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Related Experiment Video

Updated: Oct 9, 2025

Preparation of Nanoparticles for ToF-SIMS and XPS Analysis
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XAS Data Preprocessing of Nanocatalysts for Machine Learning Applications.

Oleg O Kartashov1, Andrey V Chernov1, Dmitry S Polyanichenko1

  • 1The Smart Materials Research Institute, Southern Federal University, 178/24 Sladkova, 344090 Rostov-on-Don, Russia.

Materials (Basel, Switzerland)
|December 24, 2021
PubMed
Summary

This study introduces automated tools for preprocessing X-ray absorption spectroscopy data, crucial for machine learning analysis of nanocatalysts. The developed pipeline and datasets accelerate materials discovery by improving data quality and enabling efficient knowledge extraction.

Keywords:
X-ray absorption spectradata preprocessingfunctional materialsmachine learningmaterials characterization

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Area of Science:

  • Materials Science
  • Chemistry
  • Data Science

Background:

  • Nanocatalyst development relies on precise characterization, often generating large, heterogeneous datasets.
  • Extracting knowledge from experimental data, particularly X-ray absorption spectroscopy (XAS), is key to accelerating materials discovery.
  • Current research often overlooks the critical XAS data preprocessing stage for machine learning (ML) applications.

Purpose of the Study:

  • To develop automated tools for preprocessing and presenting physical experimental data, specifically XAS data.
  • To create deposited datasets for palladium-based nanocatalysts to facilitate ML-driven research.
  • To bridge the gap between raw XAS data acquisition and its application in ML models.

Main Methods:

  • Developed a software toolkit implementing a single pipeline for XAS data preprocessing.
  • Utilized principal component analysis (PCA), z-score normalization, and the interquartile method for data cleaning.
  • Applied k-means clustering for material phase identification based on experimental feature vectors.

Main Results:

  • Successfully created a pipeline for automated XAS data preprocessing.
  • Generated deposited datasets of physical experiments on palladium-based nanocatalysts.
  • Demonstrated efficient data preparation for ML analysis and prediction.

Conclusions:

  • Automated preprocessing of XAS data is essential for reliable ML analysis in nanocatalyst research.
  • The developed tools and datasets will accelerate knowledge extraction and materials discovery.
  • Facilitates data dissemination and reuse among researchers for advancing artificial intelligence in materials science.