Authors: M. Yamada | Tetsuya Yamada | Kazumichi Yanagisawa | Mongkol Tipplook | Fumitaka Hayashi | Katsuya Teshima
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Data-Driven Analysis of Contributing Factors in Spectra Toward Quantitative Prediction of Photocatalytic Activity of BaTaO₂N Photocatalytic hydrogen production via water splitting has attracted attention as an energy infrastructure independent of fossil resources. However, it has not yet reached the practical energy conversion efficiency level (5%), necessitating the enhancement of photocatalyst materials. Various crystal characteristics such as crystal shape, surface state, and lattice defects are known to be influencing factors on the performance of photocatalysts. However, their quantitative correlation and synergistic effects of each factor on performance are unclear. Understanding the quantitative correlation of crystal properties with photocatalytic activity would give effective crystal design for improvement. In this study, we aimed to extract crystallographic properties and understand their correlation with photocatalytic water splitting by multivariate analysis. BaTaO 2 N (BTON), which responds to visible light, was selected as a model material for photocatalysis. First, internal crystal information was collected from X-ray diffraction (XRD) patterns and Ultraviolet-Visible (UV-Vis) absorption spectra. Interpretable feature values, such as intensity ratio and full width at half maximum at the diffraction peak, impurity, absorption edge wavelength and its area were automatically extracted using Python. The factors contributing to water splitting were then identified by using LASSO regression. These feature values were used to depict a two-dimensional dimension reduction diagram (t-SNE diagram), which describe photocatalytic activity as color map. The t-SNE diagram confirmed the clustering of data with similar water splitting activity, indicating that this diagram can be used as an effective guide to explore the crystal properties with high photocatalytic activity. Next, scanning electron microscopy (SEM) was analyzed to extract additional feature values. Image analysis was carried out for the comprehensive extraction of crystal surface information. Gray-Level Co-occurrence Matrix (GLCM) texture analysis was an effective method for detecting boundary parts, thus used to extract abstract crystal surface information. In addition, Variational Autoencoder (VAE) was used to extract specific crystal surfaces. The extracted feature values can contain information about crystal surface structures such as crystal morphology, particle agglomeration, and co-catalyst support state. Correlation analysis between these feature values and photocatalytic activity is now underway and will be reported in the presentation. Acknowledgement This work was partly supported by the Strategic Innovation Program (SIP) of the Cabinet Office, the NEDO Green Innovation fund projects, and collaborative research with companies. Photocatalysis ; Chemistry ; Environmental science ; Environmental chemistry ; Biochemistry ; Catalysis Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Water Quality Monitoring and Analysis