Non-lithium energy storage devices, especially sodium ion batteries, are drawing attention due to insufficient and uneven distribution of lithium resources. Prussian blue and its analogs (Prussian blue analogs [PBAs]), or hexacyanoferrates, are well-known since the 18th century and have been used for hydrogen storage, cancer therapy, biosensing, seawater desalination, and sewage treatment. Owing to their unique features, PBAs are receiving increasing interest in the field of energy storage, such as their high theoretical specific capacity, ease of synthesis, as well as low cost. In this review, a general summary and evaluation of the applications of PBAs for rechargeable batteries are given. After a brief review of the history of PBAs, their crystal structure, nomenclature, synthesis, and working principle in rechargeable batteries are discussed. Then, previous works classified based on the combination of insertion cations and transition metals are analyzed comprehensively. The review includes an outlook toward the further development of PBAs in electrochemical energy storage.

Prussian blue

Electrochemical energy storage

10.1016/j.isci.2018.04.008

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Citations (396)

Alloying Co Species into Ordered and Interconnected Macroporous Carbon Polyhedra for Efficient Oxygen Reduction Reaction in Rechargeable Zinc–Air Batteries

Advanced Materials (2022)

Wei Li Bo Liu Da Liu Peifang Guo Jing Liu

Engineering non-precious transition metal (TM)-based electrocatalysts to simultaneously achieve an optimal intrinsic activity, high density of active sites, and rapid mass transfer ability for the oxygen reduction reaction (ORR) remains a significant challenge. To address this challenge, a hybrid composite consisting of Fex Co alloy nanoparticles uniformly implanted into hierarchically ordered macro-/meso-/microporous N-doped carbon polyhedra (HOMNCP) is rationally designed. The combined results of experimental and theoretical investigations indicate that the alloying of Co enables a favorable electronic structure for the formation of the *OH intermediate, while the periodically trimodal-porous structured carbon matrix structure not only provides highly accessible channels for active site utilization but also dramatically facilitates mass transfer in the catalytic process. As expected, the Fe0.5 Co@HOMNCP composite catalyst exhibits extraordinary ORR activity with a half-wave potential of 0.903 V (vs reversible hydrogen electrode), surpassing most Co-based catalysts reported to date. More remarkably, the use of the Fe0.5 Co@HOMNCP catalyst as the air electrode in a zinc-air battery results in superior open-circuit voltage and power density compared to a commercial Pt/C + IrO2 catalyst. The results of this study are expected to inspire the development of advanced TM-based catalysts for energy storage and conversion applications.

Carbon fibers

Oxygen storage

Reversible hydrogen electrode

10.1002/adma.202109605

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Citations (113)

Magnetic properties of the Y3(Fe,Mo)29Nx nitride

Journal of Alloys and Compounds (1997)

Hongge Pan Changpin Chen Shouquan Li Yang Fu-ming Xingguo Han

Iron nitride

Carbon nitride

10.1016/s0925-8388(96)03116-7

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Citations (3)

First-row transition metal compounds for aqueous metal ion batteries

Journal of Energy Chemistry (2021)

Mengmeng Zhou Xinjun Huang Xiaomeng Tian Baohua Jia Hongge Pan

10.1016/j.jechem.2021.09.002

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Citations (9)

Microstructure and mechanical properties of multi-carbides/(Al, Si) composites derived from porous B4C preforms by reactive melt infiltration

Materials Science and Engineering A (2012)

Hongyan Wu Shengcai Zhang Mingxia Gao Dan Zhu Yi Pan

Boron carbide

Vickers hardness test

Liquidus

Infiltration (HVAC)

Chemical vapor infiltration

10.1016/j.msea.2012.05.008

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Citations (16)

Integrating Machine Learning and Characterization in Battery Research: Toward Cognitive Digital Twins with Physics and Knowledge

Advanced Functional Materials (2025)

Erhai Hu Hong Han Choo Wei Zhang Afriyanti Sumboja Tribidasari A. Ivandini

Abstract The rapid advancement of battery technology has driven the need for innovative approaches to enhance battery management systems. In response, the concept of a cognitive digital twin has been developed to serve as a sophisticated virtual model that dynamically simulates, predicts, and optimizes battery behavior. These models integrate real‐time data with in‐depth physical insights, offering a comprehensive solution for battery management. Fundamental to this development are advanced characterization techniques such as microscopy, spectroscopy, tomography, and electrochemical methods—that provide critical insights into the underlying physics of batteries. Additionally, machine learning (ML) extends beyond predictive analytics to enhance the analytical capabilities. By uncovering deep physical insights, ML significantly improving the accuracy, reliability, and interpretability of these techniques. This review explores how integrating ML with traditional battery characterization techniques bridges the gap between deep physical insights and data‐driven analysis. The synergy not only enhances precision and computational efficiency but also minimizes human intervention, thereby paving the way for more robust and transparent digital twin technologies in battery research.

Characterization

10.1002/adfm.202422601

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Citations (0)

Optimizing hydrogen ad/desorption of Mg-based hydrides for energy-storage applications

Journal of Material Science and Technology (2022)

Zeng-Yi Li Yujia Sun Chenchen Zhang Sheng Wei Li Zhao

Magnesium hydride

10.1016/j.jmst.2022.08.047

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Citations (48)