Bibliographic record
Abstract
Battery technologies involving Li‐S chemistries have been touted as one of the most promising next generation systems. The theoretical capacity of sulfur is nearly an order of magnitude higher than current Li‐ion battery insertion cathodes and when coupled with a Li metal anode, Li‐S batteries promise specific energies nearly five‐fold higher. However, this assertion only holds if sulfur cathodes could be designed in the same manner as cathodes for Li‐ion batteries. Here, the recent efforts to engineer high capacity, thick, sulfur‐based cathodes are explored. Various works are compared in terms of capacity, areal mass loading, and fraction of conductive additive, which are the critical parameters dictating the potential for a device to achieve a specific energy higher than current Li‐ion batteries (i.e., >200 Wh kg −1 ). While an inferior specific energy is projected in the majority of cases, several promising strategies have the potential to achieve >500 Wh kg −1 . The challenges associated with the limited cycle‐life of these systems due to both the polysulfide shuttle phenomenon and the rapid degradation of the Li metal anode that is experienced at the current densities required to charge high specific energy batteries in a reasonable timeframe are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".