EXAFS as a tool for catalyst characterization: a review of the data analysis methods

Noronha, Fábio B.

doi:10.1590/s0104-66321999000300009

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…The values of the parameters, mean square deviation (σ 2 ) and mean free path (λ) calculated using MathCAD programming for theoretical EXAFS data of cobalt metal foil are in a good agreement with the values of these parameters determined from X-ray measurements of the Debye-Waller factor by Noronha [9].…”

Section: Resultssupporting

confidence: 72%

Determination of Structural Parameters by EXAFS Analysis of Some Co Complexes

Mishra

Parsai

Dagaonkar³

2010

Acta Phys. Pol. A

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In this paper we deal with a relatively simpler method for the Fourier analysis of EXAFS data and illustrate this by a detailed analysis of the data for cobalt metal foil and cobalt complexes of isoxezol series. The Fourier analysis is useful in extracting distances and information about the phase shifts, the amplitudes, the Debye-Waller factors and the mean free paths. Here we have determined interatomic distances, mean square deviation and inelastic mean free path from the Fourier transform of EXAFS data for cobalt metal foil and cobalt complexes of isoxezol series by MathCAD programming.

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Section: Resultssupporting

confidence: 72%

Determination of Structural Parameters by EXAFS Analysis of Some Co Complexes

Mishra

Parsai

Dagaonkar³

2010

Acta Phys. Pol. A

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“…The data preprocessing module is often partially automated; moreover, the result obtained cannot always be examined outside the context of the functionality of software packages. Furthermore, the review work [21] proposes various theoretical methods for analyzing EXAFS data to characterize the catalysts under study, which primarily aimed at the direct extraction of primary data and material characteristics. Basically, most of the works are directed towards the analysis of the data obtained, without going into the details of their preliminary processing, which in the future can lead to difficulties in the reproducibility of the result obtained or its reuse in other studies.…”

Section: Mini Review Of Existing Solutionsmentioning

confidence: 99%

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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