Consolidating Lithiothermic‐Ready Transition Metals for Li<sub>2</sub>S‐Based Cathodes
Bibliographic record
Abstract
Abstract Li2S holds a promising role as a high‐capacity Li‐containing cathode, circumventing use of metallic lithium in constructing next‐generation batteries to replace current Li‐ion batteries. However, progress of Li2S cathode has been plagued by its intrinsic drawbacks, including high activation potentials, poor rate performance, and rapid capacity fading during long cycling. Herein, a series of Li2S/transition metal (TM) nanocomposites are synthesized via a lithiothermic reduction reaction, and it is realized that the presence of TMs in Li2S matrix can transform electrochemical behaviors of Li2S. On the one hand, the incorporation of W, Mo, or Ti greatly increases electronic and ionic conductivity of Li2S composites and inhibits the polysulfide dissolution via the TMS bond, effectively addressing the drawbacks of Li2S cathodes. In particular, Li2S/W and Li2S/Mo exhibit the highest ionic conductivity of solid‐phase Li‐ion conductors ever‐reported: 5.44 × 10−2 and 3.62 × 10−2 S m−1, respectively. On the other hand, integrating Co, Mn, and Zn turns Li2S into a prelithiation agent, forming metal sulfides rather than S8 after the full charge. These interesting findings may shed light on the design of Li2S‐based cathode materials.
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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".