Realizing High‐Performance Li‐S Batteries through Additive Manufactured and Chemically Enhanced Cathodes
Bibliographic record
Abstract
Abstract Numerous efforts are made to improve the reversible capacity and long‐term cycling stability of Li‐S cathodes. However, they are susceptible to irreversible capacity loss during cycling owing to shuttling effects and poor Li+ transport under high sulfur loading. Herein, a physically and chemically enhanced lithium sulfur cathode is proposed to address these challenges. Additive manufacturing is used to construct numerous microchannels within high sulfur loading cathodes, which enables desirable deposition mechanisms of lithium polysulfides and improves Li+ and e‐ transport. Concurrently, cobalt sulfide is incorporated into the cathode composition and demonstrates strong adsorption behavior toward lithium polysulfides during cycling. As a result, excellent electrochemical performance is obtained by the design of a physically and chemically enhanced lithium sulfur cathode. The reported electrode, with a sulfur loading of 8 mg cm‐2, delivers an initial capacity of 1118.8 mA h g‐1 and a reversible capacity of 771.7 mA h g‐1 after 150 cycles at a current density of 3 mA cm‐2. This work demonstrates that a chemically enhanced sulfur cathode, manufactured through additive manufacturing, is a viable pathway to achieve high‐performance 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".