Recent progress in application of cobalt‐based compounds as anode materials for high‐performance potassium‐ion batteries
Bibliographic record
Abstract
Abstract Potassium‐ion batteries (KIBs) are regarded as one of the most promising replacements for lithium‐ion batteries because of their low cost and high performance. Exploring suitable anode materials to stably and effectively store potassium is critical for the development of KIBs. Given their high theoretical specific capacity, cobalt‐based compounds have been extensively investigated as an anode material in recent years; however, specific reviews summarizing the research progress in the application of cobalt‐based compounds as anode materials for high‐performance KIBs are lacking. Consequently, this review systematically summarizes the recent states of cobalt‐based anode materials in KIBs starting at the potassium storage mechanism, followed by strategies and applications to improve the electrochemical performance. The current challenges are also discussed, and corresponding prospects are proposed. This work may facilitate the realization of various applications of cobalt‐based compound anodes for high‐performance rechargeable batteries and is expected to provide some guidance for developing other metal‐based compounds for KIBs anodes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".