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2026.07.24 by 張 葉平 Yohei ChoEstablishing design guidelines for photocatalysts is challenging. In particular, identifying materials with favorable kinetic properties among those that satisfy the thermodynamic requirements remains a major bottleneck in materials development. In this study, promising materials identified through large-scale exploration of a diverse materials space were systematically evaluated under controlled reaction temperatures and light intensities to examine their charge-supply and charge-transfer characteristics. The results revealed that these kinetic characteristics vary systematically with elemental composition, enabling the development of element-based design guidelines for photocatalysts. This work extends high-throughput experimentation beyond simple activity screening toward kinetics-informed materials design.
2026.07.09 by Patchanee ChammingkwanConventional catalyst research starts from a predefined target reaction. Here, we demonstrated “reaction exploration,” an original approach that searches catalysts, conditions, and products simultaneously without fixing the target reaction or product in advance. By exploring a broad CH4–O2–CO2 reaction space using high-throughput experimentation and non-targeted mass analysis, we obtained a one-million-point dataset and found previously hidden high-performance regions as well as hints of overlooked reactions.
2026.02.14 by Poulami MUKHERJEESurface-engineering strategy potentially synthesizes next-generation MOF-derived electrocatalysts. In this work, PBA chemistry is reimagined for advanced carbon conversion through engineered interfaces that drive conductivity and electrochemically active surface area exposure, contributing to CO2RR performance.
2025.12.26 by 張 葉平 Yohei ChoA low experimental throughput in photocatalytic testing has been a major bottleneck in large-scale materials exploration. In this study, we developed a microplate assay capable of evaluating approximately 500 photocatalysts per day, enabling rapid and quantitative assessment of photocatalytic activity. This method provides a foundation for accelerating comprehensive photocatalyst exploration and data-driven research.
2025.11.25 by 大信田 泰康 Taiko OSHIDAPolymer blending is commonly used to improve plastic properties, but accurately characterizing internal (phase) structures has required advanced chemical and analytical techniques. In this study, we demonstrate that multiparameter images from atomic force microscopy can be used to automatically classify phase structures via unsupervised machine learning. We also propose a method to select parameter combinations that maximize classification performance. This approach provides a general framework applicable to various polymer blends.