Promoting the Transformation of Li<sub>2</sub>S<sub>2</sub> to Li<sub>2</sub>S: Significantly Increasing Utilization of Active Materials for High‐Sulfur‐Loading Li–S Batteries
Bibliographic record
Abstract
Abstract Lithium–sulfur (Li–S) batteries with high sulfur loading are urgently required in order to take advantage of their high theoretical energy density. Ether‐based Li–S batteries involve sophisticated multistep solid–liquid–solid–solid electrochemical reaction mechanisms. Recently, studies on Li–S batteries have widely focused on the initial solid (sulfur)–liquid (soluble polysulfide)–solid (Li2S2) conversion reactions, which contribute to the first 50% of the theoretical capacity of the Li–S batteries. Nonetheless, the sluggish kinetics of the solid–solid conversion from solid‐state intermediate product Li2S2 to the final discharge product Li2S (corresponding to the last 50% of the theoretical capacity) leads to the premature end of discharge, resulting in low discharge capacity output and low sulfur utilization. To tackle the aforementioned issue, a catalyst of amorphous cobalt sulfide (CoS3) is proposed to decrease the dissociation energy of Li2S2 and propel the electrochemical transformation of Li2S2 to Li2S. The CoS3 catalyst plays a critical role in improving the sulfur utilization, especially in high‐loading sulfur cathodes (3–10 mg cm−2). Accordingly, the Li2S/Li2S2 ratio in the discharge products increased to 5.60/1 from 1/1.63 with CoS3 catalyst, resulting in a sulfur utilization increase of 20% (335 mAh g−1) compared to the counterpart sulfur electrode without CoS3.
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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) | 0.002 | 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".