Theoretical Study on the Role of Solvents in Lithium Polysulfide Anchoring on Vanadium Disulfide Facets for Lithium–Sulfur Batteries
Bibliographic record
Abstract
The shuttle effects by lithium polysulfides (LiPSs) and the sluggish reaction kinetics are crucial obstacles in the commercialization of Li–S batteries. Hence, effectively trapping and promoting the conversion of LiPSs is of prime importance. However, the fundamental kinetics of the electrocatalytic charging and discharging of Li–S batteries have not been sufficiently explored yet. Therefore, by taking VS 2 as a model, we conducted a density functional theory-based study to investigate the ability of dominant exposed crystal planes of VS 2 to trap LiPSs from leaching into electrolytes and to act as an electrocatalyst to increase the sulfur reduction reaction (SRR) kinetics. To reflect a realistic environment of a battery, the effect of solvents on the electrocatalytic activity was further investigated. Our calculations show that VS 2 has moderate binding energy toward LiPSs; therefore, it can effectively inhibit LiPS shuttling and leaching. However, there was no consistent pattern for binding energies under different VS 2 facets. Furthermore, VS 2 (001) facets exhibit excellent electrocatalytic activity for the SRR and Li 2 S decomposition reaction compared to other dominant crystal planes, which significantly lowers the energy barriers of LiPS conversion during the charging and discharging process, ensuring high-rate performance and longer cycle life. Beyond the VS 2 systems explored in the current study, the same approach can apply to other potential electrocatalysts as a promising pathway to improve the sluggish reaction kinetics of Li–S batteries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".