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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".