<i>EDA</i>: EXAFS data-analysis software package

Kuzmin, Alexei

doi:10.1107/s1574870720003365

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…X-ray absorption spectroscopy (XAS) is a powerful element-specific technique to monitor the oxidation state and the local environment of copper atoms in the course of the reaction with methane. Commonly, two subtechniques are distinguished in XAS: the X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS). − Applied to copper-containing zeolites, the first technique allows monitoring the oxidation state and the changes in the local symmetry of copper sites. ,, The use of reference systems allows one to determine the fraction of different copper sites by analyzing XANES using linear combination fitting. ,,, Ab initio modeling of XANES spectra is possible; however, derivation of structural information solely based on XANES data remains challenging. , The spectral shape of EXAFS is determined by the local environment of the absorbing atom, yielding distances, coordination numbers, types of neighbors, and the Debye–Waller factor of the scattering neighbors. , Modeling and fitting of EXAFS are well-developed and widely utilized procedures, and there are several validated algorithms and software packages applied for this purpose. − Notably, the simultaneous analysis of both XANES and EXAFS data enables obtaining complementary information on the oxidation state, the symmetry, and the geometrical environment of the absorbing atom.…”

Section: Introductionmentioning

confidence: 99%

Structural Evolution of Copper-Oxo Sites in Zeolites upon the Reaction with Methane Investigated by Means of Cu K-edge X-ray Absorption Spectroscopy

et al. 2023

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The structure of copper sites formed under an oxidative environment and their evolution in the course of the reaction with methane at elevated temperature was investigated by means of Cu K-edge X-ray absorption spectroscopy for a series of copper-containing MFI, MOR, and FAU zeolites. The pretreatment in oxygen at 723 K leads to the formation of copper(II)-oxo sites, whose nature depends on the framework type. Dimeric species are formed in CuMFI material, dimeric and monomeric sites coexist in CuMOR, and agglomerated copper-oxo nanoclusters are found in large-pore copper-containing faujasite (CuFAU). For all studied materials, the reaction with methane resulted in the exclusive formation of copper(I) species; no formation of metallic copper was detected even at 748 K. The nature of formed copper(I) species is governed by the structure of corresponding copper(II) centers. In particular, monomeric and dimeric copper(II)-oxo sites hosted in CuMOR and CuMFI are transformed into isolated copper(I) cations coordinated to ion-exchange positions of the zeolite. Contrarily, copper(II)-oxo clusters present in CuFAU undergo restructuring with only a partial loss of extra-framework oxygen and form aggregated species with a structure similar to that of bulk copper(I) oxide.

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Section: Introductionmentioning

confidence: 99%

Structural Evolution of Copper-Oxo Sites in Zeolites upon the Reaction with Methane Investigated by Means of Cu K-edge X-ray Absorption Spectroscopy

et al. 2023

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XAS Data Preprocessing of Nanocatalysts for Machine Learning Applications

et al. 2021

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Innovative development in the energy and chemical industries is mainly dependent on advances in the accelerated design and development of new functional materials. The success of research in new nanocatalysts mainly relies on modern techniques and approaches for their precise characterization. The existing methods of experimental characterization of nanocatalysts, which make it possible to assess the possibility of using these materials in specific chemical reactions or applications, generate significant amounts of heterogeneous data. The acceleration of new functional materials, including nanocatalysts, directly depends on the speed and quality of extracting hidden dependencies and knowledge from the obtained experimental data. Usually, such experiments involve different characterization techniques and different types of X-ray absorption spectroscopy (XAS) too. Using the machine learning (ML) methods based on XAS data, we can study and predict the atomic-scale structure and another bunch of parameters for the nanocatalyst efficiently. However, before using any ML model, it is necessary to make sure that the XAS raw experimental data is properly pre-processed, cleared, and prepared for ML application. Usually, the XAS preprocessing stage is vaguely presented in scientific studies, and the main efforts of researchers are devoted to the ML description and implementation stage. However, the quality of the input data influences the quality of ML analysis and the prediction results used in the future. This paper fills the gap between the stage of obtaining XAS data from synchrotron facilities and the stage of using and customizing various ML analysis and prediction models. We aimed this study to develop automated tools for the preprocessing and presentation of data from physical experiments and the creation of deposited datasets on the basis of the example of studying palladium-based nanocatalysts using synchrotron radiation facilities. During the study, methods of preliminary processing of XAS data were considered, which can be conditionally divided into X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS). This paper proposes a software toolkit that implements data preprocessing scenarios in the form of a single pipeline. The main preprocessing methods used in this study proposed are principal component analysis (PCA); z-score normalization; the interquartile method for eliminating outliers in the data; as well as the k-means machine learning method, which makes it possible to clarify the phase of the studied material sample by clustering feature vectors of experiments. Among the results of this study, one should also highlight the obtained deposited datasets of physical experiments on palladium-based nanocatalysts using synchrotron radiation. This will allow for further high-quality data mining to extract new knowledge about materials using artificial intelligence methods and machine learning models, and will ensure the smooth dissemination of these datasets to researchers and their reuse.

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EDA: EXAFS data-analysis software package

Cited by 2 publications

References 28 publications

Structural Evolution of Copper-Oxo Sites in Zeolites upon the Reaction with Methane Investigated by Means of Cu K-edge X-ray Absorption Spectroscopy

Structural Evolution of Copper-Oxo Sites in Zeolites upon the Reaction with Methane Investigated by Means of Cu K-edge X-ray Absorption Spectroscopy

XAS Data Preprocessing of Nanocatalysts for Machine Learning Applications

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