A global design principle for polysulfide electrocatalysis in lithium–sulfur batteries—A computational perspective
Bibliographic record
Abstract
Abstract Widespread commercialization of high‐energy‐density lithium–sulfur (Li–S) batteries is difficult due to the lithium polysulfide, Li 2 S n ( n = 4, 6, 8), shuttle effect. Efficient adsorption/conversion of Li 2 S n species on an electrocatalytic surface can suppress the shuttle effect. Modeling of the adsorption of Li 2 S n species using density functional theory (DFT) calculations has contributed significantly toward an understanding of their anchoring mechanism at a surface. Different surfaces show a unique range of binding energies for faster Li 2 S n adsorption/reaction kinetics. To predict the optimum binding energy zone, a systematic DFT study is performed on transition‐metal sulfide (TMS) surfaces including TiS 2 , VS 2 , NbS 2 , MoS 2 , WS 2 , and SnS 2 . The investigation revealed that the geometric properties at the anchoring site possibly regulate the adsorption energy of Li 2 S n species. A geometry parameter, G score , is defined as a function of bond length and number of lithium‐atom interactions between the Li 2 S n species and the binding surface. The design principle is extended to sulfur‐deficient (TMSs‐x) and edge‐exposed (TMS(100)) surfaces. The G score predicts the most effective binding energy zone distinctive to these materials—TMS (1.7–2.1 eV/ G score ≥ 2.0), TMSs‐x (2.0–2.8 eV/ G score ≥ 2.1), and TMS(100) (2.5–3.2 eV/G score ≥ 1.09).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".