Thermodynamically‐Driven Phase Engineering and Reconstruction Deduction of Medium‐Entropy Prussian Blue Analogue Nanocrystals

作者：Guangxun Zhang, Wanchang Feng, Guangyu Du, Yi Zhang, Ya Yang, Dian Xu, Tianyi Wang, Han‐Yi Chen, Huaiguo Xue, Mohsen Shakouri, Huan Pang · 发表于：Advanced Materials · 年份：2025 · DOI：10.1002/adma.202503814 · 被引用次数：31 · 研究领域：Machine Learning in Materials Science、Electrocatalysts for Energy Conversion、Conducting polymers and applications

Prussian blue analogs (PBAs) are exemplary precursors for the synthesis of a diverse array of derivatives.Yet, the intricate mechanisms underlying phase transitions in these multifaceted frameworks remain a formidable challenge. In this study, a machine learning-guided analysis of phase transitions in a medium-entropy PBA system is delineated, utilizing an array of descriptors that encompass crystallographic phases, structural subtleties, and fluctuations in multimetal valence states. By integrating multimodal simulations with experimental validation, a thermodynamics-driven phase transformation model for medium-entropy PBA is established and accurately predicted the critical synthesis parameters. A constellation of advanced techniques-including atomic force microscopy coupled with Kelvin probe force microscopy for individual nanoparticles, X-ray absorption spectroscopy, operando ultraviolet-visible spectroscopy, in situ X-ray diffraction, theoretical calculations, and multiphysics simulations-substantiated that the iron oxide@NiCoZnFe-PBA exhibits both exceptional stability and remarkable electrochemical activity. This investigation provides profound insights into the phase transition dynamics of polymetallic complexes and propels the rational design of other thermally-induced derivatives.