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Title: Derivation of Multiple Covarying Material and Process Parameters Using Physics-Based Modeling of X-ray Data

Abstract

There is considerable interest in developing multimodal characterization frameworks capable of probing critical properties of complex materials by relying on distinct, complementary methods or tools. Any such framework should maximize the amount of information that is extracted from any given experiment and should be sufficiently powerful and efficient to enable on-the-fly analysis of multiple measurements in a self-consistent manner. Such a framework is demonstrated in this work in the context of self-assembling polymeric materials, where theory and simulations provide the language to seamlessly mesh experimental data from two different scattering measurements. Specifically, the samples considered here consist of diblock copolymers (BCP) that are self-assembled on chemically nanopatterned surfaces. The copolymers microphase separate into ordered lamellae with characteristic dimensions on the scale of tens of nanometers that are perfectly aligned by the substrate over macroscopic areas. These aligned lamellar samples provide ideal standards with which to develop the formalism introduced in this work and, more generally, the concept of high-information-content, multimodal experimentation. The outcomes of the proposed analysis are then compared to images generated by 3D scanning electron microscopy tomography, serving to validate the merit of the framework and ideas proposed here.

Authors:
 [1];  [2];  [3];  [3];  [3]; ORCiD logo [4];  [3];  [3]; ORCiD logo [5];  [6];  [6]; ORCiD logo [5];  [7];  [5]; ORCiD logo [8]; ORCiD logo [8]
  1. Mentor: A Siemens Business, Wilsonville, Oregon 97070, United States
  2. Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996, United States
  3. Institute for Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, United States
  4. Department of Chemical Engineering, Technion - Israel Institute of Technology, Haifa 32000, Israel
  5. Material Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
  6. Argonne National Laboratory, Argonne, Illinois 60439, United States
  7. imec, Kapeldreef 75, B-3001 Leuven, Belgium
  8. Institute for Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, United States; Argonne National Laboratory, Argonne, Illinois 60439, United States
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC); National Institute of Standards and Technology (NIST)
OSTI Identifier:
1419959
DOE Contract Number:
AC02-06CH11357
Resource Type:
Journal Article
Resource Relation:
Journal Name: Macromolecules; Journal Volume: 50; Journal Issue: 19
Country of Publication:
United States
Language:
English

Citation Formats

Khaira, Gurdaman, Doxastakis, Manolis, Bowen, Alec, Ren, Jiaxing, Suh, Hyo Seon, Segal-Peretz, Tamar, Chen, Xuanxuan, Zhou, Chun, Hannon, Adam F., Ferrier, Nicola J., Vishwanath, Venkatram, Sunday, Daniel F., Gronheid, Roel, Kline, R. Joseph, de Pablo, Juan J., and Nealey, Paul F. Derivation of Multiple Covarying Material and Process Parameters Using Physics-Based Modeling of X-ray Data. United States: N. p., 2017. Web. doi:10.1021/acs.macromol.7b00691.
Khaira, Gurdaman, Doxastakis, Manolis, Bowen, Alec, Ren, Jiaxing, Suh, Hyo Seon, Segal-Peretz, Tamar, Chen, Xuanxuan, Zhou, Chun, Hannon, Adam F., Ferrier, Nicola J., Vishwanath, Venkatram, Sunday, Daniel F., Gronheid, Roel, Kline, R. Joseph, de Pablo, Juan J., & Nealey, Paul F. Derivation of Multiple Covarying Material and Process Parameters Using Physics-Based Modeling of X-ray Data. United States. doi:10.1021/acs.macromol.7b00691.
Khaira, Gurdaman, Doxastakis, Manolis, Bowen, Alec, Ren, Jiaxing, Suh, Hyo Seon, Segal-Peretz, Tamar, Chen, Xuanxuan, Zhou, Chun, Hannon, Adam F., Ferrier, Nicola J., Vishwanath, Venkatram, Sunday, Daniel F., Gronheid, Roel, Kline, R. Joseph, de Pablo, Juan J., and Nealey, Paul F. Wed . "Derivation of Multiple Covarying Material and Process Parameters Using Physics-Based Modeling of X-ray Data". United States. doi:10.1021/acs.macromol.7b00691.
@article{osti_1419959,
title = {Derivation of Multiple Covarying Material and Process Parameters Using Physics-Based Modeling of X-ray Data},
author = {Khaira, Gurdaman and Doxastakis, Manolis and Bowen, Alec and Ren, Jiaxing and Suh, Hyo Seon and Segal-Peretz, Tamar and Chen, Xuanxuan and Zhou, Chun and Hannon, Adam F. and Ferrier, Nicola J. and Vishwanath, Venkatram and Sunday, Daniel F. and Gronheid, Roel and Kline, R. Joseph and de Pablo, Juan J. and Nealey, Paul F.},
abstractNote = {There is considerable interest in developing multimodal characterization frameworks capable of probing critical properties of complex materials by relying on distinct, complementary methods or tools. Any such framework should maximize the amount of information that is extracted from any given experiment and should be sufficiently powerful and efficient to enable on-the-fly analysis of multiple measurements in a self-consistent manner. Such a framework is demonstrated in this work in the context of self-assembling polymeric materials, where theory and simulations provide the language to seamlessly mesh experimental data from two different scattering measurements. Specifically, the samples considered here consist of diblock copolymers (BCP) that are self-assembled on chemically nanopatterned surfaces. The copolymers microphase separate into ordered lamellae with characteristic dimensions on the scale of tens of nanometers that are perfectly aligned by the substrate over macroscopic areas. These aligned lamellar samples provide ideal standards with which to develop the formalism introduced in this work and, more generally, the concept of high-information-content, multimodal experimentation. The outcomes of the proposed analysis are then compared to images generated by 3D scanning electron microscopy tomography, serving to validate the merit of the framework and ideas proposed here.},
doi = {10.1021/acs.macromol.7b00691},
journal = {Macromolecules},
number = 19,
volume = 50,
place = {United States},
year = {Wed Sep 27 00:00:00 EDT 2017},
month = {Wed Sep 27 00:00:00 EDT 2017}
}