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Zhou, P., Xiao, X., Zhu, X., Chen, Y., Lu, W., Piao, M., Cao, Z., Lu, M., Fang, F., Li, Z., Jiang, L. and Chen, L. (2023) Machine Learning Enabled Customization of Performance-Oriented Hydrogen Storage Materials for Fuel Cell Systems. Energy Storage Mater, 63, 102964. https://doi.org/10.1016/j.ensm.2023.102964

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• TITLE: Properties of Ti-Based Hydrogen Storage Alloy

  AUTHORS: Rui Xu, Tao Cheng, Chaoyu Li, Xue Yang, Junfeng Rong

  KEYWORDS: Renewable Energy, Hydrogen Storage, Ti-Based Alloy, Machine Learning

  JOURNAL NAME: Journal of Power and Energy Engineering, Vol.12 No.3, March 29, 2024

  ABSTRACT: An efficient and safe hydrogen storage method is one of the important links for the large-scale development of hydrogen in the future. Because of its low price and simple design, Ti-based hydrogen storage alloys are considered to be suitable for practical applications. In this paper, we review the latest research on Ti-based hydrogen storage alloys. Firstly, the machine learning and density functional theory are introduced to provide theoretical guidance for the optimization of Ti-based hydrogen storage alloys. Then, in order to improve the hydrogen storage performance, we briefly introduce the research of AB type and AB2 type Ti-based alloys, focusing on doping elements and adaptive after treatment. Finally, suggestions for the future research and development of Ti-based hydrogen storage alloys are proposed.