Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part II: Quantitative Fitting of Spectra
Abstract
Carbon-based nanomaterials are a promising platform for diverse technologies, but their rational design requires a more detailed chemical control over their structure and properties than is currently available. A long-standing challenge for the field has been in the interpretation and use of experimental X-ray spectra, especially for the amorphous and disordered forms of carbon. Here, we outline a unified approach to simultaneously and quantitatively analyze experimental X-ray absorption spectroscopy (XAS) and X-ray photoelectron spectroscopy (XPS) spectra of carbonaceous materials. We employ unsupervised machine learning to identify the most representative chemical environments and deconvolute experimental data according to these spectral contributions. To fit experimental spectra we rely on ab initio references and use all the information available: to fit experimental XAS spectra, the whole XAS fingerprint (reference) spectra of certain sites are taken into account, rather than just peak positions, as is currently the standard procedure. We argue that, even for predominantly pure-carbon materials, carbon Kedge and oxygen K-edge spectra should not be interpreted separately, since the presence of even small amounts of functional groups at the surface manifests itself on the X-ray spectroscopic signatures of both elements in an interlinked manner. Finally, we introduce the idea of carrying out simultaneousmore »
- Authors:
-
[1];
[2];
[3];
[1];
[4]
- Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, 02150 Espoo, Finland
- Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
- Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, California 94025, United States
- Department of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, 02150 Espoo, Finland, Department of Applied Physics, Aalto University, 02150 Espoo, Finland
- Publication Date:
- Research Org.:
- Aalto Univ., Otaniemi (Finland); SLAC National Accelerator Lab., Menlo Park, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1575819
- Alternate Identifier(s):
- OSTI ID: 1575917; OSTI ID: 1596277
- Grant/Contract Number:
- AC02-76SF00515; 285526; 310574; 730897
- Resource Type:
- Published Article
- Journal Name:
- Chemistry of Materials
- Additional Journal Information:
- Journal Name: Chemistry of Materials Journal Volume: 31 Journal Issue: 22; Journal ID: ISSN 0897-4756
- Publisher:
- American Chemical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Citation Formats
Aarva, Anja, Deringer, Volker L., Sainio, Sami, Laurila, Tomi, and Caro, Miguel A. Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part II: Quantitative Fitting of Spectra. United States: N. p., 2019.
Web. doi:10.1021/acs.chemmater.9b02050.
Aarva, Anja, Deringer, Volker L., Sainio, Sami, Laurila, Tomi, & Caro, Miguel A. Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part II: Quantitative Fitting of Spectra. United States. https://doi.org/10.1021/acs.chemmater.9b02050
Aarva, Anja, Deringer, Volker L., Sainio, Sami, Laurila, Tomi, and Caro, Miguel A. Mon .
"Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part II: Quantitative Fitting of Spectra". United States. https://doi.org/10.1021/acs.chemmater.9b02050.
@article{osti_1575819,
title = {Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part II: Quantitative Fitting of Spectra},
author = {Aarva, Anja and Deringer, Volker L. and Sainio, Sami and Laurila, Tomi and Caro, Miguel A.},
abstractNote = {Carbon-based nanomaterials are a promising platform for diverse technologies, but their rational design requires a more detailed chemical control over their structure and properties than is currently available. A long-standing challenge for the field has been in the interpretation and use of experimental X-ray spectra, especially for the amorphous and disordered forms of carbon. Here, we outline a unified approach to simultaneously and quantitatively analyze experimental X-ray absorption spectroscopy (XAS) and X-ray photoelectron spectroscopy (XPS) spectra of carbonaceous materials. We employ unsupervised machine learning to identify the most representative chemical environments and deconvolute experimental data according to these spectral contributions. To fit experimental spectra we rely on ab initio references and use all the information available: to fit experimental XAS spectra, the whole XAS fingerprint (reference) spectra of certain sites are taken into account, rather than just peak positions, as is currently the standard procedure. We argue that, even for predominantly pure-carbon materials, carbon Kedge and oxygen K-edge spectra should not be interpreted separately, since the presence of even small amounts of functional groups at the surface manifests itself on the X-ray spectroscopic signatures of both elements in an interlinked manner. Finally, we introduce the idea of carrying out simultaneous fits of XAS and XPS spectra, to reduce the number of degrees of freedom and arbitrariness of the fits. This work opens up a new direction, tightly integrating experiment and simulation, for understanding and ultimately controlling the functionalization of carbon nanomaterials at the atomic level.},
doi = {10.1021/acs.chemmater.9b02050},
journal = {Chemistry of Materials},
number = 22,
volume = 31,
place = {United States},
year = {2019},
month = {10}
}
https://doi.org/10.1021/acs.chemmater.9b02050
Search WorldCat to find libraries that may hold this journalWeb of Science