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Sample records for information materials image

  1. Institute for Functional Imaging of Materials

    E-Print Network [OSTI]

    Weston, Ken

    Institute for Functional Imaging of Materials (IFIM) Guiding the design of materials tailored Kalman filters New methods Bigdata Imaging Electronic Structure Molecular Dynamics Multiscale Ab Initio dynamics electrons to materials and architectures #12;3 Imaging: The big picture ORNL has unique strengths

  2. Information system revives materials management

    SciTech Connect (OSTI)

    Hansen, T.

    1995-12-01

    Through a change in philosophy and the development of a new, more efficient information management system, Arizona Public Service Co. (APSW) has, in less than two years, reduced material and service costs by 10 percent. The utility plans to cut these costs form 1993 figures by 25 percent before 2000. The utility is breaking new ground with ongoing implementation of new business processes and the new Materials Logistics Information System (MLIS), which has been co-developed with Texas Instruments Software Division (TISD).

  3. The contents of this article and referenced websites, such as text, graphics, images, and other material contained on the site are for informational purposes only. The content is not intended to be a substitute for professional medical advice, diagnosis,

    E-Print Network [OSTI]

    Washington at Seattle, University of

    The contents of this article and referenced websites, such as text, graphics, images, and other material contained on the site are for informational purposes only. The content is not intended for the contents of any "off-site" web page referenced from this server. © APS Healthcare, Inc., White Plains, NY

  4. 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

  5. Three Dimensional Molecular Imaging for Lignocellulosic Materials

    SciTech Connect (OSTI)

    Bohn, Paul W.; Sweedler, Jonathan V.

    2011-06-09

    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. X-ray imaging reveals secrets in battery materials | Argonne...

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

    X-ray imaging reveals secrets in battery materials June 22, 2015 Tweet EmailPrint Imaging and data analysis techniques offer new approach to probing material properties In a new...

  7. 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

  8. Advanced materials: Information and analysis needs

    SciTech Connect (OSTI)

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

    1990-09-01

    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.

  9. Serious Materials | Open Energy Information

    Open Energy Info (EERE)

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  10. Reflecting the instant : information, image, architecture

    E-Print Network [OSTI]

    Solander, Carl A., 1977-

    2005-01-01

    A response to the growing importance of designing in an environment that is composed both of physical and non-physical characteristics, this thesis explores a process whereby information is given a visual form through image ...

  11. Image-Based Reconstruction of Spatially Varying Materials

    E-Print Network [OSTI]

    Heidrich, Wolfgang

    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

  12. 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

    2010-12-21

    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.

  13. 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

  14. Fluid Imaging | Open Energy Information

    Open Energy Info (EERE)

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  15. Hyperspectral Imaging | Open Energy Information

    Open Energy Info (EERE)

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  16. Multispectral Imaging | Open Energy Information

    Open Energy Info (EERE)

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  17. Tailored Materials 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Information Serbia-Enhancing CapacityVectren)Model forTechnologies95Symerton,E CTEPTTPTailored Materials

  18. Materials Research in the Information Age

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

    at taking the guesswork out of new materials discoveries, especially those aimed at energy applications like batteries. (Roy Kaltschmidt, LBNL) New materials are crucial to...

  19. Information Engineering Option (paper 8) Photo Editing and Image Search

    E-Print Network [OSTI]

    Ghahramani, Zoubin

    , and act on it. #12;Information Engineering Year 3 (Part IIA) Modules · 3F1 Signals and systems · 3F2 systems · 3F6 Software engineering and design #12;Information Engineering Year 4 (Part IIB) Modules · 4F1Information Engineering Option (paper 8) Photo Editing and Image Search Part C - Image Searching

  20. Information-based analysis of simple incoherent imaging systems

    E-Print Network [OSTI]

    Ashok, Amit

    Information-based analysis of simple incoherent imaging systems Amit Ashok and Mark A. Neifeld@ece.arizona.edu Abstract: We present an information-based analysis of three candidate imagers: a conventional lens system.3050) Information processing; (220.4830) Op- tical systems design References and links 1. J. van der Gracht and G. W

  1. Chemical imaging of biological materials by NanoSIMS

    SciTech Connect (OSTI)

    Weber, P K; Smith, J B; Hutcheon, I D; Shmakov, A; Rybitskaya, I; Curran, H

    2004-08-23

    The NanoSIMS 50 represents the state -of-the-art for in situ microanalysis for secondary ion mass spectrometry (SIMS), combining unprecedented spatial resolution (as good as 50 nm) with ultra-high sensitivity (MDL of 200 atoms). The NanoSIMS incorporates an array of detectors, enabling simultaneous collection of 5 elements or isotopes originating from the same sputtered volume of a sample. The primary ion beam (Cs{sup +} or O{sup -}) can be scanned across the sample to produce quantitative secondary ion images. This capability for multiple isotope imaging with high spatial resolution is unique to the NanoSIMS and provides a novel new approach to the study of the distribution of elements in biological materials. We have applied this technique extensively to mammalian cells and bacterial spores. Results from these studies and critical analytical issues such as sample preparation, instrument tuning, and data processing will be discussed.

  2. Center Information Scintillation Materials Research Center

    E-Print Network [OSTI]

    Wang, Xiaorui "Ray"

    to be a critical need in medical imaging systems, homeland security inspection and monitoring systems, neutron the next generation of gamma-ray, x-ray, and neutron detectors. New radiation detectors continue radiation such as gamma rays, X-rays, or neutrons and convert that energy into short bursts of visible

  3. Using mobile phone contextual information to facilitate managing image collections

    E-Print Network [OSTI]

    -awareness, metadata, image collections, mobile phones, embedded sensors INTRODUCTION Personal information management@gmail.com ABSTRACT In this paper, we describe a prototype application that utilizes the embedded sensors in advanced information management. We hypothesize that information inferred from embedded mobile phone sensors can offer

  4. Informational Materials | Y-12 National Security Complex

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room NewsInformation CurrentHenry Bellamy,ImpactScientificInfluence ofMedia on LightingInformational

  5. Template:ReferenceMaterial | 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Information Serbia-EnhancingEt Al., 2013) | Opensource History View New PagesTemplate

  6. Serious Materials (Colorado) | Open Energy Information

    Open Energy Info (EERE)

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  7. Micro Materials Japan Inc | Open Energy Information

    Open Energy Info (EERE)

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  8. Mitsubishi Materials Corp | Open Energy Information

    Open Energy Info (EERE)

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  9. Advanced Materials Partners 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION JEnvironmental Jump to:EAand DaltonSolar Energy LLCAdema Technologies IncFuel CellMaterials

  10. 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX ECoopButtePowerEdisto Electric Coop, IncsourceEnginuityBusinessEnviva Materials LLC Jump

  11. Category:Reference 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX ECoopButte County,Camilla, Georgia:GeothermalNEPAReference Materials (Redirected from

  12. 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

    2004-11-01

    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.

  13. Topsil Semiconductor Materials AS | Open Energy Information

    Open Energy Info (EERE)

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  14. Adaptive Materials Inc | Open Energy Information

    Open Energy Info (EERE)

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  15. 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)

    2003-01-01

    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.

  16. 2015 Advanced Imaging Application 1. BIOGRAPHICAL INFORMATION

    E-Print Network [OSTI]

    Barrash, Warren

    to the selection committee. 8. TECHNICAL STANDARDS Read the following statements identifying the TECHNICAL stressful situations related to technical and procedural standards and patient care situations; 2. Providing STANDARDS appropriate to medical imaging and answer the inquiry provided below. The technologist

  17. Charting Image Artifacts in Digital Image Sequences Using Velocital Information Content

    E-Print Network [OSTI]

    Ahmed, Farid

    algorithm. The transmission algorithm is based on the discrete cosine transform (DCT), and thus introduces is currently marketed to the consumer as higher quality. However, digital image sequences still incur image artifacts, and spurious noise. Other author information: (Send correspondence to G.J.Power) G .J .Power: E

  18. Supplemental Material: Analyzing angular distributions for two-step dissociation mechanisms in velocity map imaging

    E-Print Network [OSTI]

    Butler, Laurie J.

    Supplemental Material: Analyzing angular distributions for two-step dissociation mechanisms in velocity map imaging D. B. Straus,* L. M. Butler, B. W. Alligood,* and L. J

  19. Using phase information to enhance speckle noise reduction in the ultrasonic NDE of coarse grain materials

    SciTech Connect (OSTI)

    Lardner, Timothy; Gachagan, Anthony; Li, Minghui

    2014-02-18

    Materials with a coarse grain structure are becoming increasingly prevalent in industry due to their resilience to stress and corrosion. These materials are difficult to inspect with ultrasound because reflections from the grains lead to high noise levels which hinder the echoes of interest. Spatially Averaged Sub-Aperture Correlation Imaging (SASACI) is an advanced array beamforming technique that uses the cross-correlation between images from array sub-apertures to generate an image weighting matrix, in order to reduce noise levels. This paper presents a method inspired by SASACI to further improve imaging using phase information to refine focusing and reduce noise. A-scans from adjacent array elements are cross-correlated using both signal amplitude and phase to refine delay laws and minimize phase aberration. The phase-based and amplitude-based corrected images are used as inputs to a two-dimensional cross-correlation algorithm that will output a weighting matrix that can be applied to any conventional image. This approach was validated experimentally using a 5MHz array a coarse grained Inconel 625 step wedge, and compared to the Total Focusing Method (TFM). Initial results have seen SNR improvements of over 20dB compared to TFM, and a resolution that is much higher.

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

    SciTech Connect (OSTI)

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

    2011-01-01

    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.

  1. Help:Linked images | Open Energy Information

    Open Energy Info (EERE)

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  2. information in the form of images,

    E-Print Network [OSTI]

    Chang, Shih-Fu

    the documents, and create compact and searchable indexes. Users enter query terms and/or select subjects to more search engines. WebSeek provides a complete system that collects visual information from the Web by automated agents, then catalogs and indexes it for fast search and retrieval. M any search engines index

  3. Multibounce light transport analysis using ultrafast imaging for material acquisition

    E-Print Network [OSTI]

    Naik, Nikhil, S.M. Massachusetts Institute of Technology

    2012-01-01

    This thesis introduces a novel framework for analysis of multibounce light transport using time-of-flight imaging for the applications of ultrafast reflectance acquisition and imaging through scattering media. Using ultrafast ...

  4. Thermal wave image processing for characterization of subsurface of flaws in materials

    SciTech Connect (OSTI)

    Gopalan, K.; Gopalsami, N.

    1993-08-01

    Infrared images resulting from back-scattered thermal waves in composite materials are corrupted by instrument noise and sample heat-spread function. This paper demonstrates that homomorphic deconvolution and {open_quotes}demultiplication{close_quotes} result in enhanced image quality for characterization of subsurface flaws in Kevlar and graphics composites. The choice of processing depends on the material characteristics and the extent of noise in the original image.

  5. Multispectral Imaging (Laney, 2005) | 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo, Maryland: EnergyInformationOliver, Pennsylvania:(CTI PFAN) |Geothermal SystemsLaney,

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

    E-Print Network [OSTI]

    O'Shea, James Patrick

    2012-01-01

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

  7. Chinese Optical Character Recognition for Information Extraction from Video Images

    E-Print Network [OSTI]

    Lyu, Michael R.

    Chinese Optical Character Recognition for Information Extraction from Video Images Wing Hang Cheung The Chinese University of Hong Kong Shatin, N.T., Hong Kong Abstract A number of research work on text ex- traction from videos is conducted in these few years, but not many focus on the Chinese language. Due to di

  8. Shandong Linuo New Material Corp | Open Energy Information

    Open Energy Info (EERE)

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

    Open Energy Info (EERE)

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

    DOE Patents [OSTI]

    Bunch, Kyle J.; McMakin, Douglas L.

    2012-09-04

    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.

  11. UNIVERSITY OF CALIFORNIA, SAN DIEGO Towards Realistic Image Synthesis of Scattering Materials

    E-Print Network [OSTI]

    Kazhdan, Michael

    UNIVERSITY OF CALIFORNIA, SAN DIEGO Towards Realistic Image Synthesis of Scattering Materials, and it is acceptable in quality and form for publication on microfilm: Chair University of California, San Diego 2006 Radiative Transport . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.1 The Radiative Transport

  12. 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)

    1999-01-01

    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.

  13. Image Mining in IRIS: Integrated Retinal Information System Wynne Hsu, Mong Li Lee, Kheng Guan Goh

    E-Print Network [OSTI]

    Lee, Mong Li "Janice"

    , applies state-of- the-art data mining and image processing techniques on the retinal images to aid in the screening process. 2. Image Classification An abnormal retinal image has one or more of the following signs1 Image Mining in IRIS: Integrated Retinal Information System Wynne Hsu, Mong Li Lee, Kheng Guan

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

    E-Print Network [OSTI]

    Fessler, Jeffrey A.

    propose a penalized- likelihood (PL) method with edge-preserving regularizers for each material image reconstruction I. INTRODUCTION Dual-energy (DE) CT reconstruction methods typically re- construct. It obtains a dual-material-density pair through projection-based decomposition approach from DECT

  15. 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 model can be applied to any material combination and any number of energy bins. Figure: Top row Carlo model is presented to evaluate the clinical benefits of optimal energy bins in spectral X

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

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

    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 hypersonic flights...

  17. 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

    2009-07-04

    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.

  18. 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

    2012-01-01

    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.

  19. X-ray backscatter imaging of nuclear materials

    DOE Patents [OSTI]

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

    2014-09-30

    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.

  20. 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 .............................................................................................................................................. 2 Review of Web Resources

  1. 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

    the entire supply chain. This includes raw material suppliers, component producers, final assemblySupply 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

  2. New Microscopy the Imaging of Quantum Materials David C. Bell, Estelle Kalfon-Cohen and Robert M. Westervelt

    E-Print Network [OSTI]

    Walsworth, Ronald L.

    New Microscopy the Imaging of Quantum Materials David C. Bell, Estelle Kalfon-Cohen and Robert M of extraordinary new quantum materials with striking properties has caused great excitement, and promises electronic states [3]. A major thrust of this research has been to discover new ways to image these materials

  3. 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)

    2010-03-30

    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.

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

    SciTech Connect (OSTI)

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

    2015-01-01

    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.

  5. Zhongsheng Technology New Materials Ltd | Open Energy Information

    Open Energy Info (EERE)

    Technology New Materials Ltd Jump to: navigation, search Name: Zhongsheng Technology New Materials Ltd Place: Bayannaoer, Inner Mongolia Autonomous Region, China Product: Maker of...

  6. Master Thesis: 3-D Imaging of Locust Exoskeletons Department of New Materials and Biosystems / Perceiving Systems / Optics & Sensing Laboratory

    E-Print Network [OSTI]

    with the Department of New Materials and Biosystems. You will gain interdisciplinary interesting candidate for the design of new bio-inspired composite materials Master Thesis: 3-D Imaging of Locust Exoskeletons Department of New

  7. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room NewsInformationJesse BergkampCentermillion toMSDS onBudgetMaterialMaterials Materials Access to

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

    E-Print Network [OSTI]

    Schmidt, Volker

    Image-based stochastic modeling of the 3D morphology of energy materials on various length scales tomography image data O. Stenzel et al., Modelling and Simulation in Materials Science and Engineering microstructure of compressed graphite electrodes 3D morphology of hybrid organic solar cells Charge transport

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

    E-Print Network [OSTI]

    Aparicio, German Walter, Jr

    2010-01-01

    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 ...

  10. Phase-contrast imaging using ultrafast x-rays in laser-shocked materials

    SciTech Connect (OSTI)

    Workman, Jonathan B; Cobble, James A; Flippo, Kirk; Gautier, Donald C; Montgomery, David S; Offermann, Dustin T

    2010-01-01

    High-energy x-rays, > 10-keV, can be efficiently produced from ultrafast laser target interactions with many applications to dense target materials in Inertial Confinement Fusion (ICF) and High-Energy Density Physics (HEDP). These same x-rays can also be applied to measurements of low-density materials inside high-density hohlraum environments. In the experiments presented, high-energy x-ray images of laser-shocked polystyrene are produced through phase contrast imaging. The plastic targets are nominally transparent to traditional x-ray absorption but show detailed features in regions of high density gradients due to refractive effects often called phase contrast imaging. The 200-TW Trident laser is used both to produce the x-ray source and to shock the polystyrene target. X-rays at 17-keV produced from 2-ps, 100-J laser interactions with a 12-micron molybdenum wire are used to produce a small source size, required for optimizing refractive effects. Shocks are driven in the 1-mm thick polystyrene target using 2-ns, 250-J, 532-nm laser drive with phase plates. X-ray images of shocks compare well to 1-D hydro calculations, HELIOS-CR.

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

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    solely for information and program planning purposes; this RFI does not constitute a formal solicitation for proposals or abstracts. Your response to this notice will be treated...

  12. Imaging systems and methods for obtaining and using biometric information

    DOE Patents [OSTI]

    McMakin, Douglas L [Richland, WA; Kennedy, Mike O [Richland, WA

    2010-11-30

    Disclosed herein are exemplary embodiments of imaging systems and methods of using such systems. In one exemplary embodiment, one or more direct images of the body of a clothed subject are received, and a motion signature is determined from the one or more images. In this embodiment, the one or more images show movement of the body of the subject over time, and the motion signature is associated with the movement of the subject's body. In certain implementations, the subject can be identified based at least in part on the motion signature. Imaging systems for performing any of the disclosed methods are also disclosed herein. Furthermore, the disclosed imaging, rendering, and analysis methods can be implemented, at least in part, as one or more computer-readable media comprising computer-executable instructions for causing a computer to perform the respective methods.

  13. MSE 157: Quantum Mechanics of Nanoscale Materials Course Information

    E-Print Network [OSTI]

    and lasers, to solar cells and batteries and other energy- related materials problems, to broad aspects will make use more advanced concepts like partial differential equations, linear algebra, etc

  14. Information for Using Luciferin in Bioluminescent Imaging April 7, 2010

    E-Print Network [OSTI]

    Sabatini, David M.

    background levels by one hour post luciferin administration. Product information: Pricing information. Because the reaction is dependent on ATP, it allows researchers to determine the presence of energy

  15. Imaging the early material response associated with exit surface damage in fused silica

    SciTech Connect (OSTI)

    Demos, S G; Raman, R N; Negres, R A

    2010-11-05

    The processes involved at the onset of damage initiation on the surface of fused silica have been a topic of extensive discussion and thought for more than four decades. Limited experimental results have helped develop models covering specific aspects of the process. In this work we present the results of an experimental study aiming at imaging the material response from the onset of the observation of material modification during exposure to the laser pulse through the time point at which material ejection begins. The system involves damage initiation using a 355 nm pulse, 7.8 ns FWHM in duration and imaging of the affected material volume with spatial resolution on the order of 1 {micro}m using as strobe light a 150 ps laser pulse that is appropriately timed with respect to the pump pulse. The observations reveal that the onset of material modification is associated with regions of increased absorption, i.e., formation of an electronic excitation, leading to a reduction in the probe transmission to only a few percent within a time interval of about 1 ns. This area is subsequently rapidly expanding with a speed of about 1.2 {micro}m/ns and is accompanied by the formation and propagation of radial cracks. These cracks appear to initiate about 2 ns after the start of the expansion of the modified region. The damage sites continue to grow for about 25 ns but the mechanism of expansion after the termination of the laser pulse is via formation and propagation of lateral cracks. During this time, the affected area of the surface appears to expand forming a bulge of about 40 {micro}m in height. The first clear observation of material cluster ejection is noted at about 50 ns delay.

  16. Materials Science and Engineering A318 (2001) 114 Recovery of information on the microstructure of

    E-Print Network [OSTI]

    Sevostianov, Igor

    2001-01-01

    Materials Science and Engineering A318 (2001) 1­14 Recovery of information on the microstructure February 2001; received in revised form 11 June 2001 Abstract Recovery of microstructural information from/cracked microstructures that are, generally, anisotropic. The extent of uncertainty in such information recovery

  17. MASS ESTIMATES OF RAPIDLY MOVING PROMINENCE MATERIAL FROM HIGH-CADENCE EUV IMAGES

    SciTech Connect (OSTI)

    Williams, David R.; Baker, Deborah; Van Driel-Gesztelyi, Lidia

    2013-02-20

    We present a new method for determining the column density of erupting filament material using state-of-the-art multi-wavelength imaging data. Much of the prior work on filament/prominence structure can be divided between studies that use a polychromatic approach with targeted campaign observations and those that use synoptic observations, frequently in only one or two wavelengths. The superior time resolution, sensitivity, and near-synchronicity of data from the Solar Dynamics Observatory's Advanced Imaging Assembly allow us to combine these two techniques using photoionization continuum opacity to determine the spatial distribution of hydrogen in filament material. We apply the combined techniques to SDO/AIA observations of a filament that erupted during the spectacular coronal mass ejection on 2011 June 7. The resulting 'polychromatic opacity imaging' method offers a powerful way to track partially ionized gas as it erupts through the solar atmosphere on a regular basis, without the need for coordinated observations, thereby readily offering regular, realistic mass-distribution estimates for models of these erupting structures.

  18. 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)

    2000-01-01

    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.

  19. Difference image analysis: Automatic kernel design using information criteria

    E-Print Network [OSTI]

    Bramich, D M; Alsubai, K A; Bachelet, E; Mislis, D; Parley, N

    2015-01-01

    We present a selection of methods for automatically constructing an optimal kernel model for difference image analysis which require very few external parameters to control the kernel design. Each method consists of two components; namely, a kernel design algorithm to generate a set of candidate kernel models, and a model selection criterion to select the simplest kernel model from the candidate models that provides a sufficiently good fit to the target image. We restricted our attention to the case of solving for a spatially-invariant convolution kernel composed of delta basis functions, and we considered 19 different kernel solution methods including six employing kernel regularisation. We tested these kernel solution methods by performing a comprehensive set of image simulations and investigating how their performance in terms of model error, fit quality, and photometric accuracy depends on the properties of the reference and target images. We find that the irregular kernel design algorithm employing unreg...

  20. Information-Efficient Spectral Imaging Sensor With Tdi

    DOE Patents [OSTI]

    Rienstra, Jeffrey L. (Albuquerque, NM); Gentry, Stephen M. (Albuquerque, NM); Sweatt, William C. (Albuquerque, NM)

    2004-01-13

    A programmable optical filter for use in multispectral and hyperspectral imaging employing variable gain time delay and integrate arrays. A telescope focuses an image of a scene onto at least one TDI array that is covered by a multispectral filter that passes separate bandwidths of light onto the rows in the TDI array. The variable gain feature of the TDI array allows individual rows of pixels to be attenuated individually. The attenuations are functions of the magnitudes of the positive and negative components of a 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. This system provides for a very efficient determination of the presence of the target, as opposed to the very data intensive data manipulations that are required in conventional hyperspectral imaging systems.

  1. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room NewsInformationJesse BergkampCentermillion toMSDS onBudgetMaterial

  2. Waste and Materials Disposition Information | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on Delicious Rank EERE: Alternative FuelsofProgram: Report1538-1950Department ofIntroductionDepartmentWaste and Materials

  3. Microscale electromagnetic heating in heterogeneous energetic materials based on X-ray CT imaging

    E-Print Network [OSTI]

    Kort-Kamp, W J M; Ionita, A; Glover, B B; Duque, A L Higginbotham; Perry, W L; Patterson, B M; Dalvit, D A R; Moore, D S

    2015-01-01

    Electromagnetic stimulation of energetic materials provides a noninvasive and nondestructive tool for detecting and identifying explosives. We combine structural information based on X-ray computed tomography, experimental dielectric data, and electromagnetic full-wave simulations, to study microscale electromagnetic heating of realistic three-dimensional heterogeneous explosives. We analyze the formation of electromagnetic hot spots and thermal gradients in the explosive-binder meso-structures, and compare the heating rate for various binder systems.

  4. 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

    2011-02-17

    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.

  5. Identification and summary characterization of materials potentially requiring vitrification: Background information

    SciTech Connect (OSTI)

    Croff, A.G.

    1996-05-13

    This document contains background information for the Workshop in general and the presentation entitled `Identification and Summary Characterization of Materials Potentially Requiring Vitrification` that was given during the first morning of the workshop. summary characteristics of 9 categories of US materials having some potential to be vitrified are given. This is followed by a 1-2 page elaborations for each of these 9 categories. References to more detailed information are included.

  6. 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page onRAPID/Geothermal/Exploration/ColoradoRemsenburg-Speonk,SageScheucoSedcoInformation Twenty-Nine

  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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTIONRobertsdale, AlabamaETEC GmbH Jump to:Providence,New Mexico: Energy ResourcesMaterial Technology

  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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX E LISTStar2-0057-EAInvervar Hydro JumpHuari Silicon Material Co Ltd Jump to: navigation,

  9. Jinzhou Rixin 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX E LISTStar2-0057-EAInvervar Hydro JumpHuari Silicon Material Co Ltd Jump

  10. SiXtron Advanced Materials 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX ECoop Inc JumpHeter Battery Technology Co LtdOhio:ZhangjiaduBhavani PowerShurjoMaterials

  11. SiXtron Advanced 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION JEnvironmental Jump to:EA EIS ReportEurope GmbH Jump to: navigation,Show Me EthanolMaterials Jump

  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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History View New PagesSustainableGlynn County,Solar JumpInformationGrowindFunan Bioenergy Co

  13. Inner Mongolia Zhonghuan PV 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX E LISTStar2-0057-EA JumpDuimenMaking EnergyIndosolar LtdEnergyInformationYitai

  14. Tokuyama Dowa Power 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX ECoop Inc JumpHeterInformation PolicyTinna Group Jump to:Tokuyama Corporation

  15. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefieldSulfateSciTechtail.Theory of raregovAboutRecoveryplanning CareerNationalCNMS USERLOCAL INFORMATION AND

  16. 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIXsource HistoryScenariosMarysville Mt Geothermal AreaInformationsourceMassena,and

  17. New radiological material detection technologies for nuclear forensics: Remote optical imaging and graphene-based sensors.

    SciTech Connect (OSTI)

    Harrison, Richard Karl; Martin, Jeffrey B.; Wiemann, Dora K.; Choi, Junoh; Howell, Stephen W.

    2015-09-01

    We developed new detector technologies to identify the presence of radioactive materials for nuclear forensics applications. First, we investigated an optical radiation detection technique based on imaging nitrogen fluorescence excited by ionizing radiation. We demonstrated optical detection in air under indoor and outdoor conditions for alpha particles and gamma radiation at distances up to 75 meters. We also contributed to the development of next generation systems and concepts that could enable remote detection at distances greater than 1 km, and originated a concept that could enable daytime operation of the technique. A second area of research was the development of room-temperature graphene-based sensors for radiation detection and measurement. In this project, we observed tunable optical and charged particle detection, and developed improved devices. With further development, the advancements described in this report could enable new capabilities for nuclear forensics applications.

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

    SciTech Connect (OSTI)

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

    2005-10-03

    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.

  19. High Resolution Imaging Science Experiment | 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIXsource History View NewGuam: Energyarea, CaliforniaHess RetailResolution Imaging Science

  20. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room NewsInformation CurrentHenry Bellamy, Ph.D.FoodHydropower,PrincipalIdahoImaging Print The

  1. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room NewsInformation CurrentHenry Bellamy, Ph.D.FoodHydropower,PrincipalIdahoImaging Print

  2. 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.

    1980-07-31

    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.

  3. Spatial resolution, signal-to-noise and information capacity of linear imaging systems

    E-Print Network [OSTI]

    Gureyev, Timur

    2015-01-01

    A simple model for image formation in linear shift-invariant systems is considered, in which both the detected signal and the noise variance are almost constant over distances comparable with the width of the point-spread function of the system. It is shown that within the constraints of this model, the square of the signal-to-noise ratio is always proportional to the "volume" of the spatial resolution unit. The ratio of these two quantities divided by the incident density of the imaging particles (e.g. photons) represents a dimensionless invariant of the imaging system, which was previously termed the intrinsic imaging quality. This invariant is related to the notion of information capacity of imaging and communication systems as previously considered by Shannon, Gabor and others. It is demonstrated that the information capacity expressed in bits cannot exceed the total number of imaging particles utilised in the system. These results are then applied to a simple generic model of quantitative imaging and ana...

  4. Template:SpectralImagingSensor | 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Information Serbia-EnhancingEt Al., 2013) | Opensource History View New PagesTemplatesourcesource

  5. Multispectral Imaging (Lewicki & Oldenburg, 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo, Maryland: EnergyInformationOliver, Pennsylvania:(CTI PFAN) |Geothermal

  6. Multispectral Imaging At Alum Area (DOE GTP) | 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo, Maryland: EnergyInformationOliver, Pennsylvania:(CTI PFAN)

  7. OpenEI:Projects/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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo, Maryland:NPI VenturesNewSt.Information

  8. DSI Dipole Shear Sonic Imager | 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX ECoopButtePower VenturesInformation9) Wind

  9. Chaotic Crystallography: How the physics of information reveals structural order in materials

    E-Print Network [OSTI]

    Dowman P. Varn; James P. Crutchfield

    2014-11-10

    We review recent progress in applying information- and computation-theoretic measures to describe material structure that transcends previous methods based on exact geometric symmetries. We discuss the necessary theoretical background for this new toolset and show how the new techniques detect and describe novel material properties. We discuss how the approach relates to well known crystallographic practice and examine how it provides novel interpretations of familiar structures. Throughout, we concentrate on disordered materials that, while important, have received less attention both theoretically and experimentally than those with either periodic or aperiodic order.

  10. Multispectral Imaging At Maui Area (DOE GTP) | 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History ViewMayo, Maryland: EnergyInformationOliver, Pennsylvania:(CTI PFAN)Open EnergyOpenAl., 2001)

  11. Discover the Benefits of Radar 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX ECoopButtePower VenturesInformation9)ask queriesWind FarmArea

  12. Federal Automated Information System of Nuclear Material Control and Accounting: Uniform System of Reporting Documents

    SciTech Connect (OSTI)

    Pitel, M V; Kasumova, L; Babcock, R A; Heinberg, C

    2003-06-12

    One of the fundamental regulations of the Russian State System for Nuclear Material Accounting and Control (SSAC), ''Basic Nuclear Material Control and Accounting Rules,'' directed that a uniform report system be developed to support the operation of the SSAC. According to the ''Regulation on State Nuclear Material Control and Accounting,'' adopted by the Russian Federation Government, Minatom of Russia is response for the development and adoption of report forms, as well as the reporting procedure and schedule. The report forms are being developed in tandem with the creation of an automated national nuclear material control and accounting system, the Federal Information System (FIS). The forms are in different stages of development and implementation. The first report forms (the Summarized Inventory Listing (SIL), Summarized Inventory Change Report (SICR) and federal and agency registers of nuclear material) have already been created and implemented. The second set of reports (nuclear material movement reports and the special anomaly report) is currently in development. A third set of reports (reports on import/export operations, and foreign nuclear material temporarily located in the Russian Federation) is still in the conceptual stage. To facilitate the development of a unified document system, the FIS must establish a uniform philosophy for the reporting system and determine the requirements for each reporting level, adhering to the following principles: completeness--the unified report system provides the entire range of information that the FIS requires to perform SSAC tasks; requisite level of detail; hierarchical structure--each report is based on the information provided in a lower-level report and is the source of information for reports at the next highest level; consistency checking--reports can be checked against other reports. A similar philosophy should eliminate redundancy in the different reports, support a uniform approach to the contents of previously developed and new reports within the FIS, as well as identify the main priorities for the direction of the FIS.

  13. Neural Networks and Information in Materials Science H. K. D. H. Bhadeshia*

    E-Print Network [OSTI]

    Cambridge, University of

    REVIEW Neural Networks and Information in Materials Science H. K. D. H. Bhadeshia* Department in Wiley InterScience (www.interscience.wiley.com). Abstract: Neural networks have pervaded all aspects 1. INTRODUCTION Neural networks are wonderful tools, which permit the development of quantitative

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

    SciTech Connect (OSTI)

    Willem, Henry; Singer, Brett

    2010-09-15

    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.

  15. Quality control of complex polymer materials using hyperspectral imaging associated with multivariate

    E-Print Network [OSTI]

    with multivariate statistical analysis Thèse Massoud Ghasemzadeh-Barvarz Doctorat en génie chimique Philosophiae Squares (PLS), Multivariate Curve Resolution (MCR) and Multivariate Image Analysis/Multivariate Image and Multivariate Image Analysis (MIA). The potential and effectiveness of the proposed method for detecting defects

  16. Time-Resolved Imaging of Material Response Following Laser-Induced Breakdown in the Bulk and Surface of Fused Silica

    SciTech Connect (OSTI)

    Raman, R N; Negres, R A; DeMange, P; Demos, S G

    2010-02-04

    Optical components within high energy laser systems are susceptible to laser-induced material modification when the breakdown threshold is exceeded or damage is initiated by pre-existing impurities or defects. These modifications are the result of exposure to extreme conditions involving the generation of high temperatures and pressures and occur on a volumetric scale of the order of a few cubic microns. The response of the material following localized energy deposition, including the timeline of events and the individual processes involved during this timeline, is still largely unknown. In this work, we investigate the events taking place during the entire timeline in both bulk and surface damage in fused silica using a set of time-resolved microscopy systems. These microscope systems offer up to 1 micron spatial resolution when imaging static or dynamic effects, allowing for imaging of the entire process with adequate temporal and spatial resolution. These systems incorporate various pump-probe geometries designed to optimize the sensitivity for detecting individual aspects of the process such as the propagation of shock waves, near-surface material motion, the speed of ejecta, and material transformations. The experimental results indicate that the material response can be separated into distinct phases, some terminating within a few tens of nanoseconds but some extending up to about 100 microseconds. Overall the results demonstrate that the final characteristics of the modified region depend on the material response to the energy deposition and not on the laser parameters.

  17. Materials Science & Metallurgy Master of Philosophy, Materials Modelling, More information on http://www.msm.cam.ac.uk/phase-trans/2005/ODS.html

    E-Print Network [OSTI]

    Cambridge, University of

    Materials Science & Metallurgy Master of Philosophy, Materials Modelling, More information on http://www.msm.cam.ac.uk/phase-trans/2005/ODS.html Course MP4, Thermodynamics and Phase Diagrams, H. K. D. H. Bhadeshia Lecture 4 about a thousand atoms block. The atom probe technique collects the experimental data on an atom by atom

  18. Video Toroid Cavity Imager

    DOE Patents [OSTI]

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

    2004-08-10

    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.

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

    SciTech Connect (OSTI)

    NONE

    1995-07-14

    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.

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

    SciTech Connect (OSTI)

    Wang, Qiang; Hou, Lingyun; Zhang, Guixin Zhang, Boya; Liu, Cheng; Wang, Zhi; Huang, Jian

    2014-02-17

    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.

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

    E-Print Network [OSTI]

    Yang, Judy

    2012-01-01

    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

  2. Real-time drill monitoring and control using building information models augmented with 3D imaging data

    E-Print Network [OSTI]

    Kamat, Vineet R.

    Real-time drill monitoring and control using building information models augmented with 3D imaging and incorporating point cloud data obtained from 3D imaging technologies1 into the drilling process in was de bridge deck. Once the point clouds were processed, zones which are safe for drilling were automatically

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

    SciTech Connect (OSTI)

    Sutter, S. L.

    1982-05-01

    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.

  4. Stereological techniques for synthesizing solid textures from images of aggregate materials

    E-Print Network [OSTI]

    Jagnow, Robert Carl, 1976-

    2005-01-01

    When creating photorealistic digital scenes, textures are commonly used to depict complex variation in surface appearance. For materials that have spatial variation in three dimensions, such as wood or marble, solid textures ...

  5. Extraction of Vectorized Graphical Information from Scientific Chart Images Ruizhe Liu, Weihua Huang and Chew Lim Tan

    E-Print Network [OSTI]

    Tan, Chew Lim

    types, such as bar chart, pie chart and line chart etc. In recent years, several works have beenExtraction of Vectorized Graphical Information from Scientific Chart Images Ruizhe Liu, Weihua}@comp.nus.edu.sg Abstract Graphical components information extraction is a crucial step in the chart recognition

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

    DOE Patents [OSTI]

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

    2003-01-01

    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.

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

    DOE Patents [OSTI]

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

    2013-03-05

    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.

  8. 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.

    2006-08-13

    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.

  9. DETECTION OF WIDESPREAD HYDRATED MATERIALS ON VESTA BY THE VIR IMAGING SPECTROMETER ON BOARD THE DAWN MISSION

    SciTech Connect (OSTI)

    De Sanctis, M. C.; Ammannito, E.; Palomba, E.; Longobardo, A.; Capaccioni, F.; Capria, M. T.; Tosi, F.; Zambon, F.; Carraro, F.; Fonte, S.; Frigeri, A.; Magni, G.; Combe, J.-Ph.; McCord, T. B.; Marchi, S.; Mittlefehldt, D. W.; Pieters, C. M.; Sunshine, J.; Raymond, C. A.; Russell, C. T.; and others

    2012-10-20

    Water plays a key role in the evolution of terrestrial planets, and notably in the occurrence of Earth's oceans. However, the mechanism by which water has been incorporated into these bodies-including Earth-is still extensively debated. Here we report the detection of widespread 2.8 {mu}m OH absorption bands on the surface of the asteroid Vesta by the VIR imaging spectrometer on board Dawn. These observations are surprising as Vesta is fully differentiated with a basaltic surface. The 2.8 {mu}m OH absorption is distributed across Vesta's surface and shows areas enriched and depleted in hydrated materials. The uneven distribution of hydrated mineral phases is unexpected and indicates ancient processes that differ from those believed to be responsible for OH on other airless bodies, like the Moon. The origin of Vestan OH provides new insight into the delivery of hydrous materials in the main belt and may offer new scenarios on the delivery of hydrous minerals in the inner solar system, suggesting processes that may have played a role in the formation of terrestrial planets.

  10. The effects of mapping CT images to Monte Carlo materials on GEANT4 proton simulation accuracy

    SciTech Connect (OSTI)

    Barnes, Samuel; McAuley, Grant; Slater, James; Wroe, Andrew

    2013-04-15

    Purpose: Monte Carlo simulations of radiation therapy require conversion from Hounsfield units (HU) in CT images to an exact tissue composition and density. The number of discrete densities (or density bins) used in this mapping affects the simulation accuracy, execution time, and memory usage in GEANT4 and other Monte Carlo code. The relationship between the number of density bins and CT noise was examined in general for all simulations that use HU conversion to density. Additionally, the effect of this on simulation accuracy was examined for proton radiation. Methods: Relative uncertainty from CT noise was compared with uncertainty from density binning to determine an upper limit on the number of density bins required in the presence of CT noise. Error propagation analysis was also performed on continuously slowing down approximation range calculations to determine the proton range uncertainty caused by density binning. These results were verified with Monte Carlo simulations. Results: In the presence of even modest CT noise (5 HU or 0.5%) 450 density bins were found to only cause a 5% increase in the density uncertainty (i.e., 95% of density uncertainty from CT noise, 5% from binning). Larger numbers of density bins are not required as CT noise will prevent increased density accuracy; this applies across all types of Monte Carlo simulations. Examining uncertainty in proton range, only 127 density bins are required for a proton range error of <0.1 mm in most tissue and <0.5 mm in low density tissue (e.g., lung). Conclusions: By considering CT noise and actual range uncertainty, the number of required density bins can be restricted to a very modest 127 depending on the application. Reducing the number of density bins provides large memory and execution time savings in GEANT4 and other Monte Carlo packages.

  11. Three-dimensional multimodal imaging and analysis of biphasic microstructure in a Ti-Ni-Sn thermoelectric material

    E-Print Network [OSTI]

    Douglas, Jason E; Echlin, McLean P; Lenthe, William C; Seshadri, Ram; Pollock, Tresa M

    2015-01-01

    microstructure in a Ti–Ni–Sn thermoelectric material Jasonsolid-state option. Thermoelectric materials, which convertin biphasic hH- based thermoelectric materials. This work

  12. IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 00, NO. 00, 2011 1 ISWLS: Novel Algorithm for Image

    E-Print Network [OSTI]

    Lambropoulou, Sofia

    IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 00, NO. 00, 2011 1 ISWLS: Novel Algorithm for Image Reconstruction in PET E. Karali, S. Pavlopoulos, S. Lambropoulou, and D. Koutsouris least- squares (WLS) acceleration of the reconstruction process. We used phantom data from a prototype

  13. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefieldSulfateSciTechtail.Theory ofDid you notHeat Pumps Heat Pumpsfacility doe logo CH2M-WG logoImaging

  14. Segmentation of UAV-based images incorporating 3D point cloud information A.Vetrivel*, M.Gerke, N.Kerle, G.Vosselman

    E-Print Network [OSTI]

    Segmentation of UAV-based images incorporating 3D point cloud information A.Vetrivel*, M.Gerke, N, Building detection, UAV images, 3D point cloud, Texture, Region growing ABSTRACT: Numerous applications characteristics jointly. 3D points generated from Unmanned Aerial Vehicle (UAV) images are used for inferring

  15. Disclaimer: The materials in this guide are provided for informational purposes only. The use of

    E-Print Network [OSTI]

    Sun, Yi

    ................................................................15 F. Part 6­Information for Criminal Records Check .................................................................3 a. Criminal Activity

  16. What happens to allochthonous material that falls into streams? A synthesis of new and published information

    E-Print Network [OSTI]

    Hutchens, John

    What happens to allochthonous material that falls into streams? A synthesis of new and published, Blacksburg, Virginia, U.S.A. SUMMARY 1. One of two things can happen to allochthonous material once it enters allochthonous material is used is the result of these two opposing factors: breakdown and transport. 2

  17. X-ray imaging of subsurface dynamics in high-Z materials at the Diamond Light Source

    SciTech Connect (OSTI)

    Eakins, D. E. Chapman, D. J.

    2014-12-15

    In this paper, we describe a new approach enabling study of subsurface dynamics in high-Z materials using the unique combination of high-energy synchrotron X-rays, a hybrid bunch structure, and a new dynamic loading platform. We detail the design and operation of the purpose-built, portable small bore gas-gun, which was installed on the I12 high-energy beamline at the Diamond Light Source and used to drive compression waves into solid and porous metal targets. Using a hybrid bunch structure and broadband X-ray pulses of up to 300 keV, radiographic snapshots were captured during various dynamic deformation processes in cm-scale specimens, thereby contributing to a more complete understanding of the evolution of mesoscale damage. Importantly, we highlight strategies for overcoming the challenges associated with using high-energy X-rays, and suggest areas for improvement needed to advance dynamic imaging through large-scale samples of relevance to engineering scenarios. These preliminary measurements demonstrate the feasibility of probing highly transient phenomena using the presented methodology.

  18. User'sManual All information contained in these materials, including products and product specifications,

    E-Print Network [OSTI]

    Bezrukov, Sergei

    is issued. Such information, however, is subject to change without any prior notice. Before purchasing or using any Renesas Electronics products listed herein, please confirm the latest product information. Renesas Electronics does not assume any liability for infringement of patents, copyrights, or other

  19. Method for deriving information regarding stress from a stressed ferromagnetic material

    DOE Patents [OSTI]

    Jiles, D.C.

    1991-04-30

    A nondestructive evaluation technique is disclosed for deriving stress in ferromagnetic materials including deriving anhysteretic and hysteresis magnetization curves for the material in both unstressed and stressed states. The anhysteretic curve is expressed as a Langevin function. The stress is expressed as an equivalent magnetic field dependent on stress and change of magnetostriction with magnetization. By measurement of these bulk magnetic properties, stress can be derived.

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

    E-Print Network [OSTI]

    Willem, Henry

    2010-01-01

    organic aerosols from ozone-initiated reactions with emissions from wood-based materials and a "green" paint.organic compounds (VOCs) and other air pollutants from the indoor application of water - based paints.Paints and coatings Wall finish and surfaces Acrylic urethane Glass fiber, resin, polystyrene, polyurethane, polyethylene foam, organic

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

    E-Print Network [OSTI]

    ® membranes were pre- treated by boiling them successively for 1 h each in a 4% H2O2 in de-ionized water, 1 M, MO, USA) in the highest available purity. Additional Details of Reactor Materials Preparation. Nafion H2SO4, and again in de-ionized water. Butyl rubber stoppers were used to prevent loss of gas from

  2. Materialized Views in Probabilistic Databases For Information Exchange and Query Optimization

    E-Print Network [OSTI]

    Thrun, Sebastian

    probabilistic data contain correlations between tuples, and the current approach is to capture these correla (BID) table. Not all views can be rep- resented as BID tables and so we propose a novel partial processing on probabilistic data, we can ignore the lineage and use materialized views to more efficiently

  3. WPN 09-6: Lead Safe Weatherization (LSW) Additional Materials and Information

    Broader source: Energy.gov [DOE]

    To provide clarification and additional information to grantees as they implement WPN 08-6, Interim Lead-Safe Weatherization Guidance. This guidance augments, but does not replace, WPN 08-6 and builds on the foundation provided in WPN 02-6, Weatherization Activities and Federal Lead Based Paint Regulations.

  4. Advanced Technology and Materials Co Ltd AT M | 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX E LIST OFAMERICA'S FUTURE.EnergyWoodenDateSA Jump to:Adaniand Materials Co Ltd AT M

  5. Approved Module Information for ME1F18, 2014/5 Module Title/Name: Materials & Processes Module Code: ME1F18

    E-Print Network [OSTI]

    Rebollo-Neira, Laura

    Approved Module Information for ME1F18, 2014/5 Module Title/Name: Materials & Processes Module Code: ME1F18 School: Engineering and Applied Science Module Type: Foundation New Module? No Module Credits: 10 Module Management Information Module Leader Name Lee Jenkins Email Address jenkinl1@aston

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

    SciTech Connect (OSTI)

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

    1995-06-01

    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.

  7. Image alignment

    DOE Patents [OSTI]

    Dowell, Larry Jonathan

    2014-04-22

    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.

  8. 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

    2013-07-16

    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.

  9. 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

  10. Practical image based lighting 

    E-Print Network [OSTI]

    Lee, Jaemin

    2003-01-01

    information is lighting. Image based lighting that is developed to recover illumination information of the real world from photographs has recently been popular in computer graphics. In this thesis we present a practical image based lighting method. Our...

  11. Three-dimensional imaging of director field orientations in liquid crystals by polarized four-wave mixing microscopy

    E-Print Network [OSTI]

    Lim, Sang-Hyun

    materials are described by the director field n r , and noninvasive three-dimensional 3D imaging that align homogeneously with LC molecules. Label- free 3D optical imaging of LCs has been demonstrated provide 3D structural information but the interpretation of a THG image is complicated due

  12. 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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefieldSulfateSciTechtail.Theory of rare Kaonforsupernovae2GatheringARMHistorygovCampaignsPropose a

  13. Edge-based correlation image registration for multispectral imaging

    DOE Patents [OSTI]

    Nandy, Prabal (Albuquerque, NM)

    2009-11-17

    Registration information for images of a common target obtained from a plurality of different spectral bands can be obtained by combining edge detection and phase correlation. The images are edge-filtered, and pairs of the edge-filtered images are then phase correlated to produce phase correlation images. The registration information can be determined based on these phase correlation images.

  14. The Department of Computer Information Systems is a leader in technology, setting the standard for IT education. The curriculum evolves continuously to ensure that your degree features the most useful and up-to-date material valued in the industry.

    E-Print Network [OSTI]

    The Department of Computer Information Systems is a leader in technology, setting the standard Technology (IT) professionals are responsible for helping information flow through organizations which-to-date material valued in the industry. What Do Computer Information Systems Professionals Do? Information

  15. 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

    2013-08-27

    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.

  16. Journal of the Korean Physical Society, Vol. 43, No. 4, October 2003, pp. 603606 Novel Photonic Materials for Advanced Imaging Applications

    E-Print Network [OSTI]

    Boyd, Robert W.

    FOR NEW PHOTONIC MATERIALS Some of the exciting potential applications of nonlin- ear optics include. Several strategies have traditionally been exploited in an attempt to cre- ate new materials with more the desirable characteristics of two or more constituent materials to create a new material with tailored

  17. Image registration method for medical image sequences

    DOE Patents [OSTI]

    Gee, Timothy F.; Goddard, James S.

    2013-03-26

    Image registration of low contrast image sequences is provided. In one aspect, a desired region of an image is automatically segmented and only the desired region is registered. Active contours and adaptive thresholding of intensity or edge information may be used to segment the desired regions. A transform function is defined to register the segmented region, and sub-pixel information may be determined using one or more interpolation methods.

  18. 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.

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

    SciTech Connect (OSTI)

    None

    2010-01-01

    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.

  20. 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

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

    SciTech Connect (OSTI)

    B. Bishop; et al

    2005-03-30

    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.

  2. An interactive ontology-driven information system for simulating background radiation and generating scenarios for testing special nuclear materials detection algorithms

    SciTech Connect (OSTI)

    Sorokine, Alexandre; Schlicher, Bob G; Ward, Richard C; Wright, Michael C; Kruse, Kara L

    2015-01-01

    This paper describes an original approach to generating scenarios for the purpose of testing the algorithms used to detect special nuclear materials (SNM) that incorporates the use of ontologies. Separating the signal of SNM from the background requires sophisticated algorithms. To assist in developing such algorithms, there is a need for scenarios that capture a very wide range of variables affecting the detection process, depending on the type of detector being used. To provide such a cpability, we developed an ontology-driven information system (ODIS) for generating scenarios that can be used in creating scenarios for testing of algorithms for SNM detection. The ontology-driven scenario generator (ODSG) is an ODIS based on information supplied by subject matter experts and other documentation. The details of the creation of the ontology, the development of the ontology-driven information system, and the design of the web user interface (UI) are presented along with specific examples of scenarios generated using the ODSG. We demonstrate that the paradigm behind the ODSG is capable of addressing the problem of semantic complexity at both the user and developer levels. Compared to traditional approaches, an ODIS provides benefits such as faithful representation of the users' domain conceptualization, simplified management of very large and semantically diverse datasets, and the ability to handle frequent changes to the application and the UI. The approach makes possible the generation of a much larger number of specific scenarios based on limited user-supplied information

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

    E-Print Network [OSTI]

    Payeur, Pierre

    055]@site.uottawa.ca Abstract - Vehicles are widely recognized as one the main sources of pollution in urban areas. In the context of the Kyoto protocol, means need to be identified to reduce pollution-time detection of moving vehicles approaching an intersection from sequences of color images. Efficient

  4. 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

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

    E-Print Network [OSTI]

    Lowe, Heather Ann

    2013-01-01

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

  6. Heart imaging method

    DOE Patents [OSTI]

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

    1991-01-01

    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.

  7. Imaging Scatterometry

    E-Print Network [OSTI]

    Madsen, Morten Hannibal

    2015-01-01

    We present an optical metrology system for characterization of topography of micro/nano-structures on a surface or embedded in a semi-transparent material. Based on the principles of scatterometry, where the intensity of scattered light is used as a 'fingerprint' to reconstruct a surface, this new imaging scatterometer can easily find areas of interest on the cm scale and measure multiple segments simultaneously. The imaging scatterometer measures structural features, such as height, width, and sidewall angle of a grating locally on few um2 areas with nm resolution. We demonstrate two imaging scatterometers, one built into an optical microscope and one in a split configuration. The two scatterometers are targeted characterization of mm2 and cm2 areas, respectively, and both setups are validated using nano-textured samples.

  8. Time encoded radiation imaging

    DOE Patents [OSTI]

    Marleau, Peter; Brubaker, Erik; Kiff, Scott

    2014-10-21

    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.

  9. Imaging using transport models for wave-wave correlations

    E-Print Network [OSTI]

    Biasutti, Michela

    exploration in geophysics [16] and non-destructive testing of materials [26]. One standard imaging technique

  10. 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)

    2006-07-11

    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.

  11. Vicinal light inspection of translucent materials

    DOE Patents [OSTI]

    Burns, Geroge R. (Albuquerque, NM); Yang, Pin (Albuquerque, NM)

    2010-01-19

    The present invention includes methods and apparatus for inspecting vicinally illuminated non-patterned areas of translucent materials. An initial image of the material is received. A second image is received following a relative translation between the material being inspected and a device generating the images. Each vicinally illuminated image includes a portion having optimal illumination, that can be extracted and stored in a composite image of the non-patterned area. The composite image includes aligned portions of the extracted image portions, and provides a composite having optimal illumination over a non-patterned area of the material to be inspected. The composite image can be processed by enhancement and object detection algorithms, to determine the presence of, and characterize any inhomogeneities present in the material.

  12. Fourier plane imaging microscopy

    SciTech Connect (OSTI)

    Dominguez, Daniel, E-mail: daniel.dominguez@ttu.edu; Peralta, Luis Grave de [Department of Physics, Texas Tech University, Lubbock, Texas 79409 (United States); Nano Tech Center, Texas Tech University, Lubbock, Texas 79409 (United States); Alharbi, Nouf; Alhusain, Mdhaoui [Department of Physics, Texas Tech University, Lubbock, Texas 79409 (United States); Bernussi, Ayrton A. [Nano Tech Center, Texas Tech University, Lubbock, Texas 79409 (United States); Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, Texas 79409 (United States)

    2014-09-14

    We show how the image of an unresolved photonic crystal can be reconstructed using a single Fourier plane (FP) image obtained with a second camera that was added to a traditional compound microscope. We discuss how Fourier plane imaging microscopy is an application of a remarkable property of the obtained FP images: they contain more information about the photonic crystals than the images recorded by the camera commonly placed at the real plane of the microscope. We argue that the experimental results support the hypothesis that surface waves, contributing to enhanced resolution abilities, were optically excited in the studied photonic crystals.

  13. 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIX ECoop IncIowaWisconsin: EnergyYork Jump| OpenExploration At TheWind Turbine Jump

  14. Imaging bolometer

    DOE Patents [OSTI]

    Wurden, Glen A. (Los Alamos, NM)

    1999-01-01

    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.

  15. Imaging bolometer

    DOE Patents [OSTI]

    Wurden, G.A.

    1999-01-19

    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.

  16. Imaging through scattering

    E-Print Network [OSTI]

    Satat, Guy

    2015-01-01

    In this thesis we demonstrate novel methods to overcome optical scattering in order to resolve information about hidden scenes, in particular for biomedical applications. Imaging through scattering media has long been a ...

  17. 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

  18. Material Misfits

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

    Issues submit Material Misfits How well nanocomposite materials align at their interfaces determines what properties they have, opening broad new avenues of materials-science...

  19. ANS materials databook

    SciTech Connect (OSTI)

    Marchbanks, M.F.

    1995-08-01

    Technical development in the Advanced Neutron Source (ANS) project is dynamic, and a continuously updated information source is necessary to provide readily usable materials data to the designer, analyst, and materials engineer. The Advanced Neutron Source Materials Databook (AMBK) is being developed as a part of the Advanced Neutron Source Materials Information System (AMIS). Its purpose is to provide urgently needed data on a quick-turnaround support basis for those design applications whose schedules demand immediate estimates of material properties. In addition to the need for quick materials information, there is a need for consistent application of data throughout the ANS Program, especially where only limited data exist. The AMBK is being developed to fill this need as well. It is the forerunner to the Advanced Neutron Source Materials Handbook (AMHB). The AMHB, as reviewed and approved by the ANS review process, will serve as a common authoritative source of materials data in support of the ANS Project. It will furnish documented evidence of the materials data used in the design and construction of the ANS system and will serve as a quality record during any review process whose objective is to establish the safety level of the ANS complex. The information in the AMBK and AMHB is also provided in electronic form in a dial-up computer database known as the ANS Materials Database (AMDB). A single consensus source of materials information prepared and used by all national program participants has several advantages. Overlapping requirements and data needs of various sub-projects and subcontractors can be met by a single document which is continuously revised. Preliminary and final safety analysis reports, stress analysis reports, equipment specifications, materials service reports, and many other project-related documents can be substantially reduced in size and scope by appropriate reference to a single data source.

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

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. Materials Data on WO2 (SG:166) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 VO2 (SG:227) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 UPS (SG:129) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 N2 (SG:194) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 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

  12. Materials Data on KSi (SG:218) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 VPt2 (SG:71) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Nd (SG:229) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 S (SG:221) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Pr (SG:8) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 VPO5 (SG:2) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Be (SG:229) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 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

  20. Materials Data on WS2 (SG:194) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 YS (SG:225) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 URh3 (SG:221) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 UBi (SG:225) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 UN (SG:225) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 UP (SG:225) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 CO2 (SG:136) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 KPb (SG:142) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 KSb2 (SG:12) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 VPO4 (SG:63) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 KHF2 (SG:140) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 KHSO4 (SG:61) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 H2 (SG:194) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 KPHNO2 (SG:148) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 HIO3 (SG:19) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 HN (SG:53) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 VO2 (SG:139) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-14

    Computed materials data using density functional theory calculations. These 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 KI (SG:221) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Yb (SG:225) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-14

    Computed materials data using density functional theory calculations. These 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 KNO2 (SG:8) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 KCN (SG:44) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 UF6 (SG:62) by Materials Project

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

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 WSCl4 (SG:2) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 YS2 (SG:227) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 VSO5 (SG:85) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 YUO4 (SG:123) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 YPb3 (SG:221) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 B (SG:166) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Fe (SG:194) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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

    2014-11-02

    Computed materials data using density functional theory calculations. These 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

    2014-11-02

    Computed materials data using density functional theory calculations. These 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

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Ga (SG:139) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 VFe (SG:221) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 VOs (SG:221) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 La (SG:225) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Ho (SG:166) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 YMn12 (SG:139) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 PI3 (SG:173) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Dy (SG:166) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 (SG:229) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 YB2 (SG:191) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 La (SG:229) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Tb (SG:229) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Dy (SG:229) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 YZn (SG:225) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Tm (SG:229) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Lu (SG:229) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 SO3 (SG:33) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 SO3 (SG:33) 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 WO3 (SG:130) by Materials Project

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

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 WO3 (SG:14) by Materials Project

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

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 WO3 (SG:129) by Materials Project

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

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 WO3 (SG:60) by Materials Project

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

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 WO3 (SG:221) by Materials Project

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

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 WO3 (SG:185) by Materials Project

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

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 WO3 (SG:193) by Materials Project

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

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 I (SG:64) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Mn (SG:217) by Materials Project

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

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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. 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

  3. VISA DEEMED EXPORT QUESTIONNAIRE -Background Information and Key Definitions First-time users: Please do not complete the Visa Deemed Export Questionnaire until you have read the following material.

    E-Print Network [OSTI]

    Bordenstein, Seth

    VISA DEEMED EXPORT QUESTIONNAIRE - Background Information and Key Definitions First-time users: Please do not complete the Visa Deemed Export Questionnaire until you have read the following material. Repeat users of the Visa Deemed Export Questionnaire: Continue to Page 3 Introduction: The revised Form I

  4. Training Management Information System

    SciTech Connect (OSTI)

    Rackley, M.P.

    1989-01-01

    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.

  5. Image Gallery

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room NewsInformation CurrentHenry Bellamy, Ph.D.FoodHydropower,PrincipalIdaho NationalA pIlyaImage

  6. 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...

  7. Engineering Materials and

    E-Print Network [OSTI]

    Furui, Sadaoki

    Science Engineering Materials and Chemical Technology Computing Life Science and Technology Environment and Society Mathematics Physics Chemistry Earth and Planetary Sciences Mechanical Engineering Systems and Control Engineering Electrical and Electronic Engineering Information and Communications

  8. Image Utility Assessment and a Relationship with Image Quality Assessment

    E-Print Network [OSTI]

    Hemami, Sheila S.

    Image Utility Assessment and a Relationship with Image Quality Assessment David M. Rouse , Romuald information to humans, and this paper investigates the utility assessment task, where human observers evaluate the usefulness of a natural image as a surrogate for a reference. Current QA algorithms implicitly assess utility

  9. Array combination for parallel imaging in Magnetic Resonance Imaging 

    E-Print Network [OSTI]

    Spence, Dan Kenrick

    2007-09-17

    In Magnetic Resonance Imaging, the time required to generate an image is proportional to the number of steps used to encode the spatial information. In rapid imaging, an array of coil elements and receivers are used to reduce the number of encoding...

  10. Chemical Imaging Initiative Delivering New Capabilities for

    E-Print Network [OSTI]

    or with light-source capabilities to image materials of importance to the nation's energy and environmentalChemical Imaging Initiative Delivering New Capabilities for In Situ, Molecular-Scale Imaging A complete, precise and realistic view of chemical, materials and biochemical processes and an understanding

  11. Chemical Sciences Division | Advanced Materials |ORNL

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

    analysis, chemical imaging, neutron science, polymer science, and interfacial science. Theory is closely integrated with materials synthesis and characterization to gain new...

  12. Evaluation and Characterization of Lightweight Materials: Success...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    at the High Temperature Materials Laboratory and HTML User Program Success Stories Characterization of Li-ion Batteries using Neutron Diffraction and Infrared Imaging Techniques...

  13. Supplemental Material Supplemental Figure Legends

    E-Print Network [OSTI]

    Tsien, Roger Y.

    Supplemental Material Supplemental Figure Legends Supp. Fig. 1. Fluorescence images of 3-D clusters) for Integrative Biology This journal is (c) The Royal Society of Chemistry 2009 #12;Supplemental Figures

  14. EC Transmission Line Materials

    SciTech Connect (OSTI)

    Bigelow, Tim S

    2012-05-01

    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.

  15. A material segmentation and classification system

    E-Print Network [OSTI]

    Wong, Jennifer L

    2013-01-01

    In this thesis, I developed a material segmentation and classification system that takes in images of an object and identifies the material composition of the object's surface. The 3D surface is first segmented into regions ...

  16. Astronomical Image and Signal Processing

    E-Print Network [OSTI]

    Starck, Jean-Luc

    of the amount of information in the image. Shannon [38], in the framework of communication theory, suggested and where, and the statistical one, which gives a quantity rela- tive to the amount of information. Another to measure the informa- tion in an astronomical image, in both a statistical and a deterministic way

  17. RADIOACTIVE MATERIALS SENSORS

    SciTech Connect (OSTI)

    Mayo, Robert M.; Stephens, Daniel L.

    2009-09-15

    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.

  18. Gabriel Sosa's Material for MA26200

    E-Print Network [OSTI]

    MA 26200: Linear Algebra and Differential Equations. Review Material. Trigonometric Identities Summary · Review of Integration Techniques. Quiz Information.

  19. 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 on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QAsource History View NewTexas: Energy Resources JumpNewTexas:HydrothermallyIFBIdea OneIllumitex Jump to:11)

  20. Laboratoire d'InfoRmatique en Image et Systmes d'Information UMR5205 CNRS/INSA de Lyon/Universit Claude Bernard Lyon 1/Universit Lumire Lyon 2/Ecole Centrale de

    E-Print Network [OSTI]

    Wolf, Christian

    Laboratoire d'InfoRmatique en Image et Systèmes d'Information UMR5205 CNRS/INSA de Lyon/Université Claude Bernard Lyon 1/Université Lumière Lyon 2/Ecole Centrale de Lyon Laboratoire d'InfoRmatique en Lyon, Université Lyon 1, Université Lyon 2, ECL 3 sites: Villeurbanne ­ Bron ­ Ecully 308 members

  1. Covetic Materials

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

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

  2. 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

  3. material protection

    National Nuclear Security Administration (NNSA)

    %2A en Office of Weapons Material Protection http:www.nnsa.energy.govaboutusourprogramsnonproliferationprogramofficesinternationalmaterialprotectionandcooperation-1

  4. Critical Materials:

    Office of Environmental Management (EM)

    Extraction Separation Processes for Critical Materials in 30- 21 Stage Test Facility (Bruce Moyer) ......

  5. Materials Scientist

    Broader source: Energy.gov [DOE]

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

  6. Materials of Gasification

    SciTech Connect (OSTI)

    2005-09-15

    The objective of this project was to accumulate and establish a database of construction materials, coatings, refractory liners, and transitional materials that are appropriate for the hardware and scale-up facilities for atmospheric biomass and coal gasification processes. Cost, fabricability, survivability, contamination, modes of corrosion, failure modes, operational temperatures, strength, and compatibility are all areas of materials science for which relevant data would be appropriate. The goal will be an established expertise of materials for the fossil energy area within WRI. This would be an effort to narrow down the overwhelming array of materials information sources to the relevant set which provides current and accurate data for materials selection for fossil fuels processing plant. A significant amount of reference material on materials has been located, examined and compiled. The report that describes these resources is well under way. The reference material is in many forms including texts, periodicals, websites, software and expert systems. The most important part of the labor is to refine the vast array of available resources to information appropriate in content, size and reliability for the tasks conducted by WRI and its clients within the energy field. A significant has been made to collate and capture the best and most up to date references. The resources of the University of Wyoming have been used extensively as a local and assessable location of information. As such, the distribution of materials within the UW library has been added as a portion of the growing document. Literature from recent journals has been combed for all pertinent references to high temperature energy based applications. Several software packages have been examined for relevance and usefulness towards applications in coal gasification and coal fired plant. Collation of the many located resources has been ongoing. Some web-based resources have been examined.

  7. Enclosure for small animals during awake animal imaging

    DOE Patents [OSTI]

    Goddard, Jr., James S

    2013-11-26

    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.

  8. Materials Physics and Applications

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room NewsInformationJesse BergkampCentermillion toMSDS onBudgetMaterialMaterials Materials

  9. Materials Science and Technology

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room NewsInformationJesse BergkampCentermillion toMSDS onBudgetMaterialMaterialsMST Materials

  10. Materials Science

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

    Database (TPMD) Aerospace Structural Metals Database (ASMD) Damage Tolerant Design Handbook (DTDH) Microelectronics Packaging Materials Database (MPMD) Structural Alloys...

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

    E-Print Network [OSTI]

    Neirotti, Juan Pablo

    and developments. Repair materials, polymers in concrete. Non-Destructive Testing of Concrete. Reasons for making of control and repair. ii) advanced knowledge of the range of non-destructive systems that can be used an assessment of in-situ properties. Non destructive methods of monitoring concrete: surface hardness - rebound

  12. Fluorescent image tracking velocimeter

    DOE Patents [OSTI]

    Shaffer, Franklin D. (Library, PA)

    1994-01-01

    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.

  13. Simultaneous acquisition of differing image types

    DOE Patents [OSTI]

    Demos, Stavros G

    2012-10-09

    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.

  14. Magnetic spectroscopy and microscopy of functional materials

    SciTech Connect (OSTI)

    Jenkins, C.A.

    2011-01-28

    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).

  15. Interpretation of HRTEM images by image simulation: An introduction to theory and practice

    SciTech Connect (OSTI)

    O`Keefe, M.A.

    1994-08-01

    This tutorial describes the use of image simulation as an aid to interpretation of high-resolution transmission electron microscope images. Topics include some image processing as well as image simulation. Image processing is the manipulation of experimental images in order to extract some desired information. Image simulation is the generation of a computed or simulated image from a model structure. It requires a detailed knowledge of the process of image formation in the high-resolution transmission electron microscope. This tutorial concentrates on image simulation, with examples of image processing appearing only as required as illustrations. Because this is an introduction, the theory of image simulation is described, but not explored in depth. The practice of image simulation is covered in sufficient detail to enable the student to understand the functions of the various steps in the computations, and the parameters necessary for their evaluation.

  16. Complete information acquisition in scanning probe microscopy

    SciTech Connect (OSTI)

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

    2015-01-01

    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.

  17. Computerized Energy Information Sources 

    E-Print Network [OSTI]

    Gordon, D.

    1979-01-01

    , the Freelance Research Service in Houston, and the Industrial Information Service at SMU in Dallas. Regardless of who is obtaining your materials for you, they will need the mos~ complete bibliographic information that you can give them. This will assure...

  18. Scintillator material

    DOE Patents [OSTI]

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

    1994-01-01

    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.

  19. Scintillator material

    DOE Patents [OSTI]

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

    1992-01-01

    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.

  20. Scintillator material

    DOE Patents [OSTI]

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

    1994-06-07

    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.

  1. Scintillator material

    DOE Patents [OSTI]

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

    1992-07-28

    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.

  2. material recovery

    National Nuclear Security Administration (NNSA)

    dispose of dangerous nuclear and radiological material, and detect and control the proliferation of related WMD technology and expertise.

  3. Energetic materials at extreme conditions 

    E-Print Network [OSTI]

    Millar, David Iain Archibald

    2011-06-27

    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 ...

  4. Nuclear material operations manual

    SciTech Connect (OSTI)

    Tyler, R.P.

    1981-02-01

    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.

  5. Materials Genome Initiative for Global Competitiveness

    E-Print Network [OSTI]

    Chandy, John A.

    Materials Genome Initiative for Global Competitiveness June 2011 #12;2 Materials Genome Initiative information visit www.ostp.gov. #12;3Materials Genome Initiative for Global Competitiveness EXECUTIVE OFFICE, the development of advanced materials will fuel many of the emerging industries that will address challenges

  6. 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

    2009-07-09

    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.

  7. Cermet materials

    DOE Patents [OSTI]

    Kong, Peter C. (Idaho Falls, ID)

    2008-12-23

    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.

  8. 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)

    2012-02-07

    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.

  9. 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

  10. High-speed photography of energetic materials and components with a copper vapor laser

    SciTech Connect (OSTI)

    Dosser, L.R.; Reed, J.W.; Stark, M.A.

    1988-01-01

    The evaluation of the properties of energetic materials, such as burn rate and ignition energy, is of primary importance in understanding their reactions and the functioning of devices containing them. One method for recording such information is high-speed photography at rates of up to 20,000 images per second. When a copper vapor laser is synchronized with the camera, laser-illuminated images can be recorded that detail the performance of a material and/or component in a manner never before possible. The laser can also be used for ignition of the energetic material, thus eliminating the need for bridgewires or electric squibs that can interfere with photography. Details of such ignitions are readily observable, and the burn rate of a material can be determined directly from the film. There are indications that information useful for the modeling of pyrotechnic reactions will become available as well. Recent results from high-speed photography of several pyrotechnic materials and devices will be presented. 9 figs.

  11. Material stabilization characterization management plan

    SciTech Connect (OSTI)

    GIBSON, M.W.

    1999-08-31

    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.

  12. Photographing paintings by image fusion Gloria Haro

    E-Print Network [OSTI]

    Photographing paintings by image fusion Gloria Haro Dept. of Information and Communications a quality photograph of a painting by multi-image fusion methods. The problem is particularly challenging in most photographs of paintings. A fully automatic image processing chain is described that, starting

  13. Gabriel Sosa's Material for MA26200

    E-Print Network [OSTI]

    Quiz Information. Material for Exams. Review Sheet for Final Exam, Answers. Lesson Notes. Summary for Lesson 4: Euler's Formula and Classification of solids.

  14. Silicon Materials and Devices (Fact Sheet)

    SciTech Connect (OSTI)

    Not Available

    2011-06-01

    Capabilities fact sheet for the National Center for Photovoltaics: Silicon Materials and Devices that includes scope, core competencies and capabilities, and contact/web information.

  15. Materials scientists use ORNL's CADES to transform big data to...

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

    chemical imaging. The US Department of Energy's Oak Ridge National Laboratory (ORNL) is home to state-of-the-art microscopes at ORNL's Center for Nanophase Materials Sciences...

  16. Light-Material Interactions in Energy Conversion - Energy Frontier...

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

    Seminars image Perovskite Solar Cells: Towards New Materials and New Applications Nripan Mathews, Nanyang Technological University, Singapore November 3, 2014, 11:15 am 101...

  17. Material Disposal Areas

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room NewsInformationJesse BergkampCentermillion toMSDS onBudgetMaterial Disposal Areas Material

  18. Materials Science Applications

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room NewsInformationJesse BergkampCentermillion toMSDS onBudgetMaterialMaterials

  19. Materials Science | NREL

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room NewsInformationJesse BergkampCentermillion toMSDS onBudgetMaterialMaterialsMST

  20. IEEE TRANSACTIONS ON IMAGE PROCESSING 1 Image Noise Level Estimation by Principal

    E-Print Network [OSTI]

    Hesser, Jürgen

    IEEE TRANSACTIONS ON IMAGE PROCESSING 1 Image Noise Level Estimation by Principal Component IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.org. S

  1. Complex Materials

    SciTech Connect (OSTI)

    Cooper, Valentino

    2014-04-17

    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.

  2. Complex Materials

    ScienceCinema (OSTI)

    Cooper, Valentino

    2014-05-23

    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.

  3. material removal

    National Nuclear Security Administration (NNSA)

    %2A en Nuclear Material Removal http:www.nnsa.energy.govaboutusourprogramsdnnm3remove

    Pag...

  4. The Natural Materials Browser: Using a Tablet Interface for Exploring Volumetric Materials Science Datasets

    E-Print Network [OSTI]

    California at Santa Barbara, University of

    The Natural Materials Browser: Using a Tablet Interface for Exploring Volumetric Materials Science, that allows a user to interact with volumetric datasets cre- ated from a series of natural materials samples. The data samples ­ high resolution meso-scale volumetric images of nutshells gath- ered via micro

  5. Propulsion materials

    SciTech Connect (OSTI)

    Wall, Edward J.; Sullivan, Rogelio A.; Gibbs, Jerry L.

    2008-01-01

    The Department of Energy’s (DOE’s) Office of Vehicle Technologies (OVT) is pleased to introduce the FY 2007 Annual Progress Report for the Propulsion Materials Research and Development Program. Together with DOE national laboratories and in partnership with private industry and universities across the United States, the program continues to engage in research and development (R&D) that provides enabling materials technology for fuel-efficient and environmentally friendly commercial and passenger vehicles.

  6. Capturing Images with Sparse Informational Pixels using Projected 3D Tags Li Zhang1 Neesha Subramaniam2 Robert Lin3 Ramesh Raskar4 Shree Nayar3

    E-Print Network [OSTI]

    Nayar, Shree K.

    , from professional SLRs to cellphone cameras, have become ubiquitous in daily life. Today, cameras are being used not only for photography but also to access information. For example, some cellphone cameras of this approach is that it requires physically installing electronics and power sources on the objects

  7. Inferring nuclear movements from fixed material Charless Fowlkes

    E-Print Network [OSTI]

    Fowlkes, Charless

    Inferring nuclear movements from fixed material Charless Fowlkes Jitendra Malik Electrical prior specific permission. #12;Inferring nuclear movements from fixed material Charless C. Fowlkes using nuclear positions extracted from a large number of images of fixed embryos. Embryos are sorted

  8. Page 1 of 14 SUPPLEMENTARY MATERIAL

    E-Print Network [OSTI]

    Cheng, Ji-Xin

    -mail:jcheng@purdue.edu Methods and Material VPA imaging system. The setup employs a Nd:YAG (Quantaray) pumped optical parametric raster scanning in the XY plane generates volumetric data on which an imaging reconstruction is grounded. Each volumetric data point was acquired at the repetition rate of the laser. With the 10 Hz repetition

  9. Advancements in branched bottlebrush polymers for responsive, targeted imaging

    E-Print Network [OSTI]

    Sowers, Molly A. (Molly Ann)

    2015-01-01

    Multi-modality and stimuli responsive nanoparticles are promising platform materials for medical imaging and diagnostics. Specifically magnetic resonance imaging (MRI) and nearinfrared (NIR) fluorescent probes can be used ...

  10. Aquaculture information package

    SciTech Connect (OSTI)

    Boyd, T.; Rafferty, K.

    1998-08-01

    This package of information is intended to provide background information to developers of geothermal aquaculture projects. The material is divided into eight sections and includes information on market and price information for typical species, aquaculture water quality issues, typical species culture information, pond heat loss calculations, an aquaculture glossary, regional and university aquaculture offices and state aquaculture permit requirements. A bibliography containing 68 references is also included.

  11. Materializing interaction

    E-Print Network [OSTI]

    Coelho, Marcelo

    2013-01-01

    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 ...

  12. Hardfacing material

    DOE Patents [OSTI]

    Branagan, Daniel J. (Iona, ID)

    2012-01-17

    A method of producing a hard metallic material by forming a mixture containing at least 55% iron and at least one of boron, carbon, silicon and phosphorus. The mixture is formed into an alloy and cooled to form a metallic material having a hardness of greater than about 9.2 GPa. The invention includes a method of forming a wire by combining a metal strip and a powder. The metal strip and the powder are rolled to form a wire containing at least 55% iron and from two to seven additional elements including at least one of C, Si and B. The invention also includes a method of forming a hardened surface on a substrate by processing a solid mass to form a powder, applying the powder to a surface to form a layer containing metallic glass, and converting the glass to a crystalline material having a nanocrystalline grain size.

  13. Materials Science Application Training 2015

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room NewsInformationJesse BergkampCentermillion toMSDS onBudgetMaterialMaterials MaterialsMaterials

  14. Materials Data on CoS (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 K(CoS)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 K(CoS)2 (SG:139) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 SrCu2GeSe4 (SG:40) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 K2ZrGe2O7 (SG:15) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 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

  20. 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

  1. Materials Data on Th(GeAu)2 (SG:139) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 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

  3. Materials Data on Ge(Te2As)2 (SG:166) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 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

  5. Materials Data on Li(NiGe)6 (SG:191) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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

    2014-11-02

    Computed materials data using density functional theory calculations. These 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

    2014-11-02

    Computed materials data using density functional theory calculations. These 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

    2014-11-02

    Computed materials data using density functional theory calculations. These 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

    2014-11-02

    Computed materials data using density functional theory calculations. These 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

    2014-11-02

    Computed materials data using density functional theory calculations. These 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

    2014-11-02

    Computed materials data using density functional theory calculations. These 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

    2014-11-02

    Computed materials data using density functional theory calculations. These 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

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. Materials Data on Tm4Zn5Ge6 (SG:36) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. Materials Data on Tb2Ge2O7 (SG:92) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 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

  20. 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

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. Materials Data on RbNa2Ge17 (SG:227) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 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

  11. 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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. Materials Data on Ge2Te5As2 (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

  19. Materials Data on Ge2Pt (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

  20. Materials Data on Zr3(Cu2Ge)2 (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 RbNa2Ge17 (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

  2. Materials Data on CaCdGe (SG:189) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Nb5Ge3 (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

  4. Materials Data on CaCdGe (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

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

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Zn3Ni2Ge (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

  7. Materials Data on HoGe (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

  8. Materials Data on Lu4Zn5Ge6 (SG:36) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Ca7Ge6 (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

  10. Materials Data on SrNi3Ge2 (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 Ge3Os2 (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

  12. Materials Data on SmCrGe3 (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

  13. Materials Data on NdCrGe3 (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 La3(GeRh)4 (SG:71) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 LiHoGe (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 Ca(GeIr)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 Li(NiGe)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

  18. Materials Data on Li2SnGe (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 PrGe3Rh (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

  20. Materials Data on Na5GeAs3 (SG:14) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Tm4Zn5Ge6 (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

  2. Materials Data on PrCoGe3 (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

  3. Materials Data on Ge3Os2 (SG:60) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 LiMnH6O7 (SG:186) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 MnH6(SO4)4 (SG:14) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 MnO2 (SG:166) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 V2O5 (SG:62) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 CoH4(CO3)2 (SG:15) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 MnTl2H2OF5 (SG:63) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 NiSnH12(OF)6 (SG:148) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Li3CoPCO7 (SG:11) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 MnH2(SO4)2 (SG:14) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 V(SiO3)2 (SG:15) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Li3MnPCO7 (SG:11) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 PWO4F (SG:2) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Tl3(MoO4)2 (SG:186) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 K2VH2OF5 (SG:15) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 CrHO2 (SG:160) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Mn3H2Se3O10 (SG:2) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Fe2P3(HO3)3 (SG:176) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Li2MnPCO7 (SG:11) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Pr(WO4)2 (SG:2) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 MoH8(NO2)2 (SG:12) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 V2(SO4)3 (SG:15) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 MgFe2P2(HO)18 (SG:2) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 MnPH2O5 (SG:15) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Li2NiPCO7 (SG:11) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Zn8Ag5 (SG:217) 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 KMnPH2O5 (SG:31) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 MoH8S2(NO)2 (SG:15) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 La4CoO8 (SG:65) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Rb2MnH2OF5 (SG:63) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 RbMnP3HO10 (SG:15) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 LiCo(WO4)2 (SG:1) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 YMo3O8 (SG:12) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 NiPO4F (SG:2) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 FeMoClO4 (SG:11) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Fe7P6(HO6)4 (SG:2) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 V2O5 (SG:12) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 FeHO2 (SG:63) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 LaCrHO5 (SG:14) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 KCr3H6(SO7)2 (SG:166) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 NiH2SeO5 (SG:15) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 LiPWO4F (SG:2) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Ni3H2Se3O10 (SG:2) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Fe4P3(HO5)3 (SG:15) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 MgMoH2O5 (SG:2) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Li3VPCO7 (SG:11) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 CoH2SeO5 (SG:15) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 MnAsH2O5 (SG:15) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 YbSe (SG:225) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 ThSnPt (SG:216) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Ta4GaSe8 (SG:216) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 LiTmSiO4 (SG:62) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 La(P3Ru)4 (SG:204) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Na3Mo(OF)3 (SG:146) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 EuNb8O14 (SG:55) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 CsSr2Nb3O10 (SG:123) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 Pr2NiO4 (SG:139) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

    Computed materials data using density functional theory calculations. These 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 SmGa5Co (SG:123) by Materials Project

    SciTech Connect (OSTI)

    Kristin Persson

    2014-11-02

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