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Cited by 507

The DOI matches its Crossref record; 147 of the 147 references it cites were found in a registry or an index.

Progress in Mechanistic Understanding and Characterization Techniques of Li‐S Batteries

Xu, Rui, Lu, Jun, Amine, Khalil·2015·Advanced Energy Materials

Abstract

Rechargeable lithium‐sulfur batteries that operate at room temperature have attracted much research interest as next‐generation energy storage systems. Although tremendous advances have been made with Li‐S batteries, great challenges still exist in achieving high capacity, high loading, high coulombic efficiency, and long cycle life. These challenges arise from the system complexity, lack of mechanistic understanding of the redox reaction, and operational limitations of Li‐S cells. The focus here is on the recent gains in fundamental understanding of the Li‐S redox reaction mechanism based on the application of advanced characterization techniques. Research results that help with the understanding of the close relationship between cell design (including development of new and advanced electrode materials, electrolytes, separators, binders, and cell configurations), the Li‐S reaction mechanism, characterization methods, and Li‐S battery performance are discussed.

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Metadata

Authors
Xu, R., Lu, J., & Amine, K.
Venue
Advanced Energy Materials
Publisher
Wiley
Published
2015-05-26
Volume
5
Issue
16
ISSN
  • 1614-6832 (ISSN-L)
  • 1614-6840
OpenAlex
W2159050554
Cited by
507
References
147
FWCI
26.94
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Progress in Mechanistic Understanding and Characterization Techniques of Li‐S Batteries. https://app.cochat.ai/papers/10.1002/aenm.201500408
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Xu, R., Lu, J., & Amine, K. (2015). Progress in Mechanistic Understanding and Characterization Techniques of Li‐S Batteries. Advanced Energy Materials. https://doi.org/10.1002/aenm.201500408

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.