Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part II: Quantitative Fitting of Spectra

doi:10.1021/acs.chemmater.9b02050

Title: Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part II: Quantitative Fitting of Spectra

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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 »« less

Authors:

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Aalto Univ., Otaniemi (Finland)
Univ. of Cambridge (United Kingdom)
SLAC National Accelerator Lab., Menlo Park, CA (United States)

Publication Date:: 2019-10-28

Research Org.:: Aalto Univ., Otaniemi (Finland)

Sponsoring Org.:: USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)

OSTI Identifier:: 1575819

Alternate Identifier(s):: OSTI ID: 1575917

Grant/Contract Number:: AC02-76SF00515

Resource Type:: Published Article

Journal Name:: Chemistry of Materials

Additional Journal Information:: Journal Volume: 31; Journal Issue: 22; Journal ID: ISSN 0897-4756

Publisher:: American Chemical Society (ACS)

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.

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                    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.  doi:10.1021/acs.chemmater.9b02050.

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                    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.  doi:10.1021/acs.chemmater.9b02050.

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@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}

}

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