High-density/efficient surface active sites on modified separators to boost Li-S batteries via atomic Co3+-Se termination
Bibliographic record
Abstract
Various and critical electrocatalytic processes are involved during the redox reactions in the Li-S batteries, which extremely depend on the surface structure and chemical state. Recently, single-atom concept unlocks a route to maximize the use of surface-active atoms, however, further increasing the density of active site is still strictly limited by the inherent structure that single-atoms are only highly-dispersed on substrate. Herein, we provide a viewpoint that an elaborate facet design with single-crystalline structure engineering can harvest high-density surface active sites, which can significantly boost the electrocatalyst performance for excellent Li-S batteries. Specifically, the single-crystal CoSe 2 (scCS) exhibits three-types of terminated (011) facet, efficiently obtaining the surface with a high-rich Co 3+ —Se bond termination, in contrast with lots of surface grain boundaries and dangling bonds in polycrystalline CoSe 2 . Surprisingly, the surface active sites concentration can reach more than 69%. As anticipated, it can provide high-density and high-efficient active sites, enormously suppressing the shuttle effect and improving the reaction kinetics via accelerating the conversion and deposition of polysulfides and Li 2 S. This surface lattice strategy with element terminated mode is a promising approach for designing electrocatalyst effect-based energy system, not merely for 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 | 0.000 |
| 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) | 0.001 | 0.001 |
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".