Electron and Ion Co‐Conductive Catalyst Achieving Instant Transformation of Lithium Polysulfide towards Li<sub>2</sub>S
Bibliographic record
Abstract
Abstract Most of the catalysts in lithium sulfur (Li–S) batteries present low electronic conductivity and the lithium polysulfides (LiPSs) must diffuse onto the surface of the carbon materials to achieve their conversion reaction. It is a significant challenge to achieve the instantaneous transformation of LiPSs to Li 2 S in Li–S batteries to suppress the shuttle effect of LiPSs. Herein, a unique electron and ion co‐conductive catalyst of carbon‐coated Li 1.4 Al 0.4 Ti 1.6 (PO 4 ) 3 (C@LATP) is developed, which not only possesses strong adsorption to LiPSs, but, more importantly, also promotes the instantaneous conversion reaction of LiPSs to Li 2 S. The C@LATP nanoparticles as catalytic active sites can synchronously and efficiently provide both Li ions and electrons to facilitate the conversion reaction of LiPSs. The conversion reaction path of LiPSs using C@LATP changes from traditional “adsorption–diffusion–conversion” to novel “adsorption–conversion,” which effectively lowers the decomposition barrier of Li 2 S 6 and promotes faster conversion of LiPSs. The shuttle effect of LiPSs is considerably suppressed and utilization of sulfur is greatly improved. The Li–S batteries using C@LATP present excellent rate, cycling, and self‐discharge properties. This work highlights the significance of electron and ion co‐conductive solid‐state electrolytes for the instantaneous transformation of LiPSs in advanced 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 | 0.000 |
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".