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

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.

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Publication Details

Journal
Advanced Materials
Published
2025-04-14
DOI
https://doi.org/10.1002/adma.202503814
Citations
20
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
10.36

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article

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

Guangxun Zhang, Guangyu Du, Han‐Yi Chen, Mohsen Shakouri et al.
20 citations
Advanced Materials
Machine Learning in Materials Science
10.36
article

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

Guangxun Zhang, Guangyu Du, Han‐Yi Chen, Mohsen Shakouri, Tianyi Wang, Wanchang Feng, Huan Pang, Huaiguo Xue, Dian Xu, Ya Yang, Yi Zhang
article en
20 citations

Abstract

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.

Advanced MaterialsVol. 37(26)
Xinyang Normal University (CN), Hong Kong Polytechnic University (HK), National Tsing Hua University (TW), University of Saskatchewan (CA), Yangzhou University (CN), Canadian Light Source (Canada) (CA), Nanjing University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 1%
Machine Learning in Materials Science
10.36
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