Evidence map›Paper›PMID 42653834›Full record

ReviewMolecules (Basel, Switzerland)2026

Review of the Applications of Density Functional Theory Calculations in Lithium-Sulfur Batteries.

Ang Yu, Yingjie Ji, Guangrun Hu, Qi Zhang, Zhaodi Wang, Yi Zhang

Abstract readReview
In one paragraph

Review in Molecules (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ang YuSchool of Energy Sciences and Engineering, Nanjing Tech University, Nanjing 211816, China.
Yingjie JiSchool of Energy Sciences and Engineering, Nanjing Tech University, Nanjing 211816, China.
Guangrun HuSchool of Energy Sciences and Engineering, Nanjing Tech University, Nanjing 211816, China.
Qi ZhangCollege of Electronic and Information Engineering, Anshun University, Anshun 561000, China.ORCID 0000-0002-5486-9815
Zhaodi WangKey Laboratory for Light-Weight Materials, Nanjing Tech University, Nanjing 211816, China.
Yi ZhangSchool of Energy Sciences and Engineering, Nanjing Tech University, Nanjing 211816, China.ORCID 0000-0002-1603-8623

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lithium-sulfur (Li-S) batteries, featuring a superior theoretical energy density of 2600 Wh/kg and a high specific capacity of 1675 mAh/g for sulfur cathodes, have emerged as a promising candidate for next-generation, high-energy-density energy storage technologies. Nevertheless, their commercialization has been hindered by some bottlenecks, including the polysulfide (LiPSs) shuttle effect, severe volume expansion, and sluggish reaction kinetics. Density Functional Theory (DFT), serving as an atomic-scale computational tool, offers essential theoretical assistance for clarifying the mechanisms and guiding the precision design of S cathode of Li-S batteries. This review focuses on the role of DFT in the mechanism of polysulfide conversion and the shuttle effect. By simulating the adsorption energy, charge density distribution, and reaction pathways of LiPSs using DFT calculations, the key reaction steps of polysulfide conversion can be clearly identified. In addition, DFT calculations also play a vital role in the inhibition of lithium dendrites and the regulation of the solid-electrolyte interphase (SEI) film. This review suggests that DFT calculations could provide more applications in the development of Li-S batteries.

Indexed as

density functional theoryelectrodelithium-sulfur batteriespolysulfides

Identifiers

PMID42653834
PMCPMC13515066

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.