Computational Studies of Electrode Materials in Sodium‐Ion Batteries-Reference-Cited by-同舟云学术

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Computational Studies of Electrode Materials in Sodium‐Ion Batteries

Published:2018-03-12 Issue:17 Volume:8 Page:1702998
ISSN:1614-6832
Container-title:Advanced Energy Materials
language:en
Short-container-title:Adv. Energy Mater.

Author:

Bai Qiang¹,Yang Lufeng²,Chen Hailong²,Mo Yifei¹³^ORCID

Affiliation:

1. Department of Materials Science and Engineering University of Maryland College Park MD 20742 USA

2. The Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA 30332 USA

3. Maryland Energy Innovation Institute University of Maryland College Park MD 20742 USA

Funder

Office of Naval Research

U.S. Department of Energy

National Science Foundation

China Scholarship Council

Publisher

Wiley

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aenm.201702998

Reference287 articles.

1. Electrical Energy Storage for the Grid: A Battery of Choices

2. Room-temperature stationary sodium-ion batteries for large-scale electric energy storage

3. Recent advances in rechargeable battery materials: a chemist’s perspective

4. Electrode Materials for Rechargeable Sodium-Ion Batteries: Potential Alternatives to Current Lithium-Ion Batteries

5. Research Development on Sodium-Ion Batteries

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1. Encapsulation of Se into Cu-decorated MXene nanosheets with superior electrochemical performance for advanced Na-Se batteries;Journal of Colloid and Interface Science;2025-01

2. Hybrid of sub-1 nm single-dispersed polyoxometalate and reduced graphene oxide as advanced anodes for durable sodium-ion batteries;Journal of Power Sources;2024-10

3. Surface Gradient Desodiation Chemistry in Layered Oxide Cathode Materials;Angewandte Chemie;2024-09-10

4. Surface Gradient Desodiation Chemistry in Layered Oxide Cathode Materials;Angewandte Chemie International Edition;2024-09-10

5. Confluence of electronic structure calculations (DFT) and machine learning (ML) for lithium and sodium-ion batteries: a theoretical perspective;Engineering Research Express;2024-08-30

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