatic processing in space and time was the focus of this program. We worked to develop new imaging strategies that produce real-time molecular speciation information in situ; extract sub-surface information about the effects of processing; and follow the spatial and temporal characteristics of the molecular species in the matrix and correlate this complex profile with saccharification. Spatially correlated SIMS and Raman imaging were used to provide high quality, high resolution subcellular images of Miscanthus cross sections. Furthermore, the combination of information from the mass spectrometry and Raman scattering allows specific chemical assignments of observed structures, difficult to assign from either imaging approach alone and lays the foundation for subsequent heterocorrelated imaging experiments targeted at more challenging biological systems, such as the interacting plant-microbe systems relevant to the rhizosphere.

  6. Advanced materials: Information and analysis needs

    SciTech Connect (OSTI)

    Curlee, T.R.; Das, S.; Lee, R.; Trumble, D.

    1990-09-01T23:59:59.000Z

    This report presents the findings of a study to identify the types of information and analysis that are needed for advanced materials. The project was sponsored by the US Bureau of Mines (BOM). It includes a conceptual description of information needs for advanced materials and the development and implementation of a questionnaire on the same subject. This report identifies twelve fundamental differences between advanced and traditional materials and discusses the implications of these differences for data and analysis needs. Advanced and traditional materials differ significantly in terms of physical and chemical properties. Advanced material properties can be customized more easily. The production of advanced materials may differ from traditional materials in terms of inputs, the importance of by-products, the importance of different processing steps (especially fabrication), and scale economies. The potential for change in advanced materials characteristics and markets is greater and is derived from the marriage of radically different materials and processes. In addition to the conceptual study, a questionnaire was developed and implemented to assess the opinions of people who are likely users of BOM information on advanced materials. The results of the questionnaire, which was sent to about 1000 people, generally confirm the propositions set forth in the conceptual part of the study. The results also provide data on the categories of advanced materials and the types of information that are of greatest interest to potential users. 32 refs., 1 fig., 12 tabs.

  7. Serious Materials | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere IRaghuraji Agro Industries Pvt Ltd JumpInformationScottsOklahoma: EnergySeoul Marine Co|Serious

  8. PET IMAGE RECONSTRUCTION USING ANATOMICAL INFORMATION THROUGH MUTUAL INFORMATION BASED PRIORS: A SCALE SPACE APPROACH

    E-Print Network [OSTI]

    Rangarajan, Anand

    PET IMAGE RECONSTRUCTION USING ANATOMICAL INFORMATION THROUGH MUTUAL INFORMATION BASED PRIORS prior for incorpo- rating information from co-registered anatomical images into PET image reconstruction using mutual information based rigid registration. PET data are then simulated from the au

  9. Advanced Imaging and Ultra-fast Material Probing With Inverse...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Imaging and Ultra-fast Material Probing With Inverse Compton Scattering A proposal to the Brookhaven Accelerator Test Facility Gerard Andonian, Alberto Bacci, Ubaldo...

  10. Interactive image quantification tools in nuclear material forensics

    SciTech Connect (OSTI)

    Porter, Reid B [Los Alamos National Laboratory; Ruggiero, Christy [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory; Harvey, Neal [Los Alamos National Laboratory; Kelly, Pat [Los Alamos National Laboratory; Scoggins, Wayne [Los Alamos National Laboratory; Tandon, Lav [Los Alamos National Laboratory

    2011-01-03T23:59:59.000Z

    Morphological and microstructural features visible in microscopy images of nuclear materials can give information about the processing history of a nuclear material. Extraction of these attributes currently requires a subject matter expert in both microscopy and nuclear material production processes, and is a time consuming, and at least partially manual task, often involving multiple software applications. One of the primary goals of computer vision is to find ways to extract and encode domain knowledge associated with imagery so that parts of this process can be automated. In this paper we describe a user-in-the-loop approach to the problem which attempts to both improve the efficiency of domain experts during image quantification as well as capture their domain knowledge over time. This is accomplished through a sophisticated user-monitoring system that accumulates user-computer interactions as users exploit their imagery. We provide a detailed discussion of the interactive feature extraction and segmentation tools we have developed and describe our initial results in exploiting the recorded user-computer interactions to improve user productivity over time.

  11. Hyperspectral Imaging | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluatingGroup | OpenHunan Runhua New Energy DevelopmentList ofHyperspectral Imaging

  12. Image-Based Reconstruction of Spatially Varying Materials

    E-Print Network [OSTI]

    Goesele, Michael

    Image-Based Reconstruction of Spatially Varying Materials Hendrik P. A. Lensch1 Jan Kautz1 Michael material properties is an important step towards photorealistic rendering. Many real-world objects are composed of a number of materials that often show subtle changes even within a single material. Thus

  13. Image Logs | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdfGetecGtel JumpCounty, Texas:ITCSolidIdaho‎Information RiverIllumitex Jump

  14. Multispectral Imaging | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal Pwer Plant JumpMarysville,Missoula,MontereyHill,SpurrMulberry,EnergyEnergy Informationform

  15. Holographic imaging of natural-fiber-containing materials

    DOE Patents [OSTI]

    Bunch, Kyle J [Richland, WA; Tucker, Brian J [Pasco, WA; Severtsen, Ronald H [Richland, WA; Hall, Thomas E [Kennewick, WA; McMakin, Douglas L [Richland, WA; Lechelt, Wayne M [West Richland, WA; Griffin, Jeffrey W [Kennewick, WA; Sheen, David M [Richland, WA

    2010-12-21T23:59:59.000Z

    The present invention includes methods and apparatuses for imaging material properties in natural-fiber-containing materials. In particular, the images can provide quantified measures of localized moisture content. Embodiments of the invention utilize an array of antennas and at least one transceiver to collect amplitude and phase data from radiation interacting with the natural-fiber-containing materials. The antennas and the transceivers are configured to transmit and receive electromagnetic radiation at one or more frequencies, which are between 50 MHz and 1 THz. A conveyance system passes the natural-fiber-containing materials through a field of view of the array of antennas. A computing device is configured to apply a synthetic imaging algorithm to construct a three-dimensional image of the natural-fiber-containing materials that provides a quantified measure of localized moisture content. The image and the quantified measure are both based on the amplitude data, the phase data, or both.

  16. Analyzing lead information from SAR images

    E-Print Network [OSTI]

    Van Dyne, M. M.; Tsatsoulis, Costas; Fetterer, F.

    1998-03-01T23:59:59.000Z

    means of gathering important information about the sea ice cover and its climatic influence, This paper describes: 1) a method for extracting and analyzing leads from ERS-1 synthetic aperture radar (SAR) images classified by ice type and 2) the results...

  17. Supplemental Material for "Efficient MR Image Reconstruction for Compressed MR Imaging"

    E-Print Network [OSTI]

    Huang, Junzhou

    D MR images: cardiac, brain, chest and artery respectively. Figure 1, 2, 3 and 4 shows the visual complexity. (a) (b) (c) (d) (e) (f) Fig. 1. Cardiac MR image reconstruction from 20% sampling (a) OriginalSupplemental Material for "Efficient MR Image Reconstruction for Compressed MR Imaging" Paper ID

  18. Serious Materials (Colorado) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere IRaghuraji Agro Industries Pvt Ltd JumpInformationScottsOklahoma: EnergySeoul Marine Co|Serious Materials

  19. Atlas Material Testing Solutions | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualProperty EditCalifornia: Energy ResourcesInformationGuide | OpenAthensAtlas Material

  20. Thermal imaging analysis of material structures and defects.

    SciTech Connect (OSTI)

    Sun, J. G.; Nuclear Engineering Division

    2008-01-01T23:59:59.000Z

    A numerical one-dimensional (1D) heat-transfer model was developed to simulate the surface temperature response under one-sided pulsed thermal imaging for plate samples with internal material variations including different optical and thermal properties, multilayer structures, and defect distributions (delaminations). The simulation results showed the complexity and subtle differences of the thermal imaging response to the material variations. With further development in data processing technologies, it is expected that thermal imaging may be used to detect and predict these material property variations.

  1. Help:Images | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdfGetec AG|InformationInformation Station - South IcelandImages Jump to:

  2. Micro Materials Japan Inc | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluatingGroup |JilinLu an GroupInformationMexicoInformationMichaelMicro

  3. Materials Research in the Information Age

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of Science (SC)Integrated Codes |IsLove Your Home andDisposition | NationalMaterialsMPA

  4. The Use of Energy Information in Plastic Scintillator Material

    SciTech Connect (OSTI)

    Ely, James H.; Anderson, Kevin K.; Bates, Derrick J.; Kouzes, Richard T.; Lopresti, Charles A.; Runkle, Robert C.; Siciliano, Edward R.; Weier, Dennis R.

    2008-06-15T23:59:59.000Z

    Plastic scintillator material is often used for gamma-ray detection in many applications due to its relatively good sensitivity and cost-effectiveness compared to other detection materials. However, due to the dominant Compton scattering interaction mechanism, full energy peaks are not observed in plastic scintillator spectra and isotopic identification is impossible. Typically plastic scintillator detectors are solely gross count detectors. In some safeguards and security applications, such as radiation portal monitors for vehicle screening, naturally-occurring radioactive material (NORM) often triggers radiation alarms and results in innocent or nuisance alarms. The limited energy information from plastic scintillator material can be used to discriminate the NORM from targeted materials and reduce the nuisance alarm rate. An overview of the utilization of the energy information from plastic scintillator material will be presented, with emphasis on the detection capabilities and potential limitations for safeguards and security applications. (PIET-43741-TM-490)

  5. advanced materials information: Topics by E-print Network

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    materials information First Page Previous Page 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Next Page Last Page Topic Index 1 Department of Advanced Materials...

  6. REDD+ Training Materials | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I GeothermalPotentialBiopowerSolidGenerationMethodInformationeNevada <REC Solar (Colorado)

  7. Henan Xindaxin Materials XDXM | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluatingGroup | Open Energy InformationHebeiProgram Jump to:HelpTianguan FuelHenan

  8. Enviva Materials LLC | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualPropertyd8c-a9ae-f8521cbb8489 No revision|LLC Place:EnergyLiteInformation

  9. Defense Nuclear Material Stewardship Integrated Inventory Information Management System (IIIMS).

    SciTech Connect (OSTI)

    Aas, Christopher A.; Lenhart, James E.; Bray, Olin H.; Witcher, Christina Jenkin

    2004-11-01T23:59:59.000Z

    Sandia National Laboratories was tasked with developing the Defense Nuclear Material Stewardship Integrated Inventory Information Management System (IIIMS) with the sponsorship of NA-125.3 and the concurrence of DOE/NNSA field and area offices. The purpose of IIIMS was to modernize nuclear materials management information systems at the enterprise level. Projects over the course of several years attempted to spearhead this modernization. The scope of IIIMS was broken into broad enterprise-oriented materials management and materials forecasting. The IIIMS prototype was developed to allow multiple participating user groups to explore nuclear material requirements and needs in detail. The purpose of material forecasting was to determine nuclear material availability over a 10 to 15 year period in light of the dynamic nature of nuclear materials management. Formal DOE Directives (requirements) were needed to direct IIIMS efforts but were never issued and the project has been halted. When restarted, duplicating or re-engineering the activities from 1999 to 2003 is unnecessary, and in fact future initiatives can build on previous work. IIIMS requirements should be structured to provide high confidence that discrepancies are detected, and classified information is not divulged. Enterprise-wide materials management systems maintained by the military can be used as overall models to base IIIMS implementation concepts upon.

  10. Material characterization using a hyperspectral infrared imaging spectrometer

    SciTech Connect (OSTI)

    Aimonetti, W D; Bixler, J V; Roberts, R S

    1998-10-30T23:59:59.000Z

    Fourier transform spectroscopy has found application in many areas including chemometrics, biomedical and biochemical studies, and atmospheric chemistry. This paper describes an investigation into the application of the LLNL Hyperspectral Infrared Imaging Spectrometer (HIRIS) to the non-destructive evaluation of man-made and natural materials. We begin by describing the HIRIS system and the objects studied in the investigation. Next, we describe the technique used to collect the hyperspec- tral imagery, and discuss the processing required to transform the data into usable form. We then describe a technique to analyze the data, and provide some preliminary results.

  11. Information-efficient spectral imaging sensor

    DOE Patents [OSTI]

    Sweatt, William C. (Albuquerque, NM); Gentry, Stephen M. (Albuquerque, NM); Boye, Clinton A. (Albuquerque, NM); Grotbeck, Carter L. (Albuquerque, NM); Stallard, Brian R. (Albuquerque, NM); Descour, Michael R. (Tucson, AZ)

    2003-01-01T23:59:59.000Z

    A programmable optical filter for use in multispectral and hyperspectral imaging. The filter splits the light collected by an optical telescope into two channels for each of the pixels in a row in a scanned image, one channel to handle the positive elements of a spectral basis filter and one for the negative elements of the spectral basis filter. Each channel for each pixel disperses its light into n spectral bins, with the light in each bin being attenuated in accordance with the value of the associated positive or negative element of the spectral basis vector. The spectral basis vector is constructed so that its positive elements emphasize the presence of a target and its negative elements emphasize the presence of the constituents of the background of the imaged scene. The attenuated light in the channels is re-imaged onto separate detectors for each pixel and then the signals from the detectors are combined to give an indication of the presence or not of the target in each pixel of the scanned scene. This system provides for a very efficient optical determination of the presence of the target, as opposed to the very data intensive data manipulations that are required in conventional hyperspectral imaging systems.

  12. Effective Materials Property Information Management for the 21st Century

    SciTech Connect (OSTI)

    Ren, Weiju [ORNL; Cebon, David [Cambridge University; Barabash, Oleg M [ORNL

    2011-01-01T23:59:59.000Z

    This paper discusses key principles for the development of materials property information management software systems. There are growing needs for automated materials information management in various organizations. In part these are fuelled by the demands for higher efficiency in material testing, product design and engineering analysis. But equally important, organizations are being driven by the needs for consistency, quality and traceability of data, as well as control of access to proprietary or sensitive information. Further, the use of increasingly sophisticated nonlinear, anisotropic and multi-scale engineering analyses requires both processing of large volumes of test data for development of constitutive models and complex materials data input for Computer-Aided Engineering (CAE) software. And finally, the globalization of economy often generates great needs for sharing a single gold source of materials information between members of global engineering teams in extended supply-chains. Fortunately material property management systems have kept pace with the growing user demands and evolved to versatile data management systems that can be customized to specific user needs. The more sophisticated of these provide facilities for: (i) data management functions such as access, version, and quality controls; (ii) a wide range of data import, export and analysis capabilities; (iii) data pedigree traceability mechanisms; (iv) data searching, reporting and viewing tools; and (v) access to the information via a wide range of interfaces. In this paper the important requirements for advanced material data management systems, future challenges and opportunities such as automated error checking, data quality characterization, identification of gaps in datasets, as well as functionalities and business models to fuel database growth and maintenance are discussed.

  13. MATERIALS AND INFORMATION FLOWS FOR HVAC DUCTWORK FABRICATION AND SITE

    E-Print Network [OSTI]

    Tommelein, Iris D.

    MATERIALS AND INFORMATION FLOWS FOR HVAC DUCTWORK FABRICATION AND SITE INSTALLATION Matt Holzemer,1, and air-conditioning (HVAC) systems requires a set of complex activities and handoffs between multiple architecture-, engineering-, and construction practitioners. This paper highlights one part of the HVAC

  14. Supply chain management (SCM) involves the management of materials and information across the entire supply chain. This includes raw material

    E-Print Network [OSTI]

    Calgary, University of

    Supply chain management (SCM) involves the management of materials and information across this concentration will be required to take three core courses: Materials and Supply Chain Management, Transportation Purchasing identify global sources of materials, select vendors, and manage negotiati

  15. Imaging laser analysis of building materials - practical examples

    SciTech Connect (OSTI)

    Wilsch, G.; Schaurich, D.; Wiggenhauser, H. [BAM, Federal Institute for Materials Research and Testing, Berlin (Germany)

    2011-06-23T23:59:59.000Z

    The Laser induced Breakdown Spectroscopy (LIBS) is supplement and extension of standard chemical methods and SEM- or Micro-RFA-applications for the evaluation of building materials. As a laboratory method LIBS is used to gain color coded images representing composition, distribution of characteristic ions and/or ingress characteristic of damaging substances. To create a depth profile of element concentration a core has to be taken and split along the core axis. LIBS was proven to be able to detect all important elements in concrete, e. g. Chlorine, Sodium or Sulfur, which are responsible for certain degradation mechanisms and also light elements like lithium or hydrogen. Practical examples are given and a mobile system for on-site measurements is presented.

  16. Radiance: Synthetic Imaging System | Open Energy Information

    Open Energy Info (EERE)

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  17. Help:Linked images | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluatingGroup | Open Energy InformationHebeiProgram Jump to:

  18. Shandong Linuo New Material Corp | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere IRaghuraji Agro Industries Pvt Ltd JumpInformationScottsOklahoma:SevinShamil Ayntrazi JumpMaterial Corp

  19. The perception of shape, lighting, and material properties in images

    E-Print Network [OSTI]

    O'Shea, James Patrick

    2012-01-01T23:59:59.000Z

    the true lighting direction. . . . . . . . . . . . . . . .rep- resent different lighting tilts on the test object. Thedirection. We found the lighting used in the center image

  20. Appraisal of a cementitious material for waste disposal: Neutron imaging studies of pore structure and sorptivity

    SciTech Connect (OSTI)

    McGlinn, Peter J., E-mail: pjm@ansto.gov.a [Australian Nuclear Science and Technology Organisation, New Illawarra Road, Lucas Heights, NSW 2234 (Australia); Beer, Frikkie C. de [South African Nuclear Energy Corporation (Necsa), Church Street West Extension, Pelindaba, Brits District, Pretoria 0001 (South Africa); Aldridge, Laurence P. [Australian Nuclear Science and Technology Organisation, New Illawarra Road, Lucas Heights, NSW 2234 (Australia); Radebe, Mabuti J.; Nshimirimana, Robert [South African Nuclear Energy Corporation (Necsa), Church Street West Extension, Pelindaba, Brits District, Pretoria 0001 (South Africa); Brew, Daniel R.M.; Payne, Timothy E.; Olufson, Kylie P. [Australian Nuclear Science and Technology Organisation, New Illawarra Road, Lucas Heights, NSW 2234 (Australia)

    2010-08-15T23:59:59.000Z

    Cementitious materials are conventionally used in conditioning intermediate and low level radioactive waste. In this study a candidate cement-based wasteform has been investigated using neutron imaging to characterise the wasteform for disposal in a repository for radioactive materials. Imaging showed both the pore size distribution and the extent of the cracking that had occurred in the samples. The rate of the water penetration measured both by conventional sorptivity measurements and neutron imaging was greater than in pastes made from Ordinary Portland Cement. The ability of the cracks to distribute the water through the sample in a very short time was also evident. The study highlights the significant potential of neutron imaging in the investigation of cementitious materials. The technique has the advantage of visualising and measuring, non-destructively, material distribution within macroscopic samples and is particularly useful in defining movement of water through the cementitious materials.

  1. Materials - Imaging atoms for better batteries ... | ornl.gov

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    in the material's atomic arrangement. These location-dependent changes rely on nickel swapping with lithium, and can raise or lower energy barriers for lithium-ion...

  2. Compton DIV: Using a Compton-Based Gamma-Ray Imager for Design Information Verification of Uranium Enrichment Plants

    SciTech Connect (OSTI)

    Burks, M; Verbeke, J; Dougan, A; Wang, T; Decman, D

    2009-07-04T23:59:59.000Z

    A feasibility study has been performed to determine the potential usefulness of Compton imaging as a tool for design information verification (DIV) of uranium enrichment plants. Compton imaging is a method of gamma-ray imaging capable of imaging with a 360-degree field of view over a broad range of energies. These systems can image a room (with a time span on the order of one hour) and return a picture of the distribution and composition of radioactive material in that room. The effectiveness of Compton imaging depends on the sensitivity and resolution of the instrument as well the strength and energy of the radioactive material to be imaged. This study combined measurements and simulations to examine the specific issue of UF{sub 6} gas flow in pipes, at various enrichment levels, as well as hold-up resulting from the accumulation of enriched material in those pipes. It was found that current generation imagers could image pipes carrying UF{sub 6} in less than one hour at moderate to high enrichment. Pipes with low enriched gas would require more time. It was also found that hold-up was more amenable to this technique and could be imaged in gram quantities in a fraction of an hour. another questions arises regarding the ability to separately image two pipes spaced closely together. This depends on the capabilities of the instrument in question. These results are described in detail. In addition, suggestions are given as to how to develop Compton imaging as a tool for DIV.

  3. Holographic imaging based on time-domain data of natural-fiber-containing materials

    DOE Patents [OSTI]

    Bunch, Kyle J.; McMakin, Douglas L.

    2012-09-04T23:59:59.000Z

    Methods and apparatuses for imaging material properties in natural-fiber-containing materials can utilize time-domain data. In particular, images can be constructed that provide quantified measures of localized moisture content. For example, one or more antennas and at least one transceiver can be configured to collect time-domain data from radiation interacting with the natural-fiber-containing materials. The antennas and the transceivers are configured to transmit and receive electromagnetic radiation at one or more frequencies, which are between 50 MHz and 1 THz, according to a time-domain impulse function. A computing device is configured to transform the time-domain data to frequency-domain data, to apply a synthetic imaging algorithm for constructing a three-dimensional image of the natural-fiber-containing materials, and to provide a quantified measure of localized moisture content based on a pre-determined correlation of moisture content to frequency-domain data.

  4. Apparatus for imaging liquid and dielectric materials with scanning polarization force microscopy

    DOE Patents [OSTI]

    Hu, Jun (Berkeley, CA); Ogletree, D. Frank (El Cerrito, CA); Salmeron, Miguel (El Cerrito, CA); Xiao, Xudong (Kowloon, CN)

    1998-01-01T23:59:59.000Z

    The invention images dielectric polarization forces on surfaces induced by a charged scanning force microscope (SFM) probe tip. On insulators, the major contribution to the surface polarizability at low frequencies is from surface ions. The mobility of these ions depends strongly on the humidity. Using the inventive SFM, liquid films, droplets, and other weakly adsorbed materials have been imaged.

  5. Method for imaging liquid and dielectric materials with scanning polarization force microscopy

    DOE Patents [OSTI]

    Hu, Jun (Berkeley, CA); Ogletree, D. Frank (El Cerrito, CA); Salmeron, Miguel (El Cerrito, CA); Xiao, Xudong (Kowloon, CN)

    1999-01-01T23:59:59.000Z

    The invention images dielectric polarization forces on surfaces induced by a charged scanning force microscope (SFM) probe tip. On insulators, the major contribution to the surface polarizability at low frequencies is from surface ions. The mobility of these ions depends strongly on the humidity. Using the inventive SFM, liquid films, droplets, and other weakly adsorbed materials have been imaged.

  6. SIMULATION OF ENERGY SELECTIVE X-RAY IMAGES FOR MATERIAL DIS-CRIMINATION

    E-Print Network [OSTI]

    Hickman, Mark

    SIMULATION OF ENERGY SELECTIVE X-RAY IMAGES FOR MATERIAL DIS- CRIMINATION Rune S Thing1 , Syen J Carlo model is presented to evaluate the clinical benefits of optimal energy bins in spectral X-ray imaging, using the BEAMnrc code system. While energy resolving photon counting detectors have been

  7. Digital Material Fabrication Using Mask-Image-Projection-based Stereolithography

    E-Print Network [OSTI]

    Chen, Yong

    on its PolyJet Matrix Technology, these three-dimensional (3D) printers are capable of manufacturing is motivated by the recent 3D printer development especially by the digital material fabrication in which two

  8. CLARIFICATION AND VALIDATION OF NEED FOR EDUCATIONAL/INFORMATIONAL MATERIALS TO

    E-Print Network [OSTI]

    CLARIFICATION AND VALIDATION OF NEED FOR EDUCATIONAL/INFORMATIONAL MATERIALS TO SUPPORT Center for Homeland Security Clarification and Validation of Need for Educational/Informational Materials through the review of published material was shared with interviewees prior to the telephone interviews

  9. Real-Time Quantitative Imaging of Failure Events in Materials...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Monday, 25 March 2013 00:00 Gathering information on the evolution of small cracks in ceramic matrix composites used in hostile environments such as in gas turbines and...

  10. X-ray backscatter imaging of nuclear materials

    DOE Patents [OSTI]

    Chapman, Jeffrey Allen; Gunning, John E; Hollenbach, Daniel F; Ott, Larry J; Shedlock, Daniel

    2014-09-30T23:59:59.000Z

    The energy of an X-ray beam and critical depth are selected to detect structural discontinuities in a material having an atomic number Z of 57 or greater. The critical depth is selected by adjusting the geometry of a collimator that blocks backscattered radiation so that backscattered X-ray originating from a depth less than the critical depth is not detected. Structures of Lanthanides and Actinides, including nuclear fuel rod materials, can be inspected for structural discontinuities such as gaps, cracks, and chipping employing the backscattered X-ray.

  11. THERMAL IMAGING OF ACTIVE MAGNETIC REGERNERATOR MCE MATERIALS DURING OPERATION

    SciTech Connect (OSTI)

    Shassere, Benjamin [ORNL] [ORNL; West, David L [ORNL] [ORNL; Abdelaziz, Omar [ORNL] [ORNL; Evans III, Boyd Mccutchen [ORNL] [ORNL

    2012-01-01T23:59:59.000Z

    An active magnetic regenerator (AMR) prototype was constructed that incorporates a Gd sheet into the regenerator wall to enable visualization of the system s thermal transients. In this experiment, the thermal conditions inside the AMR are observed under a variety of operating conditions. An infrared (IR) camera is employed to visualize the thermal transients within the AMR. The IR camera is used to visually and quantitatively evaluate the temperature difference and thus giving means to calculate the performance of the system under the various operating conditions. Thermal imaging results are presented for two differing experimental test runs. Real time imaging of the thermal state of the AMR has been conducted while operating the system over a range of conditions. A 1 Tesla twin-coil electromagnet (situated on a C frame base) is used for this experiment such that all components are stationary during testing. A modular, linear reciprocating system has been realized in which the effects of regenerator porosity and utilization factor can be investigated. To evaluate the performance variation in porosity and utilization factor the AMR housing was constructed such that the plate spacing of the Gd sheets may be varied. Each Gd sheet has dimensions of 38 mm wide and 66 mm long with a thickness of 1 mm and the regenerator can hold a maximum of 29 plates with a spacing of 0.25 mm. Quantitative and thermal imaging results are presented for several regenerator configurations.

  12. No-reference image quality assessment and blind deblurring with sharpness metrics exploiting Fourier phase information

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    , total variation, Fourier transform, random phase noise, no-reference image quality assessment, imageNo-reference image quality assessment and blind deblurring with sharpness metrics exploiting information of an image to achieve quality assessment, edge detection, and, more recently, blind deblurring

  13. Center for Nanophase Materials Sciences (CNMS) - Imaging Functionality

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisiting the TWPSuccessAlamos Laboratory Nastasi image UserFacilities

  14. Holzbau : timber construction and material information exchanges for the design of complex geometrical structures

    E-Print Network [OSTI]

    Aparicio, German Walter, Jr

    2010-01-01T23:59:59.000Z

    In a universe made of bits where everything is continuously computing and nature itself is processing information everyday, what is it that our materials compute? Specifically, what are the bits of information registered ...

  15. Engineering and information technology: Using imaging to reengineer business

    SciTech Connect (OSTI)

    Norton, F.J.

    1996-06-10T23:59:59.000Z

    Image processing can be a great asset to business process reengineering. This paper examines image processing`s impact on workflow and attempts to list the questions that should be addressed before imaging technology is introduced.

  16. Microsoft Word - FINAL Materials Strategy Request for Information...

    Broader source: Energy.gov (indexed) [DOE]

    as vehicles, wind turbines or PV cells) and other energy related processes (such as oil refining). * What is the material requirement of these energy technology applications?...

  17. Ultra-low field nuclear magnetic resonance and magnetic resonance imaging to discriminate and identify materials

    DOE Patents [OSTI]

    Kraus, Robert H. (Los Alamos, NM); Matlashov, Andrei N. (Los Alamos, NM); Espy, Michelle A. (Los Alamos, NM); Volegov, Petr L. (Los Alamos, NM)

    2010-03-30T23:59:59.000Z

    An ultra-low magnetic field NMR system can non-invasively examine containers. Database matching techniques can then identify hazardous materials within the containers. Ultra-low field NMR systems are ideal for this purpose because they do not require large powerful magnets and because they can examine materials enclosed in conductive shells such as lead shells. The NMR examination technique can be combined with ultra-low field NMR imaging, where an NMR image is obtained and analyzed to identify target volumes. Spatial sensitivity encoding can also be used to identify target volumes. After the target volumes are identified the NMR measurement technique can be used to identify their contents.

  18. Recent Fast Neutron Imaging Measurements with the Fieldable Nuclear Materials Identification System

    SciTech Connect (OSTI)

    Wellington, Tracey [ORNL; Palles, Blake A [ORNL; Mullens, James Allen [ORNL; Mihalczo, John T [ORNL; Archer, Daniel E [ORNL; Thompson, Thad [ORNL; Britton Jr, Charles L [ORNL; Ezell, N Dianne Bull [ORNL; Ericson, Milton Nance [ORNL; Farquhar, Ethan [ORNL; Lind, Randall F [ORNL; Carter, Jake [ORNL

    2015-01-01T23:59:59.000Z

    This paper describes some recent fast neutron imaging measurements of the fieldable nuclear materials identification system (FNMIS) under development by the National Nuclear Security Administration (NNSA-NA-22) for possible future use in arms control and nonproliferation applications. The general configuration of FNMIS has been previously described, and a description of the application-specific integrated circuit (ASIC) electronics designed for FNMIS has been reported. This paper presents initial imaging measurements performed at ORNL with a Thermo Fisher API 120 DT generator and the fast-neutron imaging module of FNMIS.

  19. Multi-Material Decomposition Using Statistical Image Reconstruction in X-Ray CT

    E-Print Network [OSTI]

    Fessler, Jeffrey A.

    and Jeffrey A. Fessler Abstract--Dual-energy (DE) CT scans provide two sets of measurements at two different-mean-square (RMS) errors. Index Terms--Computed tomography, dual energy, multi- material decomposition, statistical image reconstruction I. INTRODUCTION Dual-energy (DE) CT reconstruction methods typically re- construct

  20. Semiconductor and Materials Company Inc SAMCO | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere IRaghuraji Agro Industries Pvt Ltd JumpInformationScotts Corners,EnergyInformation1987)Semafo

  1. Hunan Reshine New Material Co Ltd | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluatingGroup | Open EnergyInformationHorizonEnergyHubeiHumusInformationHunan

  2. Image Quality Assessment Based On Harmonics Gain/Loss Information

    E-Print Network [OSTI]

    Gunawan, I.P.

    Gunawan,I.P. Ghanbari,M. ICIP 2005. IEEE International Conference on Image Processing, 2005. pp I - 429-32 IEEE

  3. The U.S. national nuclear forensics library, nuclear materials information program, and data dictionary

    SciTech Connect (OSTI)

    Lamont, Stephen Philip [Los Alamos National Laboratory; Brisson, Marcia [DOE-IN; Curry, Michael [DEPT. OF STATE

    2011-02-17T23:59:59.000Z

    Nuclear forensics assessments to determine material process history requires careful comparison of sample data to both measured and modeled nuclear material characteristics. Developing centralized databases, or nuclear forensics libraries, to house this information is an important step to ensure all relevant data will be available for comparison during a nuclear forensics analysis and help expedite the assessment of material history. The approach most widely accepted by the international community at this time is the implementation of National Nuclear Forensics libraries, which would be developed and maintained by individual nations. This is an attractive alternative toan international database since it provides an understanding that each country has data on materials produced and stored within their borders, but eliminates the need to reveal any proprietary or sensitive information to other nations. To support the concept of National Nuclear Forensics libraries, the United States Department of Energy has developed a model library, based on a data dictionary, or set of parameters designed to capture all nuclear forensic relevant information about a nuclear material. Specifically, information includes material identification, collection background and current location, analytical laboratories where measurements were made, material packaging and container descriptions, physical characteristics including mass and dimensions, chemical and isotopic characteristics, particle morphology or metallurgical properties, process history including facilities, and measurement quality assurance information. While not necessarily required, it may also be valuable to store modeled data sets including reactor burn-up or enrichment cascade data for comparison. It is fully expected that only a subset of this information is available or relevant to many materials, and much of the data populating a National Nuclear Forensics library would be process analytical or material accountability measurement data as opposed to a complete forensic analysis of each material in the library.

  4. Zhongsheng Technology New Materials Ltd | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualProperty Edit withTianlinPapersWindey Wind GeneratingZhongsheng Technology New Materials Ltd

  5. Nantong Dongtai Electrical Material Co Ltd | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual Siteof Energy 2,AUDIT REPORTEnergyFarmsPower CoLongxingPartnersNREL releasesMaterial Co

  6. NS Solar Material Co Ltd | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluatingGroup |JilinLu anMicrogreenMoonNASA/AmesNS Solar Material Co Ltd Jump to:

  7. Edison Material Technology Center EMTEC | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model,DOEHazel Crest, Illinois:Edinburgh University aka Wave Power GroupMaterial

  8. Jinzhou Huari Silicon Material Co Ltd | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluatingGroup |Jilin Zhongdiantou New Energy Co LtdJinzhou Huari Silicon Material Co

  9. Federal Emergency Management Information System (FEMIS) Bill of Materials (BOM) for FEMIS Version 1.4.6

    SciTech Connect (OSTI)

    Downing, Timothy R.; Fangman, Patricia M.; Homer, Brian J.; Johnson, Daniel M.; Johnson, Ranata L.; Johnson, Sharon M.; Millard, W. David; Stoops, Lamar R.; Wood, Blanche M.

    1999-03-05T23:59:59.000Z

    Federal Emergency Management Information System (FEMIS) Bill of Materials (BOM) for FEMIS Version 1.4.6

  10. Chemical Emissions of Residential Materials and Products: Review of Available Information Environmental Energy Technologies Division

    E-Print Network [OSTI]

    -up approach of collecting and evaluating emissions data from construction and interior materials and commonChemical Emissions of Residential Materials and Products: Review of Available Information Building Technologies Program, Office of Energy Efficiency and Renewable Energy under DOE Contract No. DE

  11. Shaanxi Tianhong Silicon Material Co Ltd | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere IRaghuraji Agro Industries Pvt Ltd JumpInformationScottsOklahoma:Sevin RosenEnergy Jump to:Tianhong

  12. Guangzhou ChienSong Grind Material | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdfGetec AG| Open Energy InformationGettop Science Technology Co Ltd Jump

  13. Materials and Systems Research MSRI | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, searchOfRose Bend <Stevens JumpMassachusetts/Wind ResourcesInformationMSRI

  14. Center for Nanophase Materials Sciences (CNMS) - Local Information

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742EnergyOnItem NotEnergy,ARMForms About Batteriesmetal-organicNationalRelatedLOCAL INFORMATION AND

  15. Jiangsu Tehua New Materials Technology Co Ltd | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluatingGroup | OpenHunanInformationJames WatkinsTianlong MechanicalJiangsuJiangsu

  16. Jiangxi Jiahua Silicon Material Co Ltd | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluatingGroup | OpenHunanInformationJamesManufacturing | Open EnergyJiangxiJiangxi

  17. Jiangxi Jingde Semiconductor Materials Co Ltd | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluatingGroup | OpenHunanInformationJamesManufacturing | OpenJiangxi Jingde

  18. Jiangxi Sinoma New Solar Materials Co Ltd | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluatingGroup | OpenHunanInformationJamesManufacturing | OpenJiangxiJiangxi

  19. Huachang Silicon Material Co Ltd | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluatingGroup | Open EnergyInformationHorizon Fuel CellOpenEISouthHua an

  20. International Center for Materials Research ICMR | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnual SiteofEvaluatingGroup | OpenHunan RunhuaInnerInformation International Center

  1. Compendium of information on identification and testing of materials for plastic solar thermal collectors

    SciTech Connect (OSTI)

    McGinniss, V.D.; Sliemers, F.A.; Landstrom, D.K.; Talbert, S.G.

    1980-07-31T23:59:59.000Z

    This report is intended to organize and summarize prior and current literature concerning the weathering, aging, durability, degradation, and testing methodologies as applied to materials for plastic solar thermal collectors. Topics covered include (1) rate of aging of polymeric materials; (2) environmental factors affecting performance; (3) evaluation and prediction of service life; (4) measurement of physical and chemical properties; (5) discussion of evaluation techniques and specific instrumentation; (6) degradation reactions and mechanisms; (7) weathering of specific polymeric materials; and (8) exposure testing methodology. Major emphasis has been placed on defining the current state of the art in plastics degradation and on identifying information that can be utilized in applying appropriate and effective aging tests for use in projecting service life of plastic solar thermal collectors. This information will also be of value where polymeric components are utilized in the construction of conventional solar collectors or any application where plastic degradation and weathering are prime factors in material selection.

  2. Near-electrode imager

    DOE Patents [OSTI]

    Rathke, Jerome W. (Lockport, IL); Klingler, Robert J. (Westmont, IL); Woelk, Klaus (Wachtberg, DE); Gerald, II, Rex E. (Brookfield, IL)

    2000-01-01T23:59:59.000Z

    An apparatus, near-electrode imager, for employing nuclear magnetic resonance imaging to provide in situ measurements of electrochemical properties of a sample as a function of distance from a working electrode. The near-electrode imager uses the radio frequency field gradient within a cylindrical toroid cavity resonator to provide high-resolution nuclear magnetic resonance spectral information on electrolyte materials.

  3. Category:Map Image Files | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand JumpConceptual Model, click here. Category:Conceptual ModelLists for Companies"Image Files Jump

  4. Property:EZFeed/JurisdictionImage | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation,PillarPublicationType JumpDOEInvolveRtoSpp Jump to: navigation,JurisdictionImage

  5. SUPPORTING INFORMATION Comparison of non-precious metal cathode materials for methane

    E-Print Network [OSTI]

    S1 SUPPORTING INFORMATION Comparison of non-precious metal cathode materials for methane production H2SO4, and again in de-ionized water. Butyl rubber stoppers were used to prevent loss of gas from thick and 43 mm diameter) were cut from large butyl rubber sheets (McMaster-Carr, Cleveland, OH, USA

  6. Chemical Emissions of Residential Materials and Products: Review of Available Information

    SciTech Connect (OSTI)

    Willem, Henry; Singer, Brett

    2010-09-15T23:59:59.000Z

    This report is prepared in the context of a larger program whose mission is to advance understanding of ventilation and indoor air quality in U.S. homes. A specific objective of this program is to develop the scientific basis ? through controlled experiments, monitoring and analysis ? for health risk-based ventilation standards. Appropriate and adequate ventilation is a basic element of a healthy home. Ventilation provides outdoor air and in the process removes indoor odors and contaminants including potentially unhealthful chemicals emitted by indoor materials, products and activities. Ventilation traditionally was assured to occur via infiltration of outdoor air through cracks and other leakage pathways in the residential building envelope. As building air tightness is improved for energy efficiency, infiltration can be reduced to inadequate levels. This has lead to the development of standards requiring mechanical ventilation. Though nominally intended to ensure acceptable indoor air quality, the standards are not explicitly tied to health risk or pollutant exposure targets. LBNL is currently designing analyses to assess the impact of varying ventilation standards on pollutant concentrations, health risks and energy use. These analyses require information on sources of chemical pollutant emissions, ideally including emission rates and the impact of ventilation on emissions. Some information can be obtained from recent studies that report measurements of various air contaminants and their concentrations in U.S. residences. Another way to obtain this information is the bottom-up approach of collecting and evaluating emissions data from construction and interior materials and common household products. This review contributes to the latter approach by summarizing available information on chemical emissions from new residential products and materials. We review information from the scientific literature and public sources to identify and discuss the databases that provide information on new or low-emission materials and products. The review focuses on the primary chemical or volatile organic compound (VOC) emissions from interior surface materials, furnishings, and some regularly used household products; all of these emissions are amenable to ventilation. Though it is an important and related topic, this review does not consider secondary pollutants that result from reactions of ozone and unsaturated organics bound to or emitted from material surfaces. Semi-volatile organic compounds (SVOCs) have been largely excluded from this review because ventilation generally is not an effective way to control SVOC exposures. Nevertheless, health concerns about exposures to SVOCs emitted from selected materials warrant some discussion.

  7. Formation Micro-Imager Logs (FMI) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualPropertyd8c-a9ae-f8521cbb8489Information HydroFontana,dataset name below

  8. Image Logs At Coso Geothermal Area (2004) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to: navigation, search OpenEIHesperia,IDGWP Wind FarmInformationIllinois/WindIllustrative

  9. Image Logs At Coso Geothermal Area (2011) | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdfGetecGtel JumpCounty, Texas:ITCSolidIdaho‎Information RiverIllumitex

  10. Small Wind Guidebook/Image Library | Open Energy Information

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are beingZealand Jump to:Ezfeedflag JumpID-f < RAPID‎ |RippeyInformation SlimSlough Heat and Power102WindSmall

  11. Multispectral Imaging At Alum Area (DOE GTP) | Open Energy Information

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal Pwer Plant JumpMarysville,Missoula,MontereyHill,SpurrMulberry, Ohio:GeothermalInformationAlum

  12. Multispectral Imaging At Maui Area (DOE GTP) | Open Energy Information

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov YouKizildere I Geothermal Pwer Plant JumpMarysville,Missoula,MontereyHill,SpurrMulberry,Energy InformationAl., 2001) |

  13. Discover the Benefits of Radar Imaging | Open Energy Information

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualPropertyd8c-a9ae-f8521cbb8489 No revision| Open Energy Information At1986) |Disa Jump to:of

  14. The Development of a Parameterized Scatter Removal Algorithm for Nuclear Materials Identification System Imaging

    SciTech Connect (OSTI)

    Grogan, Brandon R [ORNL

    2010-03-01T23:59:59.000Z

    This dissertation presents a novel method for removing scattering effects from Nuclear Materials Identification System (NMIS) imaging. The NMIS uses fast neutron radiography to generate images of the internal structure of objects non-intrusively. If the correct attenuation through the object is measured, the positions and macroscopic cross-sections of features inside the object can be determined. The cross sections can then be used to identify the materials and a 3D map of the interior of the object can be reconstructed. Unfortunately, the measured attenuation values are always too low because scattered neutrons contribute to the unattenuated neutron signal. Previous efforts to remove the scatter from NMIS imaging have focused on minimizing the fraction of scattered neutrons which are misidentified as directly transmitted by electronically collimating and time tagging the source neutrons. The parameterized scatter removal algorithm (PSRA) approaches the problem from an entirely new direction by using Monte Carlo simulations to estimate the point scatter functions (PScFs) produced by neutrons scattering in the object. PScFs have been used to remove scattering successfully in other applications, but only with simple 2D detector models. This work represents the first time PScFs have ever been applied to an imaging detector geometry as complicated as the NMIS. By fitting the PScFs using a Gaussian function, they can be parameterized and the proper scatter for a given problem can be removed without the need for rerunning the simulations each time. In order to model the PScFs, an entirely new method for simulating NMIS measurements was developed for this work. The development of the new models and the codes required to simulate them are presented in detail. The PSRA was used on several simulated and experimental measurements and chi-squared goodness of fit tests were used to compare the corrected values to the ideal values that would be expected with no scattering. Using the PSRA resulted in an improvement of the chi-squared test by a factor of 60 or more when applied to simple homogeneous objects.

  15. Multiphase imaging of gas flow in a nanoporous material usingremote detection NMR

    SciTech Connect (OSTI)

    Harel, Elad; Granwehr, Josef; Seeley, Juliette A.; Pines, Alex

    2005-10-03T23:59:59.000Z

    Pore structure and connectivity determine how microstructured materials perform in applications such as catalysis, fluid storage and transport, filtering, or as reactors. We report a model study on silica aerogel using a recently introduced time-of-flight (TOF) magnetic resonance imaging technique to characterize the flow field and elucidate the effects of heterogeneities in the pore structure on gas flow and dispersion with Xe-129 as the gas-phase sensor. The observed chemical shift allows the separate visualization of unrestricted xenon and xenon confined in the pores of the aerogel. The asymmetrical nature of the dispersion pattern alludes to the existence of a stationary and a flow regime in the aerogel. An exchange time constant is determined to characterize the gas transfer between them. As a general methodology, this technique provides new insights into the dynamics of flow in porous media where multiple phases or chemical species may be present.

  16. Conceptual design report: Nuclear materials storage facility renovation. Part 3, Supplemental information

    SciTech Connect (OSTI)

    NONE

    1995-07-14T23:59:59.000Z

    The Nuclear Materials Storage Facility (NMSF) at the Los Alamos National Laboratory (LANL) was a Fiscal Year (FY) 1984 line-item project completed in 1987 that has never been operated because of major design and construction deficiencies. This renovation project, which will correct those deficiencies and allow operation of the facility, is proposed as an FY 97 line item. The mission of the project is to provide centralized intermediate and long-term storage of special nuclear materials (SNM) associated with defined LANL programmatic missions and to establish a centralized SNM shipping and receiving location for Technical Area (TA)-55 at LANL. Based on current projections, existing storage space for SNM at other locations at LANL will be loaded to capacity by approximately 2002. This will adversely affect LANUs ability to meet its mission requirements in the future. The affected missions include LANL`s weapons research, development, and testing (WRD&T) program; special materials recovery; stockpile survelliance/evaluation; advanced fuels and heat sources development and production; and safe, secure storage of existing nuclear materials inventories. The problem is further exacerbated by LANL`s inability to ship any materials offsite because of the lack of receiver sites for mate rial and regulatory issues. Correction of the current deficiencies and enhancement of the facility will provide centralized storage close to a nuclear materials processing facility. The project will enable long-term, cost-effective storage in a secure environment with reduced radiation exposure to workers, and eliminate potential exposures to the public. It is organized into seven parts. Part I - Design Concept describes the selected solution. Part III - Supplemental Information contains calculations for the various disciplines as well as other supporting information and analyses.

  17. Thermal diffusivity imaging of continuous fiber ceramic composite materials and components

    SciTech Connect (OSTI)

    Ahuja, S.; Ellingson, W.A. [Argonne National Lab., IL (United States); Steckenrider, J.S. [Northwestern Univ., Evanston, IL (United States); King, S. [Argonne National Lab., IL (United States)

    1995-12-31T23:59:59.000Z

    Continuous-fiber ceramic matrix composites (CFCCs) are currently being developed for various high-temperature applications, including use in advanced turbine engines. In such composites, the condition of the interfaces between the fibers and matrix or between laminae in a two-dimensional weave lay-up are critical to the mechanical and thermal behavior of the component. A nondestructive evaluation method that could be used to assess the interface condition and/or detect other `defects` has been developed at Argonne National Laboratory (ANL) and uses infrared thermal imaging to provide `single-shot` full- field quantitative measurement of the distribution of thermal diffusivity in large components. By applying digital filtering, interpolation, and least-squares-estimation techniques for noise reduction, shorter acquisition and analysis times have been achieved with submillimeter spatial resolution for materials with a wide range of `thermal thicknesses`. The system at ANL has been used to examine the effects of thermal shock, oxidation treatment, density variations, and variations in fiber coating in a full array of test specimens. In addition, actual subscale CFCC components of nonplanar geometries have been inspected for manufacturing-induced variations in thermal properties.

  18. Accident Generated Particulate Materials and Their Characteristics -- A Review of Background Information

    SciTech Connect (OSTI)

    Sutter, S. L.

    1982-05-01T23:59:59.000Z

    Safety assessments and environmental impact statements for nuclear fuel cycle facilities require an estimate of the amount of radioactive particulate material initially airborne (source term) during accidents. Pacific Northwest Laboratory (PNL) has surveyed the literature, gathering information on the amount and size of these particles that has been developed from limited experimental work, measurements made from operational accidents, and known aerosol behavior. Information useful for calculating both liquid and powder source terms is compiled in this report. Potential aerosol generating events discussed are spills, resuspension, aerodynamic entrainment, explosions and pressurized releases, comminution, and airborne chemical reactions. A discussion of liquid behavior in sprays, sparging, evaporation, and condensation as applied to accident situations is also included.

  19. Video Toroid Cavity Imager

    DOE Patents [OSTI]

    Gerald, Rex E. II; Sanchez, Jairo; Rathke, Jerome W.

    2004-08-10T23:59:59.000Z

    A video toroid cavity imager for in situ measurement of electrochemical properties of an electrolytic material sample includes a cylindrical toroid cavity resonator containing the sample and employs NMR and video imaging for providing high-resolution spectral and visual information of molecular characteristics of the sample on a real-time basis. A large magnetic field is applied to the sample under controlled temperature and pressure conditions to simultaneously provide NMR spectroscopy and video imaging capabilities for investigating electrochemical transformations of materials or the evolution of long-range molecular aggregation during cooling of hydrocarbon melts. The video toroid cavity imager includes a miniature commercial video camera with an adjustable lens, a modified compression coin cell imager with a fiat circular principal detector element, and a sample mounted on a transparent circular glass disk, and provides NMR information as well as a video image of a sample, such as a polymer film, with micrometer resolution.

  20. Material Property Estimation for Direct Detection of DNAPL using Integrated Ground-Penetrating Radar Velocity, Imaging and Attribute Analysis

    SciTech Connect (OSTI)

    John H. Bradford; Stephen Holbrook; Scott B. Smithson

    2004-12-09T23:59:59.000Z

    The focus of this project is direct detection of DNAPL's specifically chlorinated solvents, via material property estimation from multi-fold surface ground-penetrating radar (GPR) data. We combine state-of-the-art GPR processing methodology with quantitative attribute analysis and material property estimation to determine the location and extent of residual and/or pooled DNAPL in both the vadose and saturated zones. An important byproduct of our research is state-of-the-art imaging which allows us to pinpoint attribute anomalies, characterize stratigraphy, identify fracture zones, and locate buried objects.

  1. Imaging of self-assembly and self-assembled materials P. V. Braun

    E-Print Network [OSTI]

    Braun, Paul

    of the microscope. Thus real time imaging is possible. The multiphoton imaging is performed using a mode locked Ti, is used to control the growth of a hard, inorganic phase, in this case CdS. The growth process in some. Space between spheres filled with 1x10-4 molar Rhodamine-6G in DMF, Excitation wavelength: 488nm

  2. Using indium tin oxide material to implement the imaging of microwave plasma ignition process

    SciTech Connect (OSTI)

    Wang, Qiang; Hou, Lingyun; Zhang, Guixin, E-mail: guixin@mail.tsinghua.edu.cn; Zhang, Boya; Liu, Cheng [Department of Electrical Engineering, Tsinghua University, Beijing 100084 (China); Wang, Zhi; Huang, Jian [State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084 (China)

    2014-02-17T23:59:59.000Z

    In this paper, a method is introduced to get global observation of microwave plasma ignition process at high pressure. A microwave resonator was designed with an indium tin oxide coated glass at bottom. Microwave plasma ignition was implemented in methane and air mixture at 10 bars by a 2?ms-3?kW-2.45?GHz microwave pulse, and the high speed images of the ignition process were obtained. The images visually proved that microwave plasma ignition could lead to a multi-point ignition. The system may also be applied to obtain Schlieren images, which is commonly used to observe the development of flame kernel in an ignition process.

  3. Materials

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of Science (SC)Integrated Codes |IsLove Your Home andDisposition | NationalMaterials

  4. Image-Based Multiscale Modeling of Poroelastic Biological Materials with Application to Bones

    E-Print Network [OSTI]

    Yang, Judy

    2012-01-01T23:59:59.000Z

    two-scale problems in geomechanics, Int J Numer Anal Methodsbeen initiated in geomechanics. Due to different subjects ofhas been extended from geomechanics to material science and

  5. Image Analysis

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Recognition Image Analysis and Recognition Snapshot1498121slicesqResedison Fibers permeating imaged material (Courtesy: Bale, Loring, Perciano and Ushizima) Imagery coming from...

  6. Investigation of the secondary electron emission characteristics of alternative dynode materials for imaging photomultipliers

    E-Print Network [OSTI]

    Bristol, University of

    electron emission (SEE) coefficients for polycrystalline CVD diamond of 84 have been reported for Hydrogen]. Polycrystalline diamond is a widely available, relatively inexpensive material that can be de- posited over large by in- creasing the gain of each dynode stage by utilizing materials such as CVD diamond. Secondary

  7. Chemical Emissions of Residential Materials and Products: Review of Available Information

    E-Print Network [OSTI]

    Willem, Henry

    2010-01-01T23:59:59.000Z

    Emission of phthalates from PVC and other materials. Indoorof esters contained in PVC flooring and adhesive. Buildingemissions from individual PVC materials, adhesives and from

  8. Algorithms for biomagnetic source imaging with prior anatomical and physiological information

    SciTech Connect (OSTI)

    Hughett, P W [Univ. of California, Berkeley, CA (United States). Dept. of Electrical Engineering and Computer Sciences

    1995-12-01T23:59:59.000Z

    This dissertation derives a new method for estimating current source amplitudes in the brain and heart from external magnetic field measurements and prior knowledge about the probable source positions and amplitudes. The minimum mean square error estimator for the linear inverse problem with statistical prior information was derived and is called the optimal constrained linear inverse method (OCLIM). OCLIM includes as special cases the Shim-Cho weighted pseudoinverse and Wiener estimators but allows more general priors and thus reduces the reconstruction error. Efficient algorithms were developed to compute the OCLIM estimate for instantaneous or time series data. The method was tested in a simulated neuromagnetic imaging problem with five simultaneously active sources on a grid of 387 possible source locations; all five sources were resolved, even though the true sources were not exactly at the modeled source positions and the true source statistics differed from the assumed statistics.

  9. Chemical Emissions of Residential Materials and Products: Review of Available Information Preliminary Analysis of U.S. Residential

    E-Print Network [OSTI]

    Chemical Emissions of Residential Materials and Products: Review of Available Information Preliminary Analysis of U.S. Residential Air Leakage Database v.2011 thereof or the Regents of the University of California. #12;PRELIMINARY ANALYSIS OF U.S. RESIDENTIAL AIR

  10. Ultra-low field nuclear magnetic resonance and magnetic resonance imaging to discriminate and identify materials

    SciTech Connect (OSTI)

    Matlashov, Andrei Nikolaevich; Urbaitis, Algis V.; Savukov, Igor Mykhaylovich; Espy, Michelle A.; Volegov, Petr Lvovich; Kraus, Jr., Robert Henry

    2013-03-05T23:59:59.000Z

    Method comprising obtaining an NMR measurement from a sample wherein an ultra-low field NMR system probes the sample and produces the NMR measurement and wherein a sampling temperature, prepolarizing field, and measurement field are known; detecting the NMR measurement by means of inductive coils; analyzing the NMR measurement to obtain at least one measurement feature wherein the measurement feature comprises T1, T2, T1.rho., or the frequency dependence thereof; and, searching for the at least one measurement feature within a database comprising NMR reference data for at least one material to determine if the sample comprises a material of interest.

  11. Thermal imaging measurement of lateral diffusivity and non-invasive material defect detection

    DOE Patents [OSTI]

    Sun, Jiangang (Westmont, IL); Deemer, Chris (Downers Grove, IL)

    2003-01-01T23:59:59.000Z

    A system and method for determining lateral thermal diffusivity of a material sample using a heat pulse; a sample oriented within an orthogonal coordinate system; an infrared camera; and a computer that has a digital frame grabber, and data acquisition and processing software. The mathematical model used within the data processing software is capable of determining the lateral thermal diffusivity of a sample of finite boundaries. The system and method may also be used as a nondestructive method for detecting and locating cracks within the material sample.

  12. Fluorescence Imaging for Nuclear Arms Control Verification

    E-Print Network [OSTI]

    Feener, Jessica S

    2014-08-14T23:59:59.000Z

    treaties. Specifically, this technique uses fluorescence imaging to determine fissile material attributes in verifying, during the dismantlement process, an uncanned nuclear warhead or warhead component without revealing sensitive information. This could...

  13. Fast Image Retrieval by the Tree of Contours Content* Institute of Information Technologies, 1113 Sofia

    E-Print Network [OSTI]

    Mustakerov, Ivan

    for graphic images, as well as on the one-dimensional complex Fourier transform of the contours. In this way), CORE, Photobook (1994), Image Engine (Virage), Image Retrieval Ware (Excalibur), ARTISAN (1999 automated) retrieval of image and/or video objects by features as color, texture, shape, movement, etc. [6

  14. Using Macromedia Flash to create online information skills materials at Edinburgh University Library

    E-Print Network [OSTI]

    Jones, Richard D; Bains, Simon

    Growth in electronic information services, pressure on staff resources and developments in the area of electronic learning have resulted in a need for online information skills delivery. Edinburgh University Library has developed some simple...

  15. First Ever STEREO Images of the Entire Sun These presentations give additional information on how STEREO's first ever

    E-Print Network [OSTI]

    Christian, Eric

    STEREO 360 1 First Ever STEREO Images of the Entire Sun These presentations give additional information on how STEREO's first ever views of the entire sun will advance the study of solar and space years the STEREO spacecrafts and SDO will be able to observe the entire 360 degrees of the Sun. Credit

  16. Influence of image charge effect on exciton fine structure in an organic-inorganic quantum well material

    SciTech Connect (OSTI)

    Takagi, Hidetsugu; Kunugita, Hideyuki; Ema, Kazuhiro [Department of Physics, Sophia University, 7-1 Kioi-cho, Chiyoda-ku, Tokyo 102-8554 (Japan); Sato, Mikio; Takeoka, Yuko [Department of Materials and Life Sciences, Sophia University, 7-1 Kioi-cho, Chiyoda-ku, Tokyo 102-8554 (Japan)

    2013-12-04T23:59:59.000Z

    We have investigated experimentally excitonic properties in organic-inorganic hybrid multi quantum well crystals, (C{sub 4}H{sub 9}NH{sub 3}){sub 2}PbBr{sub 4} and (C{sub 6}H{sub 5}?C{sub 2}H{sub 4}NH{sub 3}){sub 2}PbBr{sub 4}, by measuring photoluminescence, reflectance, photoluminescence excitation spectra. In these materials, the excitonic binding energies are enhanced not only by quantum confinement effect (QCE) but also by image charge effect (ICE), since the dielectric constant of the barrier layers is much smaller than that of the well layers. By comparing the 1s-exciton and 2s-exciton energies, we have investigated the influence of ICE with regard to the difference of the Bohr radius.

  17. HIGH SPATIAL-RESOLUTION IMAGING OF TE INCLUSIONS IN CZT MATERIAL.

    SciTech Connect (OSTI)

    CAMARDA, G.S.; BOLOTNIKOV, A.E.; CARINI, G.A.; CUI, Y.; KOHMAN, K.T.; LI, L.; JAMES, R.B.

    2006-08-13T23:59:59.000Z

    We present new results from our studies of defects in current single-crystal CdZnTe material. Our previous measurements, carried out on thin ({approx}1 mm) and long (>12 mm) CZT detectors, indicated that small (1-20 {micro}m) Te inclusions can significantly degrade the device's energy resolution and detection efficiency. We are conducting detailed studies of the effects of Te inclusions by employing different characterization techniques with better spatial resolution, such as quantitative fluorescence mapping, X-ray micro-diffraction, and TEM. Also, IR microscopy and gamma-mapping with pulse-shape analysis with higher spatial resolution generated more accurate results in the areas surrounding the micro-defects (Te inclusions). Our results reveal how the performance of CdZnTe detectors is influenced by Te inclusions, such as their spatial distribution, concentration, and size. We also discuss a model of charge transport through areas populated with Te inclusions.

  18. A Methodology Supporting the Risk-Informed Management of Materials Degradation

    SciTech Connect (OSTI)

    Unwin, Stephen D.; Lowry, Peter P.; Toyooka, Michael Y.

    2011-07-28T23:59:59.000Z

    This paper describes a methodology for the synthesis of materials degradation data with nuclear power plant service data to estimate the long-term failure rates of passive components.

  19. Billions of 'nanoreactors' inform materials design > EMC2 News > The Energy

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadapInactiveVisiting the TWP TWPAlumniComplexMaterial Science |Materials Center at Cornell

  20. A fuzzy analytic hierarchy process for group decision making: application for embedding information on communicating materials

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

    to use intelligent products for ensuring an information continuum all along the product lifecycle, group decision making, Intelligent product; Product Lifecycle Management; Data Dissemination I lifecycle (PLC). However, most of the time, products only provide a network pointer to a linked database (e

  1. Polyaniline as a reversibly switchable electrochromic material. (Reannouncement with new availability information). Technical report

    SciTech Connect (OSTI)

    Shieh, W.R.; Yang, S.C.; Marzzacco, C.; Hwang, J.H.

    1990-12-31T23:59:59.000Z

    Polyaniline is an interesting electrochromic material because its color can be changed from clear to green, to blue, and to purple by electrochemical oxidation. The structural transformations associated with these color changes are shown. One of the possible applications for polyaniline is to use it as the active material in electrochromic windows. An electrochromic window is a multi-layered device with the structure of a transparent rechargeable battery. A practical electrochromic window needs to have long color-cycling lifetime and good durability under solar radiation. This is a severe requirement because all layers of materials and interfaces between layers have to be durable under such electrochemical and photochemical stress. In this communication the authors report an initial study towards the construction of a polyaniline-based electrochromic window. They concerned themselves with only polyaniline coated on tin oxide glass. They tested such a half-cell in an aqueous electrolyte to see if this part of the electrochromic device can be made durable enough for electrochromic applications and to find useful designing principles for constructing good devices.

  2. Imaging

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742EnergyOnItem NotEnergy,ARMFormsGasReleaseSpeechesHallNotSeventyTechnologiesfacilityImaging

  3. Imaging

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of Science (SC) EnvironmentalGyroSolé(tm)Hydrogen StorageITERITERBuilding EnergyImaging Print The

  4. Imaging

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of Science (SC) EnvironmentalGyroSolé(tm)Hydrogen StorageITERITERBuilding EnergyImaging Print

  5. Waste management facilities cost information for transportation of radioactive and hazardous materials

    SciTech Connect (OSTI)

    Feizollahi, F.; Shropshire, D.; Burton, D.

    1995-06-01T23:59:59.000Z

    This report contains cost information on the U.S. Department of Energy (DOE) Complex waste streams that will be addressed by DOE in the programmatic environmental impact statement (PEIS) project. It describes the results of the task commissioned by DOE to develop cost information for transportation of radioactive and hazardous waste. It contains transportation costs for most types of DOE waste streams: low-level waste (LLW), mixed low-level waste (MLLW), alpha LLW and alpha MLLW, Greater-Than-Class C (GTCC) LLW and DOE equivalent waste, transuranic (TRU) waste, spent nuclear fuel (SNF), and hazardous waste. Unit rates for transportation of contact-handled (<200 mrem/hr contact dose) and remote-handled (>200 mrem/hr contact dose) radioactive waste are estimated. Land transportation of radioactive and hazardous waste is subject to regulations promulgated by DOE, the U.S. Department of Transportation (DOT), the U.S. Nuclear Regulatory Commission (NRC), and state and local agencies. The cost estimates in this report assume compliance with applicable regulations.

  6. Image

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdfGetecGtel JumpCounty, Texas:ITCSolidIdaho‎Information RiverIllumitex Jump

  7. Workshop on Satellites for Solar Energy Resource Information -Washington, DC, April 10-11, 1996 POTENTIALS OF IMAGES FROM GEOSTATIONARY SATELLITE DATA FOR THE

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

    Workshop on Satellites for Solar Energy Resource Information - Washington, DC, April 10-11, 1996 POTENTIALS OF IMAGES FROM GEOSTATIONARY SATELLITE DATA FOR THE ASSESSMENT OF SOLAR ENERGY PARAMETERS Lucien Author manuscript, published in "Workshop `satellites for solar energy resource information', Washington

  8. Chemical Bonding and Structural Information of Black CarbonReference Materials and Individual Carbonaceous AtmosphericAerosols

    SciTech Connect (OSTI)

    Hopkins, Rebecca J.; Tivanski, Alexei V.; Marten, Bryan D.; Gilles, Mary K.

    2007-04-25T23:59:59.000Z

    The carbon-to-oxygen ratios and graphitic nature of a rangeof black carbon standard reference materials (BC SRMs), high molecularmass humic-like substances (HULIS) and atmospheric particles are examinedusing scanning transmission X-ray microscopy (STXM) coupled with nearedge X-ray absorption fine structure (NEXAFS) spectroscopy. UsingSTXM/NEXAFS, individual particles with diameter>100 nm are studied,thus the diversity of atmospheric particles collected during a variety offield missions is assessed. Applying a semi-quantitative peak fittingmethod to the NEXAFS spectra enables a comparison of BC SRMs and HULIS toparticles originating from anthropogenic combustion and biomass burns,thus allowing determination of the suitability of these materials forrepresenting atmospheric particles. Anthropogenic combustion and biomassburn particles can be distinguished from one another using both chemicalbonding and structural ordering information. While anthropogeniccombustion particles are characterized by a high proportion ofaromatic-C, the presence of benzoquinone and are highly structurallyordered, biomass burn particles exhibit lower structural ordering, asmaller proportion of aromatic-C and contain a much higher proportion ofoxygenated functional groups.

  9. Materials for Information Technology

    E-Print Network [OSTI]

    Tang, Ben Zhong

    and Thermoplastic Nanocomposites Based upon Expanded Graphite Oxide R. Mulhaupt et al. Macromol. Rapid Commun. 2009 Hybrid Multicomponent Hydrogels for Tissue Engineering X. Q. Jia et al. Macromol. Biosci. 2009, 9, 140

  10. ARM - Public Information Materials

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742EnergyOnItem NotEnergy,ARMForms About Become a User RecoveryARMParticipantsgovCampaignsPropose

  11. Combinational pixel-by-pixel and object-level classifying, segmenting, and agglomerating in performing quantitative image analysis that distinguishes between healthy non-cancerous and cancerous cell nuclei and delineates nuclear, cytoplasm, and stromal material objects from stained biological tissue materials

    DOE Patents [OSTI]

    Boucheron, Laura E

    2013-07-16T23:59:59.000Z

    Quantitative object and spatial arrangement-level analysis of tissue are detailed using expert (pathologist) input to guide the classification process. A two-step method is disclosed for imaging tissue, by classifying one or more biological materials, e.g. nuclei, cytoplasm, and stroma, in the tissue into one or more identified classes on a pixel-by-pixel basis, and segmenting the identified classes to agglomerate one or more sets of identified pixels into segmented regions. Typically, the one or more biological materials comprises nuclear material, cytoplasm material, and stromal material. The method further allows a user to markup the image subsequent to the classification to re-classify said materials. The markup is performed via a graphic user interface to edit designated regions in the image.

  12. Quantitative electron density characterization of soft tissue substitute plastic materials using grating-based x-ray phase-contrast imaging

    SciTech Connect (OSTI)

    Sarapata, A.; Chabior, M.; Zanette, I.; Pfeiffer, F. [Lehrstuhl fr Biomedizinische Physik, Physik-Department and Institut fr Medizintechnik, Technische Universitt Mnchen, 85748 Garching (Germany); Cozzini, C.; Sperl, J. I.; Bequ, D. [GE Global Research, 85748 Garching (Germany); Langner, O.; Coman, J. [QRM GmbH, Mhrendorf (Germany); Ruiz-Yaniz, M. [Lehrstuhl fr Biomedizinische Physik, Physik-Department and Institut fr Medizintechnik, Technische Universitt Mnchen, 85748 Garching (Germany); European Synchrotron Radiation Facility, Grenoble (France)

    2014-10-15T23:59:59.000Z

    Many scientific research areas rely on accurate electron density characterization of various materials. For instance in X-ray optics and radiation therapy, there is a need for a fast and reliable technique to quantitatively characterize samples for electron density. We present how a precise measurement of electron density can be performed using an X-ray phase-contrast grating interferometer in a radiographic mode of a homogenous sample in a controlled geometry. A batch of various plastic materials was characterized quantitatively and compared with calculated results. We found that the measured electron densities closely match theoretical values. The technique yields comparable results between a monochromatic and a polychromatic X-ray source. Measured electron densities can be further used to design dedicated X-ray phase contrast phantoms and the additional information on small angle scattering should be taken into account in order to exclude unsuitable materials.

  13. Image-based stochastic modeling of the 3D morphology of energy materials on various length scales

    E-Print Network [OSTI]

    Schmidt, Volker

    , to appear 3D image of uncompressed graphite electrode used in Li-ion batteries tomography: Helmholtz Center, 2013 | Volker Schmidt Contents Introduction 3D microstructure of uncompressed graphite electrodes 3D microstructure of compressed graphite electrodes 3D morphology of hybrid organic solar cells Charge transport

  14. Monolithically integrated near-infrared and mid-infrared detector array for spectral imaging

    E-Print Network [OSTI]

    Perera, A. G. Unil

    detector test results ensure the high quality of material suitable for near-infrared/QWIP dual-band focal. A CTIS records spatial and spectral information by imaging a scene through an optical relay system

  15. Image alignment

    DOE Patents [OSTI]

    Dowell, Larry Jonathan

    2014-04-22T23:59:59.000Z

    Disclosed is a method and device for aligning at least two digital images. An embodiment may use frequency-domain transforms of small tiles created from each image to identify substantially similar, "distinguishing" features within each of the images, and then align the images together based on the location of the distinguishing features. To accomplish this, an embodiment may create equal sized tile sub-images for each image. A "key" for each tile may be created by performing a frequency-domain transform calculation on each tile. A information-distance difference between each possible pair of tiles on each image may be calculated to identify distinguishing features. From analysis of the information-distance differences of the pairs of tiles, a subset of tiles with high discrimination metrics in relation to other tiles may be located for each image. The subset of distinguishing tiles for each image may then be compared to locate tiles with substantially similar keys and/or information-distance metrics to other tiles of other images. Once similar tiles are located for each image, the images may be aligned in relation to the identified similar tiles.

  16. Key experimental information on intermediate-range atomic structures in amorphous Ge2Sb2Te5 phase change material

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

    .1063/1.3657139 Nature of phase transitions in crystalline and amorphous GeTe-Sb2Te3 phase change materials J. Chem. Phys on intermediate-range atomic structures in amorphous Ge2Sb2Te5 phase change material Shinya Hosokawa,1,2,a) Wolf change material Shinya Hosokawa, Wolf-Christian Pilgrim, Astrid Hhle, Daniel Szubrin, Nathalie Boudet

  17. Development of Research Infrastructure in Nevada for the Exploitation of Hyperspectral Image Data to Address Proliferation and Detection of Chemical and Biological Materials.

    SciTech Connect (OSTI)

    James V. Taranik

    2007-12-31T23:59:59.000Z

    This research was to exploit hyperspectral reflectance imaging technology for the detection and mapping variability (clutter) of the natural background against which gases in the atmosphere are imaged. The natural background consists of landscape surface cover composed of consolidated rocks, unconsolidated rock weathering products, soils, coatings on rock materials, vegetation, water, materials constructed by humans, and mixtures of the above. Human made gases in the atmosphere may indicate industrial processes important to detecting non-nuclear chemical and biological proliferation. Our research was to exploit the Visible and Near-Infrared (NIR) and the Short-wave Infrared (SWIR) portions of the electromagnetic spectrum to determine the properties of solid materials on the earths surface that could influence the detection of gases in the Long-Wave Infrared (LWIR). We used some new experimental hyperspectral imaging technologies to collect data over the Non-Proliferation Test and Evaluation Center (NPTEC) located on the Nevada Test Site (NTS). The SpecTIR HyperSpecTIR (HST) and Specim Dual hyperspectral sensors were used to understand the variability in the imaged background (clutter), that detected, measured, identified and mapped with operational commercial hyperspectral techniques. The HST sensors were determined to be more experimental than operational because of problems with radiometric and atmospheric data correction. However the SpecTIR Dual system, developed by Specim in Finland, eventually was found to provide cost-effective hyperspectral image data collection and it was possible to correct the Dual systems data for specific areas. Batch processing of long flightlines was still complex, and if comparison to laboratory spectra was desired, the Dual system data still had to be processed using the empirical line method. This research determined that 5-meter spatial resolution was adequate for mapping natural background variations. Furthermore, this research determined that spectral resolution of 10um was adequate, but a signal to noise above 300:1 was desirable for hyperspectral sensors with this spectral resolution. Finally, we acquired a hyperspectral thermal dataset (SEBASS) at 3m spatial resolution over our study area in Beatty, Nevada that can be co-registered with the hyperspectral reflectance, LIDAR and digital Orthophoto data sets. This data set will enable us to quantify how measurements in the reflected infrared can be used to make inferences about the response of materials in the thermal infrared, the topic of our follow-on NA-22 investigation ending in 2008. These data provide the basis for our investigations proposed for the NA-22 2008 Broad Area Announcement. Beginning in June 2008, SpecTIR Corporation and Aerospace Corporation plan to fly the SpecTIR Dual and SEBASS in a stabilized mount in a twin Otter aircraft. This research provides the foundation for using reflected and emitted hyperspectral measurements together for mapping geologic and soil materials in arid to semi-arid regions.

  18. A DCT-BASED DATA-HIDING METHOD TO EMBED THE COLOR INFORMATION IN A JPEG GREY LEVEL IMAGE

    E-Print Network [OSTI]

    Paris-Sud XI, Universit de

    allows to com- press images with a Word Wide Web standard format and in the same time proposes a data allows to compress images with a Word Wide Web standard format and in the same time proposes a data

  19. Federal Emergency Management Information System (FEMIS) Bill of Materials (BOM) for FEMIS Version 1.4.7

    SciTech Connect (OSTI)

    Arp, Jonathan A. (BATTELLE (PACIFIC NW LAB)); Downing, Timothy R. (BATTELLE (PACIFIC NW LAB)); Gackle, Philip P. (BATTELLE (PACIFIC NW LAB)); Homer, Brian J. (BATTELLE (PACIFIC NW LAB)); Johnson, Daniel M. (BATTELLE (PACIFIC NW LAB)); Johnson, Ranata L. (BATTELLE (PACIFIC NW LAB)); Johnson, Sharon M. (BATTELLE (PACIFIC NW LAB)); Loveall, Robert M. (BATTELLE (PACIFIC NW LAB)); Millard, W David (BATTELLE (PACIFIC NW LAB)); Stoops, Lamar R. (BATTELLE (PACIFIC NW LAB)); Tzemos, Spyridon (BATTELLE (PACIFIC NW LAB)); Wood, Blanche M. (BATTELLE (PACIFIC NW LAB))

    1999-12-01T23:59:59.000Z

    This document describes the hardware and software required for the Federal Emergency Management Information System version 1.4.7 (FEMIS v1.4.7) released by Pacific Northwest National Laboratory (PNNL). Information included in this document about hardware and software requirements is subject to change.

  20. Data Driven Discovery in Imaging and Spectroscopy

    SciTech Connect (OSTI)

    Rajan, Krishna [Iowa State University

    2006-06-07T23:59:59.000Z

    This talk will outline a process called "materials informatics," which permits one to survey complex, multiscale information in a high-throughput, statistically robust, and yet physically meaningful manner. Examples will be provided in a number of different applications of imaging and spectroscopy including neutron diffraction, EELS images, and other microanalysis techniques. The integrations of informatics with spectroscopy data in combinatorial experiments will be shown as a means of signficantly accelerating the interpretation of complex data from high-throughput experiments. The talk will conclude with a discussion on the value of including a materials informatics component for addressing the challenges of data deluge from large-scale scattering sources.

  1. Fluid Imaging of Enhanced Geothermal Systems

    Broader source: Energy.gov (indexed) [DOE]

    for Fluids & Fractures - time lapse MTCSEM for fluid imaging - joint CSEM-MTseismic imaging ??? - use MEQ focal information with EM Imaging ScientificTechnical Approach...

  2. INFORMATION: Inspection Report on "Removal of Categories I and II Special Nuclear Material from Sandia National Laboratories-New Mexico"

    SciTech Connect (OSTI)

    None

    2010-01-01T23:59:59.000Z

    The Department of Energy's (DOE's) Sandia National Laboratories-New Mexico (Sandia) develops science-based technologies in support of national security in areas such as nuclear weapons, nonproliferation, military technologies, and homeland security. Sandia's primary mission is ensuring that the U.S. nuclear arsenal is safe, secure, and reliable and can fully support the Nation's deterrence policy. Part of this mission includes systems engineering of nuclear weapons; research, design, and development of non-nuclear components; manufacturing of non-nuclear weapons components; the provision of safety, security, and reliability assessments of stockpile weapons; and the conduct of high-explosives research and development and environmental testing. Sandia Corporation, a subsidiary of Lockheed Martin Corporation, operates Sandia for the National Nuclear Security Administration (NNSA). On May 7, 2004, the Secretary announced that the Department would evaluate missions at DOE sites to consolidate Special Nuclear Material (SNM) in the most secure environments possible. The Administrator of the NNSA said that this effort was a key part of an overall plan to transform the nuclear weapons complex into a smaller, safer, more secure, and more efficient national security enterprise. In February 2008, Sandia was the first site to report it had reduced its on-site inventory of nuclear material below 'Categories I and II' levels, which require the highest level of security to protect material such as plutonium and highly enriched uranium. The Office of Inspector General initiated an inspection to determine if Sandia made appropriate adjustments to its security posture in response to the removal of the Categories I and II SNM. We found that Sandia adjusted its security posture in response to the removal of Categories I and II SNM. For example, security posts were closed; unneeded protective force weapons and equipment were excessed from the site; and, Sandia's Site Safeguards and Security Plan was modified. We also found that some highly enriched uranium in a complex material configuration was not removed from Sandia. This material was designated as Category III material using a methodology for assessing the attractiveness of complex materials that was not specifically addressed in any current DOE directive. Although DOE and NNSA officials believed that this designation was appropriate, the methodology used to support this designation had not, as of the time of our review, been incorporated into the DOE directives system. Historically, the Department has considered the categorization of SNM to be an important national security and public policy issue. Consequently, we believe that expedited action should be taken to formalize this methodology in the DOE directives system and that it be disseminated throughout the Department of Energy complex.

  3. Image Hashes as Templates for Verification

    SciTech Connect (OSTI)

    Janik, Tadeusz; Jarman, Kenneth D.; Robinson, Sean M.; Seifert, Allen; McDonald, Benjamin S.; White, Timothy A.

    2012-07-17T23:59:59.000Z

    Imaging systems can provide measurements that confidently assess characteristics of nuclear weapons and dismantled weapon components, and such assessment will be needed in future verification for arms control. Yet imaging is often viewed as too intrusive, raising concern about the ability to protect sensitive information. In particular, the prospect of using image-based templates for verifying the presence or absence of a warhead, or of the declared configuration of fissile material in storage, may be rejected out-of-hand as being too vulnerable to violation of information barrier (IB) principles. Development of a rigorous approach for generating and comparing reduced-information templates from images, and assessing the security, sensitivity, and robustness of verification using such templates, are needed to address these concerns. We discuss our efforts to develop such a rigorous approach based on a combination of image-feature extraction and encryption-utilizing hash functions to confirm proffered declarations, providing strong classified data security while maintaining high confidence for verification. The proposed work is focused on developing secure, robust, tamper-sensitive and automatic techniques that may enable the comparison of non-sensitive hashed image data outside an IB. It is rooted in research on so-called perceptual hash functions for image comparison, at the interface of signal/image processing, pattern recognition, cryptography, and information theory. Such perceptual or robust image hashingwhich, strictly speaking, is not truly cryptographic hashinghas extensive application in content authentication and information retrieval, database search, and security assurance. Applying and extending the principles of perceptual hashing to imaging for arms control, we propose techniques that are sensitive to altering, forging and tampering of the imaged object yet robust and tolerant to content-preserving image distortions and noise. Ensuring that the information contained in the hashed image data (available out-of-IB) cannot be used to extract sensitive information about the imaged object is of primary concern. Thus the techniques are characterized by high unpredictability to guarantee security. We will present an assessment of the performance of our techniques with respect to security, sensitivity and robustness on the basis of a methodical and mathematically precise framework.

  4. Application of Two Phase (Liquid/Gas) Xenon Gamma-Camera for the Detection of Special Nuclear Material and PET Medical Imaging

    SciTech Connect (OSTI)

    McKinsey, Daniel Nicholas [Yale University] [Yale University

    2013-08-27T23:59:59.000Z

    The McKinsey group at Yale has been awarded a grant from DTRA for the building of a Liquid Xenon Gamma Ray Color Camera (LXe-GRCC), which combines state-of-the-art detection of LXe scintillation light and time projection chamber (TPC) charge readout. The DTRA application requires a movable detector and hence only a single phase (liquid) xenon detector can be considered in this case. We propose to extend the DTRA project to applications that allow a two phase (liquid/gas) xenon TPC. This entails additional (yet minimal) hardware and extension of the research effort funded by DTRA. The two phase detector will have better energy and angular resolution. Such detectors will be useful for PET medical imaging and detection of special nuclear material in stationary applications (e.g. port of entry). The expertise of the UConn group in gas phase TPCs will enhance the capabilities of the Yale group and the synergy between the two groups will be very beneficial for this research project as well as the education and research projects of the two universities. The LXe technology to be used in this project has matured rapidly over the past few years, developed for use in detectors for nuclear physics and astrophysics. This technology may now be applied in a straightforward way to the imaging of gamma rays. According to detailed Monte Carlo simulations recently performed at Yale University, energy resolution of 1% and angular resolution of 3 degrees may be obtained for 1.0 MeV gamma rays, using existing technology. With further research and development, energy resolution of 0.5% and angular resolution of 1.3 degrees will be possible at 1.0 MeV. Because liquid xenon is a high density, high Z material, it is highly efficient for scattering and capturing gamma rays. In addition, this technology scales elegantly to large detector areas, with several square meter apertures possible. The Yale research group is highly experienced in the development and use of noble liquid detectors for astrophysics, most recently in the XENON10 experiment. The existing facilities at Yale are fully adequate for the completion of this project. The facilities of the UConn group at the LNS at Avery Point include a (clean) lab for detector development and this group recently delivered an Optical Readout TPC (O-TPC) for research in Nuclear Astrophysics at the TUNL in Duke University. The machine shop at UConn will be used (free of charge) for producing the extra hardware needed for this project including grids and frames.

  5. Materials Reliability Program: Risk-Informed Revision of ASME Section XI Appendix G - Proof of Concept (MRP-143)

    SciTech Connect (OSTI)

    B. Bishop; et al

    2005-03-30T23:59:59.000Z

    This study indicates that risk-informed methods can be used to significantly relax the current ASME and NRC Appendix G requirements while still maintaining satisfactory levels of reactor vessel structural integrity. This relaxation in Appendix G requirements directly translates into significant improvements in operational flexibility.

  6. J.C. Rajapakse, L. Wong, and R. Acharya (Eds): PRIB 2006, LNBI 4146, pp. 163-173, Springer, Berlin/Heidelberg Image and fractal information processing for large-scale

    E-Print Network [OSTI]

    Benuskova, Luba

    discovery and large-scale genome comparisons, as powerful modern image processing methods can be applied/Heidelberg 1 Image and fractal information processing for large-scale chemoinformatics, genomics analyses Zealand Contact: ilkka.havukkala@aut.ac.nz Abstract Two promising approaches for handling large

  7. Materials Project: A Materials Genome Approach

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Ceder, Gerbrand (MIT); Persson, Kristin (LBNL)

    Technological innovation - faster computers, more efficient solar cells, more compact energy storage - is often enabled by materials advances. Yet, it takes an average of 18 years to move new materials discoveries from lab to market. This is largely because materials designers operate with very little information and must painstakingly tweak new materials in the lab. Computational materials science is now powerful enough that it can predict many properties of materials before those materials are ever synthesized in the lab. By scaling materials computations over supercomputing clusters, this project has computed some properties of over 80,000 materials and screened 25,000 of these for Li-ion batteries. The computations predicted several new battery materials which were made and tested in the lab and are now being patented. By computing properties of all known materials, the Materials Project aims to remove guesswork from materials design in a variety of applications. Experimental research can be targeted to the most promising compounds from computational data sets. Researchers will be able to data-mine scientific trends in materials properties. By providing materials researchers with the information they need to design better, the Materials Project aims to accelerate innovation in materials research.[copied from http://materialsproject.org/about] You will be asked to register to be granted free, full access.

  8. Associated particle imaging (API)

    SciTech Connect (OSTI)

    NONE

    1998-05-01T23:59:59.000Z

    Associated Particle Imaging (API) is an active neutron probe technique that provides a 3-D image with elemental composition of the material under interrogation, and so occupies a unique niche in the interrogation of unknown objects. The highly penetrating nature of neutrons enables API to provide detailed information about targets of interest that are hidden from view. Due to the isotropic nature of the induced reactions, radiation detectors can be set on the same side of the object as the neutron source, so that the object can be interrogated from a single side. At the heat of the system is a small generator that produces a continuous, monoenergetic flux of neutrons. By measuring the trajectory of coincident alpha particles that are produced as part of the process, the trajectory of the neutron can be inferred. Interactions between a neutron and the material in its path often produce a gamma ray whose energy is characteristic of that material. When the gamma ray is detected, its energy is measured and combined with the trajectory information to produce a 3-D image of the composition of the object being interrogated. During the course of API development, a number of improvements have been made. A new, more rugged sealed Tube Neutron Generator (STNG) has been designed and fabricated that is less susceptible to radiation damage and better able to withstand the rigors of fielding than earlier designs. A specialized high-voltage power supply for the STNG has also been designed and built. A complete package of software has been written for the tasks of system calibration, diagnostics and data acquisition and analysis. A portable system has been built and field tested, proving that API can be taken out of the lab and into real-world situations, and that its performance in the field is equal to that in the lab.

  9. Technical support for amending standards for management of uranium byproduct materials: 40 cfr part 192-subpart d. Background information document

    SciTech Connect (OSTI)

    Not Available

    1993-10-01T23:59:59.000Z

    The Environmental Protection Agency (EPA) is amending 40 CFR 192, Subpart D, dealing with disposal of uranium mill tailings at non-operational sites licensed by the Nuclear Regulatory Commission (NRC) or an agreement state pursuant to the Uranium Mill Tailings Radiation Control Act (UMTRCA) of 1978. The Background Information Document (BID) was prepared in support of the rulemaking proceedings for EPA's action. The BID only considers long-term disposal of tailings at facilities licensed by the NRC or an agreement state, and designated Title II facilities in the UMTRCA.

  10. page 1 of 5 Material Transfer Agreement

    E-Print Network [OSTI]

    Baker, Chris I.

    (SMR) of the National Institutes of Health (NIH) Roadmap Molecular Libraries and Imaging Initiative Background Information The NIH Roadmap Molecular Libraries and Imaging Initiative is a research program

  11. SU-E-J-83: Ion Imaging to Better Estimate In-Vivo Relative Stopping Powers Using X-Ray CT Prior-Knowledge Information

    SciTech Connect (OSTI)

    Dias, M [Dipartamento di Elettronica, Informazione e Bioingegneria - DEIB, Politecnico di Milano (Italy); Massachusetts General Hospital, Boston, MA (United States); Collins-Fekete, C [Massachusetts General Hospital, Boston, MA (United States); Departement de physique, de genie physique et d'optique et Centre de recherche sur le cancer, Universite Laval, Quebec (Canada); Riboldi, M; Baroni, G [Dipartamento di Elettronica, Informazione e Bioingegneria - DEIB, Politecnico di Milano (Italy); Doolan, P [Massachusetts General Hospital, Boston, MA (United States); University College London, London (United Kingdom); Hansen, D [Experimental Clinical Oncology, Aarhus University, 8000 Aarhus C (Denmark); Seco, J [Massachusetts General Hospital, Boston, MA (United States)

    2014-06-01T23:59:59.000Z

    Purpose: To reduce uncertainties in relative stopping power (RSP) estimates for ions (alpha and carbon) by using Ion radiographic-imaging and X-ray CT prior-knowledge. Methods: A 3636 phantom matrix composed of 9 materials with different thicknesses and randomly placed is generated. Theoretical RSPs are calculated using stopping power (SP) data from three references (Janni, ICRU49 and Bischel). We introduced an artificial systematic error (1.5%, 2.5% or 3.5%) and a random error (<0.5%) to the SP to simulated patient ion-range errors present in clinic environment. Carbon/alpha final energy for each RSPs set (theoretical and from CT images) is obtained with a ray-tracing algorithm. A gradient descent (GD) method is used to minimize the difference in exit particle energy, between theory and X-ray CT RSP maps, by iteratively correcting the RSP map from X-ray CT. Once a new set of RSPs is obtained for a direction a new optimization is done over other direction using the RSPs from the previous optimization. Theoretical RSPs are compared with experimental RSPs obtained with Gammex Phantom. Results: Preliminary results show that optimized RSP values can be obtained with smaller uncertainties (<1%) than clinical RSPs (1.5% to 3.5%). Theoretical values from three different references show uncertainties, up to 3% from experimental values. Further investigation will consider prior-knowledge from RSP obtained with CT images and ion radiographies from Monte Carlo Simulations. Conclusion: GD and ray-tracing methods have been implemented to reduce RSP uncertainties from values obtained for clinical treatment. Experimental RSPs will be obtained using carbon/alpha beams to consider the existence of material dependent systematic errors. Based on the results, it is hoped to show that using ray-tracing optimization with ion radiography and prior knowledge on RPSs, treatment planning accuracy and cost-effectiveness can be improved.

  12. Porous Materials Porous Materials

    E-Print Network [OSTI]

    Berlin,Technische Universität

    1 Porous Materials x Porous Materials · Physical properties * Characteristic impedance p = p 0 e -jk xa- = vej[ ] p x - j ; Zc= p ve = c ka 0k = c 1-j #12;2 Porous Materials · Specific acoustic impedance Porous Materials · Finite thickness ­ blocked p e + -jk (x-d)a p e - jk (x-d)a d x #12

  13. The authors are with the Vision, Imaging, Video and Audio research laboratory, School of Information Technology and Engineering,

    E-Print Network [OSTI]

    Payeur, Pierre

    of Information Technology and Engineering, University of Ottawa, Ottawa, Canada, K1N 6N5. Email: [ppayeur, yliu found in Northern parts of Canada and Europe also preempt this technology to be widely used. Combustible

  14. Compression of Computer Graphics Images with Image-Based Rendering

    E-Print Network [OSTI]

    Shahabi, Cyrus

    Compression of Computer Graphics Images with Image-Based Rendering Ilmi Yoon and Ulrich Neumann information from previously rendered images. Images predicted from prior images are combined with a residual-based rendering tech- nique provides accurate motion prediction and accelerates rendering at the same time

  15. Estimating radiological background using imaging spectroscopy

    SciTech Connect (OSTI)

    Bernacki, Bruce E.; Schweppe, John E.; Stave, Sean C.; Jordan, David V.; Kulisek, Jonathan A.; Stewart, Trevor N.; Seifert, Carolyn E.

    2014-06-13T23:59:59.000Z

    Optical imaging spectroscopy is investigated as a method to estimate radiological background by spectral identification of soils, sediments, rocks, minerals and building materials derived from natural materials and assigning tabulated radiological emission values to these materials. Radiological airborne surveys are undertaken by local, state and federal agencies to identify the presence of radiological materials out of regulatory compliance. Detection performance in such surveys is determined by (among other factors) the uncertainty in the radiation background; increased knowledge of the expected radiation background will improve the ability to detect low-activity radiological materials. Radiological background due to naturally occurring radiological materials (NORM) can be estimated by reference to previous survey results, use of global 40K, 238U, and 232Th (KUT) values, reference to existing USGS radiation background maps, or by a moving average of the data as it is acquired. Each of these methods has its drawbacks: previous survey results may not include recent changes, the global average provides only a zero-order estimate, the USGS background radiation map resolutions are coarse and are accurate only to 1 km 25 km sampling intervals depending on locale, and a moving average may essentially low pass filter the data to obscure small changes in radiation counts. Imaging spectroscopy from airborne or spaceborne platforms can offer higher resolution identification of materials and background, as well as provide imaging context information. AVIRIS hyperspectral image data is analyzed using commercial exploitation software to determine the usefulness of imaging spectroscopy to identify qualitative radiological background emissions when compared to airborne radiological survey data.

  16. University of Stirling PhD Studentship in Informing Behaviour Change during pregnancy using design and image based principles

    E-Print Network [OSTI]

    Little, Tony

    University of Stirling PhD Studentship in Informing Behaviour Change during pregnancy using design.nmahp.gcal.ac.uk The PhD will be registered within the University of Stirling's School of Nursing, Midwifery and Health electronically or returned by post to the Graduate Admissions Office, University of Stirling, Stirling, FK9 4LA

  17. Search Log Analysis of the ARTstor Cultural Heritage Image Database

    E-Print Network [OSTI]

    Lowe, Heather Ann

    2013-01-01T23:59:59.000Z

    Patterns in a Digital Image Database. Information RetrievalCultural Heritage Image Database A thesis submitted inCultural Heritage Image Database by Heather Ann Lowe Master

  18. Human Functional Brain Imaging

    E-Print Network [OSTI]

    Rambaut, Andrew

    Human Functional Brain Imaging 19902009 September 2011 Portfolio Review #12;2 | Portfolio Review: Human Functional Brain ImagingThe Wellcome Trust is a charity registered in England and Wales, no's role in supporting human functional brain imaging and have informed `our' speculations for the future

  19. Heart imaging method

    DOE Patents [OSTI]

    Collins, H. Dale (Richland, WA); Gribble, R. Parks (Richland, WA); Busse, Lawrence J. (Littleton, CO)

    1991-01-01T23:59:59.000Z

    A method for providing an image of the human heart's electrical system derives time-of-flight data from an array of EKG electrodes and this data is transformed into phase information. The phase information, treated as a hologram, is reconstructed to provide an image in one or two dimensions of the electrical system of the functioning heart.

  20. Applied Materials | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are being directedAnnualProperty EditCalifornia: Energy Resources JumpAnaconda,Anza ElectricInc Jump to:Applied

  1. Training Management Information System

    SciTech Connect (OSTI)

    Rackley, M.P.

    1989-01-01T23:59:59.000Z

    The Training Management Information System (TMIS) is an integrated information system for all training related activities. TMIS is at the leading edge of training information systems used in the nuclear industry. The database contains all the necessary records to confirm the department's adherence to accreditation criteria and houses all test questions, student records and information needed to evaluate the training process. The key to the TMIS system is that the impact of any change (i.e., procedure change, new equipment, safety incident in the commercial nuclear industry, etc.) can be tracked throughout the training process. This ensures the best training can be performed that meets the needs of the employees. TMIS is comprised of six functional areas: Job and Task Analysis, Training Materials Design and Development, Exam Management, Student Records/Scheduling, Evaluation, and Commitment Tracking. The system consists of a VAX 6320 Cluster with IBM and MacIntosh computers tied into an ethernet with the VAX. Other peripherals are also tied into the system: Exam Generation Stations to include mark sense readers for test grading, Production PC's for Desk-Top Publishing of Training Material, and PC Image Workstations. 5 figs.

  2. Time encoded radiation imaging

    DOE Patents [OSTI]

    Marleau, Peter; Brubaker, Erik; Kiff, Scott

    2014-10-21T23:59:59.000Z

    The various technologies presented herein relate to detecting nuclear material at a large stand-off distance. An imaging system is presented which can detect nuclear material by utilizing time encoded imaging relating to maximum and minimum radiation particle counts rates. The imaging system is integrated with a data acquisition system that can utilize variations in photon pulse shape to discriminate between neutron and gamma-ray interactions. Modulation in the detected neutron count rates as a function of the angular orientation of the detector due to attenuation of neighboring detectors is utilized to reconstruct the neutron source distribution over 360 degrees around the imaging system. Neutrons (e.g., fast neutrons) and/or gamma-rays are incident upon scintillation material in the imager, the photons generated by the scintillation material are converted to electrical energy from which the respective neutrons/gamma rays can be determined and, accordingly, a direction to, and the location of, a radiation source identified.

  3. The Safety Data Sheet, or SDS, is written or printed material used to convey the hazards of a hazardous chemical product. It contains 16 sections of important chemical information, including

    E-Print Network [OSTI]

    The Safety Data Sheet, or SDS, is written or printed material used to convey the hazards of a hazardous chemical product. It contains 16 sections of important chemical information, including: Chemical characteristics; Physical and health hazards, including relevant exposure limits; Precautions for safe handling

  4. Staring 2-D hadamard transform spectral imager

    DOE Patents [OSTI]

    Gentry, Stephen M. (Albuquerque, NM); Wehlburg, Christine M. (Albuquerque, NM); Wehlburg, Joseph C. (Albuquerque, NM); Smith, Mark W. (Albuquerque, NM); Smith, Jody L. (Albuquerque, NM)

    2006-02-07T23:59:59.000Z

    A staring imaging system inputs a 2D spatial image containing multi-frequency spectral information. This image is encoded in one dimension of the image with a cyclic Hadamarid S-matrix. The resulting image is detecting with a spatial 2D detector; and a computer applies a Hadamard transform to recover the encoded image.

  5. Restoring functional PET Images using Anatomical MR Images

    E-Print Network [OSTI]

    Mosegaard, Klaus

    Restoring functional PET Images using Anatomical MR Images Peter Alshede Philipsen, Ulrik Kjems,uk,pto,lkh@imm.dtu.dk Abstract In this paper we present a Bayesian method to enhance functional 3D PET images using apriori as a true PET­MR result, and further more show how to obtain the desired information from the MR images. 1

  6. Local Polarization Dynamics in Ferroelectric Materials

    SciTech Connect (OSTI)

    Kalinin, Sergei V [ORNL; Morozovska, A. N. [National Academy of Science of Ukraine, Kiev, Ukraine; Chen, L. Q. [Pennsylvania State University; Rodriguez, Brian J [ORNL

    2010-01-01T23:59:59.000Z

    Ferroelectrics and multiferroics have recently emerged as perspective materials for information technology and data storage applications. The combination of extremely narrow domain wall width and the capability to manipulate polarization by electric field opens the pathway towards ultrahigh (>10 TBit/in2) storage densities and small (sub-10 nm) feature sizes. The coupling between polarization and chemical and transport properties enables applications in ferroelectric lithography and electroresistive devices. The progress in these applications, as well as fundamental studies of polarization dynamics and the role of defects and disorder on domain nucleation and wall motion, requires the capability to probe these effects on the nanometer scale. In this review, we summarize recent progress in applications of Piezoresponse Force Microscopy (PFM) for imaging, manipulation, and spectroscopy of ferroelectric switching processes. We briefly introduce the principles and relevant instrumental aspects of PFM, with special emphasis on resolution and information limits. The local imaging studies of domain dynamics, including local switching and relaxation accessed through imaging experiments, and spectroscopic studies of polarization switching, are discussed in detail. Finally, we briefly review the recent progress on photochemical processes on ferroelectric surfaces, the role of surface adsorbates, and imaging and switching in liquids. Beyond classical applications, probing local bias-induced transition dynamics by PFM opens the pathway to studies of the influence of a single defect on electrochemical and solid state processes, thus providing model systems for batteries, fuel cells, and supercapacitor applications.

  7. Materials Data on VPO4 (SG:63) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  8. Materials Data on Nd (SG:229) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  9. Materials Data on VP (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  10. Materials Data on P (SG:2) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  11. Materials Data on BPO4 (SG:152) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  12. Materials Data on Ge (SG:96) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  13. Materials Data on Ge (SG:225) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  14. Materials Data on Ge (SG:148) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  15. Materials Data on Ge (SG:96) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  16. Materials Data on UGe2 (SG:63) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  17. Materials Data on UGe2 (SG:65) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  18. Materials Data on Ge (SG:69) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  19. Materials Data on Nd (SG:229) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  20. Materials Data on Tc (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  1. Materials Data on Er (SG:229) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  2. Materials Data on YB2 (SG:191) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  3. Materials Data on La (SG:229) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  4. Materials Data on Tb (SG:229) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  5. Materials Data on Dy (SG:229) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  6. Materials Data on YZn (SG:225) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  7. Materials Data on Tm (SG:229) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  8. Materials Data on Lu (SG:229) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  9. Materials Data on B (SG:166) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  10. Materials Data on Fe (SG:194) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  11. Materials Data on YS (SG:225) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  12. Materials Data on Nd (SG:225) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  13. Materials Data on KC10 (SG:204) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  14. Materials Data on Se (SG:148) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  15. Materials Data on VPt2 (SG:71) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  16. Materials Data on Ga (SG:139) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  17. Materials Data on S (SG:221) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  18. Materials Data on UAl2 (SG:227) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  19. Critical Materials Workshop

    Broader source: Energy.gov [DOE]

    AMO hosted a public workshop on Tuesday, April 3, 2012 in Arlington, VA to provide background information on critical materials assessment, the current research within DOE related to critical...

  20. Photothermal imaging scanning microscopy

    DOE Patents [OSTI]

    Chinn, Diane (Pleasanton, CA); Stolz, Christopher J. (Lathrop, CA); Wu, Zhouling (Pleasanton, CA); Huber, Robert (Discovery Bay, CA); Weinzapfel, Carolyn (Tracy, CA)

    2006-07-11T23:59:59.000Z

    Photothermal Imaging Scanning Microscopy produces a rapid, thermal-based, non-destructive characterization apparatus. Also, a photothermal characterization method of surface and subsurface features includes micron and nanoscale spatial resolution of meter-sized optical materials.

  1. Schematic of a scanning electron microscope images with different operational modes

    E-Print Network [OSTI]

    Strathclyde, University of

    for next generation light sources (LED's), LASERS, electronic devices (transistors) and memory storage and devices group Further information: naresh.gunasekar@strath.ac.uk ECCI images showing defects Overview characterisation is essential for the understanding of a wide range of materials, from bulk to nanostructures

  2. Comparison of digital images and compressed video as supplements in the teaching of floral design

    E-Print Network [OSTI]

    MacAlpine, Christine L

    2002-01-01T23:59:59.000Z

    -based methods. A Web site was created containing QuickTime video segments and a second site featured a sequence of digital images of floral design demonstrations. Each of the nine lab designs included information regarding history, usage, and floral materials...

  3. Document Imaging | Department of Energy

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Imaging Converting Paper Documents into Electronic Files Converting paper documents into electronic files helps us manage, store, access and archive the organizational information...

  4. EC Transmission Line Materials

    SciTech Connect (OSTI)

    Bigelow, Tim S [ORNL

    2012-05-01T23:59:59.000Z

    The purpose of this document is to identify materials acceptable for use in the US ITER Project Office (USIPO)-supplied components for the ITER Electron cyclotron Heating and Current Drive (ECH&CD) transmission lines (TL), PBS-52. The source of material property information for design analysis shall be either the applicable structural code or the ITER Material Properties Handbook. In the case of conflict, the ITER Material Properties Handbook shall take precedence. Materials selection, and use, shall follow the guidelines established in the Materials Assessment Report (MAR). Materials exposed to vacuum shall conform to the ITER Vacuum Handbook. [Ref. 2] Commercial materials shall conform to the applicable standard (e.g., ASTM, JIS, DIN) for the definition of their grade, physical, chemical and electrical properties and related testing. All materials for which a suitable certification from the supplier is not available shall be tested to determine the relevant properties, as part of the procurement. A complete traceability of all the materials including welding materials shall be provided. Halogenated materials (example: insulating materials) shall be forbidden in areas served by the detritiation systems. Exceptions must be approved by the Tritium System and Safety Section Responsible Officers.

  5. UNCLASSIFIED UNCLASSIFIED Nuclear Materials Management & Safeguards...

    National Nuclear Security Administration (NNSA)

    UNCLASSIFIED Nuclear Materials Management & Safeguards System CONTACT INFORMATION UPDATE REPORTING IDENTIFICATION SYMBOL (RIS) RIS: Address: Facility Name: CONTACTS Name Email:...

  6. Combined Illumination Cylindrical Millimeter-Wave Imaging Technique for Concealed Weapon Detection

    SciTech Connect (OSTI)

    Sheen, David M.; McMakin, Douglas L.; Hall, Thomas E.

    2000-04-01T23:59:59.000Z

    A novel millimeter-wave imaging technique has been developed for personnel surveillance applications, including the detection of concealed weapons, explosives, drugs, and other contraband material. Millimeter-waves are high-frequency radio waves in the frequency band of 30-300 GHz, and pose no health threat to humans at moderate power levels. These waves readily penetrate common clothing materials, and are reflected by the human body and by concealed items. The combined illumination cylindrical imaging concept consists of a vertical, high-resolution, millimeter-wave array of antennas which is scanned in a cylindrical manner about the person under surveillance. Using a computer, the data from this scan is mathematically reconstructed into a series of focused 3-D images of the person. After reconstruction, the images are combined into a single high-resolution three-dimensional image of the person under surveillance. This combined image is then rendered using 3-D computer graphics techniques. The combined cylindrical illumination is critical as it allows the display of information from all angles. This is necessary because millimeter-waves do not penetrate the body. Ultimately, the images displayed to the operator will be icon-based to protect the privacy of the person being screened. Novel aspects of this technique include the cylindrical scanning concept and the image reconstruction algorithm, which was developed specifically for this imaging system. An engineering prototype based on this cylindrical imaging technique has been fabricated and tested. This work has been sponsored by the Federal Aviation Administration (FAA).

  7. Fluid Imaging | Open Energy Information

    Open Energy Info (EERE)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Home Page onYou are now leaving Energy.gov You are now leaving Energy.gov You are8COaBulkTransmissionSitingProcess.pdf Jump to:ar-80m.pdfFillmoreGabbs Valley Area (DOEARRA Funded Projects for Fluid

  8. Imaging bolometer

    DOE Patents [OSTI]

    Wurden, Glen A. (Los Alamos, NM)

    1999-01-01T23:59:59.000Z

    Radiation-hard, steady-state imaging bolometer. A bolometer employing infrared (IR) imaging of a segmented-matrix absorber of plasma radiation in a cooled-pinhole camera geometry is described. The bolometer design parameters are determined by modeling the temperature of the foils from which the absorbing matrix is fabricated by using a two-dimensional time-dependent solution of the heat conduction equation. The resulting design will give a steady-state bolometry capability, with approximately 100 Hz time resolution, while simultaneously providing hundreds of channels of spatial information. No wiring harnesses will be required, as the temperature-rise data will be measured via an IR camera. The resulting spatial data may be used to tomographically investigate the profile of plasmas.

  9. Imaging bolometer

    DOE Patents [OSTI]

    Wurden, G.A.

    1999-01-19T23:59:59.000Z

    Radiation-hard, steady-state imaging bolometer is disclosed. A bolometer employing infrared (IR) imaging of a segmented-matrix absorber of plasma radiation in a cooled-pinhole camera geometry is described. The bolometer design parameters are determined by modeling the temperature of the foils from which the absorbing matrix is fabricated by using a two-dimensional time-dependent solution of the heat conduction equation. The resulting design will give a steady-state bolometry capability, with approximately 100 Hz time resolution, while simultaneously providing hundreds of channels of spatial information. No wiring harnesses will be required, as the temperature-rise data will be measured via an IR camera. The resulting spatial data may be used to tomographically investigate the profile of plasmas. 2 figs.

  10. RADIOACTIVE MATERIALS SENSORS

    SciTech Connect (OSTI)

    Mayo, Robert M.; Stephens, Daniel L.

    2009-09-15T23:59:59.000Z

    Providing technical means to detect, prevent, and reverse the threat of potential illicit use of radiological or nuclear materials is among the greatest challenges facing contemporary science and technology. In this short article, we provide brief description and overview of the state-of-the-art in sensor development for the detection of radioactive materials, as well as an identification of the technical needs and challenges faced by the detection community. We begin with a discussion of gamma-ray and neutron detectors and spectrometers, followed by a description of imaging sensors, active interrogation, and materials development, before closing with a brief discussion of the unique challenges posed in fielding sensor systems.

  11. Technique Recovers Atomic Resolution in Spectrum Images | ornl...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    in Spectrum Images April 08, 2015 Raw Fe L-shell spectrum image data, which indicate magnetic properties of the material, were acquired using scanning transmission electron...

  12. Covetic Materials

    Energy Savers [EERE]

    Can re-melt, dilute, alloy... Fabrication of Covetic Materials - Nanocarbon Infusion 3 4 Technical Approach Unusual Characteristics of Covetic Materials ("covalent" &...

  13. Approved Module Information for EC312C, 2014/5 Module Title/Name: Advanced Materials Module Code: EC312C

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    to assess the strength and quality of in-situ concrete, including the exposure to the test methods and developments. Repair materials, polymers in concrete. Non-Destructive Testing of Concrete. Reasons for making) advanced appreciation of the main forms of deterioration that affect concrete structures, their methods

  14. Materials Scientist

    Broader source: Energy.gov [DOE]

    Alternate Title(s):Materials Research Engineer; Metallurgical/Chemical Engineer; Product Development Manager;

  15. Complete information acquisition in scanning probe microscopy

    SciTech Connect (OSTI)

    Belianinov, Alex [ORNL; Kalinin, Sergei V [ORNL; Jesse, Stephen [ORNL

    2015-01-01T23:59:59.000Z

    In the last three decades, scanning probe microscopy (SPM) has emerged as a primary tool for exploring and controlling the nanoworld. A critical part of the SPM measurements is the information transfer from the tip-surface junction to a macroscopic measurement system. This process reduces the many degrees of freedom of a vibrating cantilever to relatively few parameters recorded as images. Similarly, the details of dynamic cantilever response at sub-microsecond time scales of transients, higher-order eigenmodes and harmonics are averaged out by transitioning to millisecond time scale of pixel acquisition. Hence, the amount of information available to the external observer is severely limited, and its selection is biased by the chosen data processing method. Here, we report a fundamentally new approach for SPM imaging based on information theory-type analysis of the data stream from the detector. This approach allows full exploration of complex tip-surface interactions, spatial mapping of multidimensional variability of material s properties and their mutual interactions, and SPM imaging at the information channel capacity limit.

  16. Medical Image Registration: A Quick Win

    E-Print Network [OSTI]

    Ansorge, Richard

    2008-11-11T23:59:59.000Z

    Medical Image Registration A Quick Win Richard Ansorge The problem CT, MRI, PET and Ultrasound produce 3D volume images Typically 256 x 256 x 256 = 16,777,216 image voxels. Combining modalities (inter modality) gives extra information. Repeated... imaging over time same modality, e.g. MRI, (intra modality) equally important. Have to spatially register the images. Example brain lesion CT MRI PET PET-MR Fusion The PET image shows...

  17. ATS materials/manufacturing

    SciTech Connect (OSTI)

    Karnitz, M.A.; Wright, I.G.; Ferber, M.K. [and others

    1997-11-01T23:59:59.000Z

    The Materials/Manufacturing Technology subelement is a part of the base technology portion of the Advanced Turbine Systems (ATS) Program. The work in this subelement is being performed predominantly by industry with assistance from national laboratories and universities. The projects in this subelement are aimed toward hastening the incorporation of new materials and components in gas turbines. Work is currently ongoing on thermal barrier coatings (TBCs), the scale-up of single crystal airfoil manufacturing technologies, materials characterization, and technology information exchange. This paper presents highlights of the activities during the past year. 12 refs., 24 figs., 4 tabs.

  18. Acoustic imaging microscope

    DOE Patents [OSTI]

    Deason, Vance A.; Telschow, Kenneth L.

    2006-10-17T23:59:59.000Z

    An imaging system includes: an object wavefront source and an optical microscope objective all positioned to direct an object wavefront onto an area of a vibrating subject surface encompassed by a field of view of the microscope objective, and to direct a modulated object wavefront reflected from the encompassed surface area through a photorefractive material; and a reference wavefront source and at least one phase modulator all positioned to direct a reference wavefront through the phase modulator and to direct a modulated reference wavefront from the phase modulator through the photorefractive material to interfere with the modulated object wavefront. The photorefractive material has a composition and a position such that interference of the modulated object wavefront and modulated reference wavefront occurs within the photorefractive material, providing a full-field, real-time image signal of the encompassed surface area.

  19. Magnetic spectroscopy and microscopy of functional materials

    SciTech Connect (OSTI)

    Jenkins, C.A.

    2011-01-28T23:59:59.000Z

    Heusler intermetallics Mn{sub 2}Y Ga and X{sub 2}MnGa (X; Y =Fe, Co, Ni) undergo tetragonal magnetostructural transitions that can result in half metallicity, magnetic shape memory, or the magnetocaloric effect. Understanding the magnetism and magnetic behavior in functional materials is often the most direct route to being able to optimize current materials for todays applications and to design novel ones for tomorrow. Synchrotron soft x-ray magnetic spectromicroscopy techniques are well suited to explore the the competing effects from the magnetization and the lattice parameters in these materials as they provide detailed element-, valence-, and site-specifc information on the coupling of crystallographic ordering and electronic structure as well as external parameters like temperature and pressure on the bonding and exchange. Fundamental work preparing the model systems of spintronic, multiferroic, and energy-related compositions is presented for context. The methodology of synchrotron spectroscopy is presented and applied to not only magnetic characterization but also of developing a systematic screening method for future examples of materials exhibiting any of the above effects. The chapter progression is as follows: an introduction to the concepts and materials under consideration (Chapter 1); an overview of sample preparation techniques and results, and the kinds of characterization methods employed (Chapter 2); spectro- and microscopic explorations of X{sub 2}MnGa/Ge (Chapter 3); spectroscopic investigations of the composition series Mn{sub 2}Y Ga to the logical Mn{sub 3}Ga endpoint (Chapter 4); and a summary and overview of upcoming work (Chapter 5). Appendices include the results of a Think Tank for the Graduate School of Excellence MAINZ (Appendix A) and details of an imaging project now in progress on magnetic reversal and domain wall observation in the classical Heusler material Co{sub 2}FeSi (Appendix B).

  20. Test Images

    E-Print Network [OSTI]

    Test Images. I hope to have a set of test images for the course soon. Some images are available now; some will have to wait until I can find another 100-200

  1. Enclosure for small animals during awake animal imaging

    DOE Patents [OSTI]

    Goddard, Jr., James S

    2013-11-26T23:59:59.000Z

    An enclosure or burrow restrains an awake animal during an imaging procedure. A tubular body, made from a radiolucent material that does not attenuate x-rays or gamma rays, accepts an awake animal. A proximal end of the body includes an attachment surface that corresponds to an attachment surface of an optically transparent and optically uniform window. An anti-reflective coating may be applied to an inner surface, an outer surface, or both surfaces of the window. Since the window is a separate element of the enclosure and it is not integrally formed as part of the body, it can be made with optically uniform thickness properties for improved motion tracking of markers on the animal with a camera during the imaging procedure. The motion tracking information is then used to compensate for animal movement in the image.

  2. Supplementary Material: Images, Papers and Videos (Electronic)

    E-Print Network [OSTI]

    Collomosse, John

    .tif Chinese dragon after 70 iterations of GA relaxation modelface.tif Man on a rock (detail on face before sharpening) relaxation.mpg Video of the GA relaxation process for dragon rock.tif Man on a rock] are included in the /papers directory. C.3 Videos The following source videos and animations are included

  3. Impacted material placement plans

    SciTech Connect (OSTI)

    Hickey, M.J.

    1997-01-29T23:59:59.000Z

    Impacted material placement plans (IMPP) are documents identifying the essential elements in placing remediation wastes into disposal facilities. Remediation wastes or impacted material(s) are those components used in the construction of the disposal facility exclusive of the liners and caps. The components might include soils, concrete, rubble, debris, and other regulatory approved materials. The IMPP provides the details necessary for interested parties to understand the management and construction practices at the disposal facility. The IMPP should identify the regulatory requirements from applicable DOE Orders, the ROD(s) (where a part of a CERCLA remedy), closure plans, or any other relevant agreements or regulations. Also, how the impacted material will be tracked should be described. Finally, detailed descriptions of what will be placed and how it will be placed should be included. The placement of impacted material into approved on-site disposal facilities (OSDF) is an integral part of gaining regulatory approval. To obtain this approval, a detailed plan (Impacted Material Placement Plan [IMPP]) was developed for the Fernald OSDF. The IMPP provides detailed information for the DOE, site generators, the stakeholders, regulatory community, and the construction subcontractor placing various types of impacted material within the disposal facility.

  4. Process for Low Cost Domestic Production of LIB Cathode Materials

    Broader source: Energy.gov (indexed) [DOE]

    information" 4 Approach BASF has a low cost production process for Li ion battery cathode materials. In this project, the cathode materials developed in the laboratory will be...

  5. Scintillator material

    DOE Patents [OSTI]

    Anderson, David F. (Batavia, IL); Kross, Brian J. (Aurora, IL)

    1994-01-01T23:59:59.000Z

    An improved scintillator material comprising cerium fluoride is disclosed. Cerium fluoride has been found to provide a balance of good stopping power, high light yield and short decay constant that is superior to known scintillator materials such as thallium-doped sodium iodide, barium fluoride and bismuth germanate. As a result, cerium fluoride is favorably suited for use as a scintillator material in positron emission tomography.

  6. Scintillator material

    DOE Patents [OSTI]

    Anderson, David F. (Batavia, IL); Kross, Brian J. (Aurora, IL)

    1992-01-01T23:59:59.000Z

    An improved scintillator material comprising cerium fluoride is disclosed. Cerium fluoride has been found to provide a balance of good stopping power, high light yield and short decay constant that is superior to known scintillator materials such as thallium-doped sodium iodide, barium fluoride and bismuth germanate. As a result, cerium fluoride is favorably suited for use as a scintillator material in positron emission tomography.

  7. Scintillator material

    DOE Patents [OSTI]

    Anderson, D.F.; Kross, B.J.

    1992-07-28T23:59:59.000Z

    An improved scintillator material comprising cerium fluoride is disclosed. Cerium fluoride has been found to provide a balance of good stopping power, high light yield and short decay constant that is superior to known scintillator materials such as thallium-doped sodium iodide, barium fluoride and bismuth germanate. As a result, cerium fluoride is favorably suited for use as a scintillator material in positron emission tomography. 4 figs.

  8. Scintillator material

    DOE Patents [OSTI]

    Anderson, D.F.; Kross, B.J.

    1994-06-07T23:59:59.000Z

    An improved scintillator material comprising cerium fluoride is disclosed. Cerium fluoride has been found to provide a balance of good stopping power, high light yield and short decay constant that is superior to known scintillator materials such as thallium-doped sodium iodide, barium fluoride and bismuth germanate. As a result, cerium fluoride is favorably suited for use as a scintillator material in positron emission tomography. 4 figs.

  9. Energetic materials at extreme conditions

    E-Print Network [OSTI]

    Millar, David Iain Archibald

    2011-06-27T23:59:59.000Z

    In order to effectively model the behaviour of energetic materials under operational conditions it is essential to obtain detailed structural information for these compounds at elevated temperature and/or pressures. The ...

  10. Critical Materials:

    Broader source: Energy.gov (indexed) [DOE]

    lighting. 14 (bottom) Criticality ratings of shortlisted raw 76 materials. 15 77 2. Technology Assessment and Potential 78 This section reviews the major trends within...

  11. Nuclear material operations manual

    SciTech Connect (OSTI)

    Tyler, R.P.

    1981-02-01T23:59:59.000Z

    This manual provides a concise and comprehensive documentation of the operating procedures currently practiced at Sandia National Laboratories with regard to the management, control, and accountability of nuclear materials. The manual is divided into chapters which are devoted to the separate functions performed in nuclear material operations-management, control, accountability, and safeguards, and the final two chapters comprise a document which is also issued separately to provide a summary of the information and operating procedures relevant to custodians and users of radioactive and nuclear materials. The manual also contains samples of the forms utilized in carrying out nuclear material activities. To enhance the clarity of presentation, operating procedures are presented in the form of playscripts in which the responsible organizations and necessary actions are clearly delineated in a chronological fashion from the initiation of a transaction to its completion.

  12. An examination of electronic file transfer between host and microcomputers for the AMPMODNET/AIMNET (Army Material Plan Modernization Network/Acquisition Information Management Network) classified network environment

    SciTech Connect (OSTI)

    Hake, K.A.

    1990-11-01T23:59:59.000Z

    This report presents the results of investigation and testing conducted by Oak Ridge National Laboratory (ORNL) for the Project Manager -- Acquisition Information Management (PM-AIM), and the United States Army Materiel Command Headquarters (HQ-AMC). It concerns the establishment of file transfer capabilities on the Army Materiel Plan Modernization (AMPMOD) classified computer system. The discussion provides a general context for micro-to-mainframe connectivity and focuses specifically upon two possible solutions for file transfer capabilities. The second section of this report contains a statement of the problem to be examined, a brief description of the institutional setting of the investigation, and a concise declaration of purpose. The third section lays a conceptual foundation for micro-to-mainframe connectivity and provides a more detailed description of the AMPMOD computing environment. It gives emphasis to the generalized International Business Machines, Inc. (IBM) standard of connectivity because of the predominance of this vendor in the AMPMOD computing environment. The fourth section discusses two test cases as possible solutions for file transfer. The first solution used is the IBM 3270 Control Program telecommunications and terminal emulation software. A version of this software was available on all the IBM Tempest Personal Computer 3s. The second solution used is Distributed Office Support System host electronic mail software with Personal Services/Personal Computer microcomputer e-mail software running with IBM 3270 Workstation Program for terminal emulation. Test conditions and results are presented for both test cases. The fifth section provides a summary of findings for the two possible solutions tested for AMPMOD file transfer. The report concludes with observations on current AMPMOD understanding of file transfer and includes recommendations for future consideration by the sponsor.

  13. Cermet materials

    DOE Patents [OSTI]

    Kong, Peter C. (Idaho Falls, ID)

    2008-12-23T23:59:59.000Z

    A self-cleaning porous cermet material, filter and system utilizing the same may be used in filtering particulate and gaseous pollutants from internal combustion engines having intermetallic and ceramic phases. The porous cermet filter may be made from a transition metal aluminide phase and an alumina phase. Filler materials may be added to increase the porosity or tailor the catalytic properties of the cermet material. Additionally, the cermet material may be reinforced with fibers or screens. The porous filter may also be electrically conductive so that a current may be passed therethrough to heat the filter during use. Further, a heating element may be incorporated into the porous cermet filter during manufacture. This heating element can be coated with a ceramic material to electrically insulate the heating element. An external heating element may also be provided to heat the cermet filter during use.

  14. Composite material

    DOE Patents [OSTI]

    Hutchens, Stacy A. (Knoxville, TN); Woodward, Jonathan (Solihull, GB); Evans, Barbara R. (Oak Ridge, TN); O'Neill, Hugh M. (Knoxville, TN)

    2012-02-07T23:59:59.000Z

    A composite biocompatible hydrogel material includes a porous polymer matrix, the polymer matrix including a plurality of pores and providing a Young's modulus of at least 10 GPa. A calcium comprising salt is disposed in at least some of the pores. The porous polymer matrix can comprise cellulose, including bacterial cellulose. The composite can be used as a bone graft material. A method of tissue repair within the body of animals includes the steps of providing a composite biocompatible hydrogel material including a porous polymer matrix, the polymer matrix including a plurality of pores and providing a Young's modulus of at least 10 GPa, and inserting the hydrogel material into cartilage or bone tissue of an animal, wherein the hydrogel material supports cell colonization in vitro for autologous cell seeding.

  15. Material stabilization characterization management plan

    SciTech Connect (OSTI)

    GIBSON, M.W.

    1999-08-31T23:59:59.000Z

    This document presents overall direction for characterization needs during stabilization of SNM at the Plutonium Finishing Plant (PFP). Technical issues for needed data and equipment are identified. Information on material categories and links to vulnerabilities are given. Comparison data on the material categories is discussed to assist in assessing the relative risks and desired processing priority.

  16. Radioactive Samples / Materials at the APS

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Using Radioactive Samples Materials at the APS The use of radioactive samples requires additional information for review and approval. All proposed experiments involving...

  17. Simultaneous acquisition of differing image types

    DOE Patents [OSTI]

    Demos, Stavros G

    2012-10-09T23:59:59.000Z

    A system in one embodiment includes an image forming device for forming an image from an area of interest containing different image components; an illumination device for illuminating the area of interest with light containing multiple components; at least one light source coupled to the illumination device, the at least one light source providing light to the illumination device containing different components, each component having distinct spectral characteristics and relative intensity; an image analyzer coupled to the image forming device, the image analyzer decomposing the image formed by the image forming device into multiple component parts based on type of imaging; and multiple image capture devices, each image capture device receiving one of the component parts of the image. A method in one embodiment includes receiving an image from an image forming device; decomposing the image formed by the image forming device into multiple component parts based on type of imaging; receiving the component parts of the image; and outputting image information based on the component parts of the image. Additional systems and methods are presented.

  18. Fluorescent image tracking velocimeter

    DOE Patents [OSTI]

    Shaffer, Franklin D. (Library, PA)

    1994-01-01T23:59:59.000Z

    A multiple-exposure fluorescent image tracking velocimeter (FITV) detects and measures the motion (trajectory, direction and velocity) of small particles close to light scattering surfaces. The small particles may follow the motion of a carrier medium such as a liquid, gas or multi-phase mixture, allowing the motion of the carrier medium to be observed, measured and recorded. The main components of the FITV include: (1) fluorescent particles; (2) a pulsed fluorescent excitation laser source; (3) an imaging camera; and (4) an image analyzer. FITV uses fluorescing particles excited by visible laser light to enhance particle image detectability near light scattering surfaces. The excitation laser light is filtered out before reaching the imaging camera allowing the fluoresced wavelengths emitted by the particles to be detected and recorded by the camera. FITV employs multiple exposures of a single camera image by pulsing the excitation laser light for producing a series of images of each particle along its trajectory. The time-lapsed image may be used to determine trajectory and velocity and the exposures may be coded to derive directional information.

  19. Exploring Interaction Between Images and Texts for Web Image Categorization

    E-Print Network [OSTI]

    Li, Tao

    , Jingxuan Li1 , Tao Li1 1 School of Computing and Information Sciences Florida International University on a manually collected image dataset (consist- ing of images related to the events after disasters) demon users in mul- timedia databases is becoming more and more difficult and challenging. Particulary, web

  20. Material Transfer Agreement NIH Molecular Libraries Screening Centers Network (MLSCN) of the

    E-Print Network [OSTI]

    Baker, Chris I.

    ) of the National Institutes of Health (NIH) Roadmap Molecular Libraries and Imaging Initiative Background Information The NIH Roadmap Molecular Libraries and Imaging Initiative is a research program designed

  1. Frontiers in Chemical Imaging Seminar Series

    E-Print Network [OSTI]

    the positions of Professor in the Dept. of Materials Science and Engineering, University of TennesseeFrontiers in Chemical Imaging Seminar Series Presented by Dr. Stephen J Pennycook, Ph.D. Materials properties. Finally, the direct imaging and identification of point defect configurations in monolayer BN

  2. \\NeuroImage" Informatics

    E-Print Network [OSTI]

    Nielsen, Finn ?rup

    International Neuroimaging Consortium Introduction Author cocitation analysis describes a scienti#12;c #12;eld on data collected by the Institute of Scien- ti#12;c Information among a limited set of key au- thors within a #12;eld. Here we work on data from a single journal (the journal \\NeuroImage") down- loaded from

  3. Material Symbols

    E-Print Network [OSTI]

    Clark, Andy

    2006-01-01T23:59:59.000Z

    What is the relation between the material, conventional symbol structures that we encounter in the spoken and written word, and human thought? A common assumption, that structures a wide variety of otherwise competing ...

  4. Complex Materials

    ScienceCinema (OSTI)

    Cooper, Valentino

    2014-05-23T23:59:59.000Z

    Valentino Cooper uses some of the world's most powerful computing to understand how materials work at subatomic levels, studying breakthroughs such as piezoelectrics, which convert mechanical stress to electrical energy.

  5. Distortion-free magnetic resonance imaging in the zero-field limit

    SciTech Connect (OSTI)

    Kelso, Nathan; Lee, Seung-Kyun; Bouchard, Louis-S.; Demas, Vasiliki; Muck, Michael; Pines, Alexander; Clarke, John

    2009-07-09T23:59:59.000Z

    MRI is a powerful technique for clinical diagnosis and materials characterization. Images are acquired in a homogeneous static magnetic field much higher than the fields generated across the field of view by the spatially encoding field gradients. Without such a high field, the concomitant components of the field gradient dictated by Maxwell's equations lead to severe distortions that make imaging impossible with conventional MRI encoding. In this paper, we present a distortion-free image of a phantom acquired with a fundamentally different methodology in which the applied static field approaches zero. Our technique involves encoding with pulses of uniform and gradient field, and acquiring the magnetic field signals with a SQUID. The method can be extended to weak ambient fields, potentially enabling imaging in the Earth's field without cancellation coils or shielding. Other potential applications include quantum information processing and fundamental studies of long-range ferromagnetic interactions.

  6. Overview of Image Reconstruction

    SciTech Connect (OSTI)

    Marr, R.B.

    1980-04-01T23:59:59.000Z

    Image reconstruction (or computerized tomography, etc.) is any process whereby a function, f, on Rn is estimated from empirical data pertaining to its integrals, ∫f(x) dx, for some collection of hyperplanes of dimension k < n. The paper begins with background information on how image reconstruction problems have arisen in practice, and describes some of the application areas of past or current interest; these include radioastronomy, optics, radiology and nuclear medicine, electron microscopy, acoustical imaging, geophysical tomography, nondestructive testing, and NMR zeugmatography. Then the various reconstruction algorithms are discussed in five classes: summation, or simple back-projection; convolution, or filtered back-projection; Fourier and other functional transforms; orthogonal function series expansion; and iterative methods. Certain more technical mathematical aspects of image reconstruction are considered from the standpoint of uniqueness, consistency, and stability of solution. The paper concludes by presenting certain open problems. 73 references. (RWR)

  7. People Images

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    People Images People Images Several hundred of the 1700 U.S. scientists contributing to the LHC accelerator and experiments gathered in June 2008 in CERN's building 40 CE0252 Joel...

  8. Materializing interaction

    E-Print Network [OSTI]

    Coelho, Marcelo

    2013-01-01T23:59:59.000Z

    At the boundary between people, objects and spaces, we encounter a broad range of surfaces. Their properties perform functional roles such as permeability, comfort or illumination, while conveying information such as an ...

  9. Materials Data on LaTl3 (SG:221) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  10. Materials Data on Ba3P4 (SG:43) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  11. Materials Data on Sr3P4 (SG:43) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  12. Materials Data on K3Sb (SG:225) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  13. Materials Data on Rb2Se (SG:225) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  14. Materials Data on EuB6 (SG:221) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  15. Materials Data on LiCoPO4 (SG:62) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  16. Materials Data on HfCr2 (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  17. Materials Data on DyNi (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  18. Materials Data on ThRe2 (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  19. Materials Data on Dy2SO2 (SG:164) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  20. Materials Data on MgPt (SG:198) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  1. Materials Data on FeClO (SG:59) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  2. Materials Data on PuCo3 (SG:166) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  3. Materials Data on Fe3(O2F)2 (SG:1) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  4. Materials Data on FeS2 (SG:58) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  5. Materials Data on Co3S4 (SG:227) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  6. Materials Data on Mn2Nb (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  7. Materials Data on Er(NiGe)2 (SG:139) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  8. Materials Data on SrO (SG:225) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  9. Materials Data on Ba2FeReO6 (SG:225) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  10. Materials Data on Al5Co2 (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  11. Materials Data on Ho2SO2 (SG:164) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  12. Materials Data on TbIr2 (SG:227) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  13. Materials Data on InN (SG:186) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  14. Materials Data on Ti5Sn3 (SG:193) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  15. Materials Data on PuGe2 (SG:141) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  16. Materials Data on Ba2CoReO6 (SG:225) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  17. Materials Data on Ni3Sn (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  18. Materials Data on Mn3O4 (SG:141) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  19. Materials Data on Na2BHO3 (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  20. Materials Data on Pr3GaC (SG:221) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  1. Materials Data on USnPt (SG:216) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  2. Materials Data on CrO3 (SG:40) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  3. Materials Data on Pb(CO2)2 (SG:2) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  4. Materials Data on Er2C(NO)2 (SG:164) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  5. Materials Data on SnPd (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  6. Materials Data on Ba2ReNiO6 (SG:225) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  7. Materials Data on TaBe12 (SG:139) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  8. Materials Data on TiCo2Sn (SG:225) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  9. Materials Data on HfAl2 (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  10. Materials Data on Ba2MgReO6 (SG:225) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  11. Materials Data on ZrSnRh (SG:190) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  12. Materials Data on TbGaPd (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  13. Materials Data on Pr2SO2 (SG:164) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  14. Materials Data on CsIO3 (SG:221) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  15. Materials Data on GdGe (SG:63) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  16. Materials Data on YCrO3 (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  17. Materials Data on Si2H2O3 (SG:148) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  18. Materials Data on BaTiO3 (SG:123) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  19. Materials Data on Ba2ZnReO6 (SG:225) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  20. Materials Data on Pu2Co (SG:189) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  1. Materials Data on Tb2SO2 (SG:164) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  2. Materials Data on Bi2O3 (SG:224) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  3. Materials Data on Lu2SO2 (SG:164) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  4. Materials Data on CdO2 (SG:205) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  5. Materials Data on TaMn2 (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  6. Materials Data on PrNbO4 (SG:15) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  7. Materials Data on Ba2MnReO6 (SG:225) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  8. Materials Data on CrO (SG:225) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  9. Materials Data on GdNbO4 (SG:15) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  10. Materials Data on Ti2Be17 (SG:166) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  11. Materials Data on CeInAu2 (SG:225) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  12. Materials Data on U2Se3 (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  13. Materials Data on LuB6 (SG:221) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  14. Materials Data on ErNbO4 (SG:15) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  15. Materials Data on Na2O2 (SG:189) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  16. Materials Data on LaSiIr (SG:198) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  17. Materials Data on Er2SO2 (SG:164) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  18. Materials Data on La2SiO5 (SG:14) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  19. Materials Data on Yb2SO2 (SG:164) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  20. Materials Data on UH12C4N4O11 (SG:14) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  1. Materials Data on Mg5Si6 (SG:12) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  2. Materials Data on AlPt3C (SG:221) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  3. Materials Data on AlPt3 (SG:221) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  4. Materials Data on GdGe (SG:63) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  5. Materials Data on Ba2MgReO6 (SG:225) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  6. Materials Data on Ba2ZnReO6 (SG:225) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  7. Materials Data on Nb5(NiP)4 (SG:87) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  8. Materials Data on Ca3PN (SG:221) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  9. Materials Data on EuKPSe4 (SG:11) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  10. Materials Data on LiZr2(PO4)3 (SG:14) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  11. Materials Data on KCa(PO3)3 (SG:188) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  12. Materials Data on NaPF6 (SG:225) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  13. Materials Data on CaPSe3 (SG:14) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  14. Materials Data on Pr(ZnP)3 (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  15. Materials Data on CoPSe (SG:61) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  16. Materials Data on P2W (SG:36) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  17. Materials Data on Pr(CdP)3 (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  18. Materials Data on SrAgP (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  19. Materials Data on NiMoP2 (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  20. Materials Data on LiZnP (SG:216) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  1. Materials Data on ZnP2 (SG:96) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  2. Materials Data on LuPPt (SG:187) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  3. Materials Data on LiScP2O7 (SG:4) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  4. Materials Data on Sr2Cu(PO4)2 (SG:12) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  5. Materials Data on LiCdP (SG:216) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  6. Materials Data on Cs(MnP)2 (SG:139) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  7. Materials Data on Mg(P2Rh3)2 (SG:187) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  8. Materials Data on P2W (SG:12) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  9. Materials Data on ErP (SG:225) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  10. Materials Data on Sm(PRu)2 (SG:139) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  11. Materials Data on Mg2Co12P7 (SG:174) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  12. Materials Data on Ce(P3Ru)4 (SG:204) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  13. Materials Data on Na3PS3O (SG:36) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  14. Materials Data on BaPSe3 (SG:14) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  15. Materials Data on Ba(POs)2 (SG:139) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  16. Materials Data on NaErPO4F (SG:12) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  17. Materials Data on Na2MgPO4F (SG:60) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  18. Materials Data on Mo3P (SG:121) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  19. Materials Data on NpP (SG:225) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  20. Materials Data on KVP2S7 (SG:5) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  1. Materials Data on SrLiP (SG:187) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  2. Materials Data on MoP4 (SG:15) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  3. Materials Data on ZrP (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  4. Materials Data on Zr(NiP)2 (SG:139) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  5. Materials Data on LuP (SG:225) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  6. Materials Data on Ni12P5 (SG:87) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  7. Materials Data on TiPRu (SG:189) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  8. Materials Data on ScNiP (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  9. Materials Data on NaYPO4F (SG:12) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  10. Materials Data on K4ZnP2 (SG:166) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  11. Materials Data on ZrMoP (SG:189) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  12. Materials Data on Ba3P14 (SG:14) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  13. Materials Data on Ca(PIr)2 (SG:154) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  14. Materials Data on P2Ir (SG:14) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  15. Materials Data on SmPPd (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  16. Materials Data on Er(PRu)2 (SG:139) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  17. Materials Data on SrSnP (SG:129) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  18. Materials Data on LiMgP (SG:216) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  19. Materials Data on Nb5(PPd)4 (SG:87) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  20. Materials Data on Ba(PIr)2 (SG:139) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  1. Materials Data on FeP (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  2. Materials Data on SrP3 (SG:12) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  3. Materials Data on LiZnPS4 (SG:82) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  4. Materials Data on CaPAu (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  5. Materials Data on BaAg(PO3)3 (SG:19) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  6. Materials Data on Li2CuP (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  7. Materials Data on Mg(Co3P2)2 (SG:187) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  8. Materials Data on NaPN2 (SG:122) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  9. Materials Data on SmPPt (SG:187) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  10. Materials Data on YbNa(PS3)2 (SG:2) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  11. Materials Data on Ca2Co12P7 (SG:174) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  12. Materials Data on Ca5P12Rh19 (SG:189) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  13. Materials Data on PrPPd (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  14. Materials Data on Ni2P (SG:189) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  15. Materials Data on TiCrP (SG:189) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  16. Materials Data on Na3Sr3GaP4 (SG:186) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  17. Materials Data on K2Mg(PSe3)2 (SG:14) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  18. Materials Data on BaPS3 (SG:14) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  19. Materials Data on BaLiP (SG:187) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  20. Materials Data on CoH6(CO3)2 (SG:14) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  1. Materials Data on NdMg3 (SG:225) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  2. Materials Data on KCd3H8Cl7O4 (SG:11) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  3. Materials Data on Na3H5(CO2)4 (SG:2) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  4. Materials Data on InH8C4NO10 (SG:180) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  5. Materials Data on H4Pb(CO3)2 (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  6. Materials Data on Sc2PbS4 (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  7. Materials Data on Nb3H12C4NCl9 (SG:164) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  8. Materials Data on Ca(ScS2)2 (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  9. Materials Data on CsTiF4 (SG:129) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  10. Materials Data on PbSO4 (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  11. Materials Data on CuH3C2NO4 (SG:61) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  12. Materials Data on TiH8C4NO10 (SG:181) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  13. Materials Data on LaH4C4NO8 (SG:14) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  14. Materials Data on TiH8C4NO10 (SG:180) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  15. Materials Data on H12W3C4NCl9 (SG:164) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  16. Materials Data on SrCu2GeSe4 (SG:40) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  17. Materials Data on K2ZrGe2O7 (SG:15) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  18. Materials Data on CdGeP2 (SG:225) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  19. Materials Data on SrLiGe2 (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  20. Materials Data on Th(GeAu)2 (SG:139) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  1. Materials Data on Ca(GeRh)2 (SG:139) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  2. Materials Data on Ge(Te2As)2 (SG:166) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  3. Materials Data on K2ZrGe2O7 (SG:15) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  4. Materials Data on Li(NiGe)6 (SG:191) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  5. Materials Data on PuGe2 (SG:141) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  6. Materials Data on LiInGe (SG:216) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  7. Materials Data on LaGe3Rh (SG:107) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  8. Materials Data on Ag6Ge2O7 (SG:4) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  9. Materials Data on Mg(CoGe)6 (SG:191) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  10. Materials Data on Ge(Te2As)2 (SG:166) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  11. Materials Data on TiGePd (SG:189) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  12. Materials Data on Tm2Ge2O7 (SG:92) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  13. Materials Data on CaMgGe (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  14. Materials Data on CeGe3Rh (SG:107) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  15. Materials Data on LaCrGe3 (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  16. Materials Data on ZnAg2GeO4 (SG:7) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  17. Materials Data on Tb2Ge2O7 (SG:92) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  18. Materials Data on Ge2Te5As2 (SG:164) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  19. Materials Data on LiNdGe (SG:189) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  20. Materials Data on CeScGe (SG:139) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  1. Materials Data on LiSmGe (SG:189) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  2. Materials Data on Ho2Ge2Os (SG:12) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  3. Materials Data on Zn3Ni2Ge (SG:227) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  4. Materials Data on Tm2Ge2O7 (SG:92) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  5. Materials Data on Lu4Zn5Ge6 (SG:36) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  6. Materials Data on SrNi2Ge (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  7. Materials Data on Ca(GeIr)2 (SG:139) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  8. Materials Data on SrCaGe (SG:62) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  9. Materials Data on NaAlGeO4 (SG:14) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  10. Materials Data on Sm5Ge4 (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  11. Materials Data on SrGe2 (SG:62) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  12. Materials Data on TmGe2 (SG:63) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  13. Materials Data on ZnNi2Ge (SG:225) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  14. Materials Data on SrMgGe (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  15. Materials Data on Co2Ge (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  16. Materials Data on ZnAg2GeO4 (SG:7) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  17. Materials Data on CaZn(GeO3)2 (SG:15) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  18. Materials Data on BaCaGe (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  19. Materials Data on SrCaGe (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  20. Materials Data on Ca(GePd)2 (SG:139) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  1. Materials Data on PrCrGe3 (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  2. Materials Data on Li2HgGe (SG:225) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  3. Materials Data on Th(GeAu)2 (SG:139) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  4. Materials Data on SrCu2GeSe4 (SG:40) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  5. Materials Data on Er(NiGe)2 (SG:139) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  6. Materials Data on Li2ZnGe (SG:216) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  7. Materials Data on Np(GeRh)2 (SG:139) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  8. Materials Data on Ni2Ge (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  9. Materials Data on Cu2GeS3 (SG:9) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  10. Materials Data on CaZn(GeO3)2 (SG:15) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  11. Materials Data on Tb5Ge4 (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  12. Materials Data on CeGe3Ir (SG:107) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  13. Materials Data on NdCoGe3 (SG:107) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  14. Materials Data on Tm4Zn5Ge6 (SG:36) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  15. Materials Data on TiNiGe (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  16. Materials Data on Tl2Ge2S5 (SG:15) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  17. Materials Data on Li2GeO3 (SG:36) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  18. Materials Data on CdGeO3 (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  19. Materials Data on Na5GeAs3 (SG:14) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  20. Materials Data on Tb2Ge2O7 (SG:92) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  1. Materials Data on La3(GeRh)4 (SG:71) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  2. Materials Data on CaZnGe (SG:194) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  3. Materials Data on Er(AlGe)2 (SG:164) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  4. Materials Data on GePt3 (SG:140) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  5. Materials Data on SrGe2 (SG:62) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  6. Materials Data on Dy(CrGe)2 (SG:139) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  7. Materials Data on NaAlGeO4 (SG:14) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  8. Materials Data on LiNi2Ge (SG:225) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  9. Materials Data on ThGe2 (SG:63) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  10. Materials Data on ThGe2 (SG:65) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  11. Materials Data on Np(GeRh)2 (SG:139) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  12. Materials Data on RbNa2Ge17 (SG:227) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02T23:59:59.000Z

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  13. Materials Data on Ge9Pd25 (SG:147) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  14. Materials Data on Ho3Ge4 (SG:63) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  15. Materials Data on PrNiGe3 (SG:65) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  16. Materials Data on Th2Ge (SG:140) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  17. Materials Data on Nd5Ge3 (SG:193) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  18. Materials Data on Ho2InGe2 (SG:127) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  19. Materials Data on Er3Al3NiGe2 (SG:189) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations

  20. Materials Data on TmTiGe (SG:129) by Materials Project

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Kristin Persson

    Computed materials data using density functional theory calculations. These calculations determine the electronic structure of bulk materials by solving approximations to the Schrodinger equation. For more information, see https://materialsproject.org/docs/calculations