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.
Authors
- Guangxun Zhang (ORCID: https://orcid.org/0000-0002-6982-9170)
- Guangyu Du (ORCID: https://orcid.org/0000-0002-9925-3942)
- Han‐Yi Chen (ORCID: https://orcid.org/0000-0001-8146-7101)
- Mohsen Shakouri (ORCID: https://orcid.org/0000-0002-8449-9763)
- Tianyi Wang (ORCID: https://orcid.org/0000-0003-1824-1171)
- Wanchang Feng
- Huan Pang (ORCID: https://orcid.org/0000-0002-5319-0480)
- Huaiguo Xue
- Dian Xu
- Ya Yang
- Yi Zhang
Institutions
- 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)
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
Funders
- National Natural Science Foundation of China