Underlying factors of mega pressure hysteresis in cerium-rich CaCu<sub>5</sub>-type metal hydrides and effective modification strategies

Zhou, Panpan; Zhang, Jianwei; Bi, Jiapeng; Xiao, Xuezhang; Cao, Ziming; Zhan, Liujun; Shen, Huahai; Lu, Miao; Li, Zhinian; Zhao, Yuyuan; Wang, Li; Yan, Mi; Chen, Lixin

doi:10.1039/d3ta06351h

J. Mater. Chem. A

2023

DOI: 10.1039/d3ta06351h

|View full text |Cite

Underlying factors of mega pressure hysteresis in cerium-rich CaCu₅-type metal hydrides and effective modification strategies

Panpan Zhou,

Jianwei Zhang,

Jiapeng Bi

et al.

Abstract: Strong elastic deformation resistance and an intrinsic stable energetic state contribute to the mega pressure hysteresis in Ce-rich CaCu5-type metal hydrides.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

2025

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 5 publications

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives

Zhou,

Xiao

et al. 2024

Advanced Materials

View full text Add to dashboard Cite

Machine learning (ML) has emerged as a pioneering tool in advancing the research application of high‐performance solid‐state hydrogen storage materials (HSMs). This review summarizes the state‐of‐the‐art research of ML in resolving crucial issues such as low hydrogen storage capacity and unfavorable de‐/hydrogenation cycling conditions. First, the datasets, feature descriptors, and prevalent ML models tailored for HSMs are described. Specific examples include the successful application of ML in titanium‐based, rare‐earth‐based, solid solution, magnesium‐based, and complex HSMs, showcasing its role in exploiting composition–structure–property relationships and designing novel HSMs for specific applications. One of the representative ML works is the single‐phase Ti‐based HSM with superior cost‐effective and comprehensive properties, tailored to fuel cell hydrogen feeding system at ambient temperature and pressure through high‐throughput composition‐performance scanning. More importantly, this review also identifies and critically analyzes the key challenges faced by ML in this domain, including poor data quality and availability, and the balance between model interpretability and accuracy, together with feasible countermeasures suggested to ameliorate these problems. In summary, this work outlines a roadmap for enhancing ML's utilization in solid‐state hydrogen storage research, promoting more efficient and sustainable energy storage solutions.

show abstract

Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives

Zhou,

Xiao

et al. 2024

Advanced Materials

View full text Add to dashboard Cite

show abstract

Advancing the thermodynamic modeling of multicomponent phases in hydrogen-para-equilibrium

Hannappel,

Heubner,

Balcerzak

et al. 2025

Acta Materialia

View full text Add to dashboard Cite

Thermodynamic assessment of the Ce H and CeNi5 H system

Hannappel,

Alvares,

Heubner

et al. 2024

Calphad

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Underlying factors of mega pressure hysteresis in cerium-rich CaCu₅-type metal hydrides and effective modification strategies

Abstract: Strong elastic deformation resistance and an intrinsic stable energetic state contribute to the mega pressure hysteresis in Ce-rich CaCu5-type metal hydrides.

Cited by 5 publications

References 49 publications

Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives

Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives

Advancing the thermodynamic modeling of multicomponent phases in hydrogen-para-equilibrium

Thermodynamic assessment of the Ce H and CeNi5 H system

Contact Info

Product

Resources

About

Underlying factors of mega pressure hysteresis in cerium-rich CaCu5-type metal hydrides and effective modification strategies

Abstract: Strong elastic deformation resistance and an intrinsic stable energetic state contribute to the mega pressure hysteresis in Ce-rich CaCu5-type metal hydrides.

Cited by 5 publications

References 49 publications

Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives

Machine Learning in Solid‐State Hydrogen Storage Materials: Challenges and Perspectives

Advancing the thermodynamic modeling of multicomponent phases in hydrogen-para-equilibrium

Thermodynamic assessment of the Ce H and CeNi5 H system

Contact Info

Product

Resources

About

Underlying factors of mega pressure hysteresis in cerium-rich CaCu₅-type metal hydrides and effective modification strategies