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The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.

Combining theory and experiment in lithium–sulfur batteries: Current progress and future perspectives

Open access
Chen, Xiang, Hou, Tingzheng, Persson, Kristin A., Zhang, Qiang·2019·Materials Today

Abstract

Abstract Lithium–sulfur (Li–S) batteries are considered as promising candidates for next-generation energy storage devices due to their ultrahigh theoretical gravimetric energy density, cost-effectiveness, and environmental friendliness. However, the application of Li–S batteries remains challenging, mainly due to a lack of understanding of the complex chemical reactions and associated equilibria occurring in a working Li–S system. In this review, the typical applications of computational chemistry in Li–S battery studies, correlating to characterization techniques, such as X-ray diffraction, infra-red & Raman spectra, X-ray absorption spectroscopy, binding energy, and nuclear magnetic resonance, are reviewed. In particular, high-accuracy calculations and large-scale models, materials genome, and machine-learning approaches are expected to further advance computational design for the development of Li–S batteries and related fields.

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Crossref · 10.1016/j.mattod.2018.04.007

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Metadata

Authors
Chen, X., Hou, T., Persson, K. A., & Zhang, Q.
Venue
Materials Today
Publisher
Elsevier BV
Published
2019-01
Volume
22
Pages
142-158
ISSN
1369-7021 (ISSN-L)
OpenAlex
W2804990541
Cited by
446
References
12
FWCI
25.40
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Combining theory and experiment in lithium–sulfur batteries: Current progress and future perspectives. https://app.cochat.ai/papers/10.1016/j.mattod.2018.04.007
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Chen, X., Hou, T., Persson, K. A., & Zhang, Q. (2019). Combining theory and experiment in lithium–sulfur batteries: Current progress and future perspectives. Materials Today. https://doi.org/10.1016/j.mattod.2018.04.007

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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.