Tuning Transition Metal Oxide–Sulfur Interactions for Long Life Lithium Sulfur Batteries: The “Goldilocks” Principle
Bibliographic record
Abstract
The lithium‐sulfur battery is a compelling energy storage system because its high theoretical energy density exceeds Li‐ion batteries at much lower cost, but applications are thwarted by capacity decay caused by the polysulfide shuttle. Here, proof of concept and the critical metrics of a strategy to entrap polysulfides within the sulfur cathode by their reaction to form a surface‐bound active redox mediator are demonstrated. It is shown through a combination of surface spectroscopy and cyclic voltammetry studies that only materials with redox potentials in a targeted window react with polysulfides to form active surface‐bound polythionate species. These species are directly correlated to superior Li‐S cell performance by electrochemical studies of high surface area oxide cathodes with redox potentials below, above, and within this window. Optimized Li‐S cells yield a very low fade rate of 0.048% per cycle. The insight gained into the fundamental surface mechanism and its correlation to the stability of the electrochemical cell provides a bridge between mechanistic understanding and battery performance essential for the design of high performance Li‐S cells.
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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 | 0.001 |
| 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".