Regulation of Anion Redox Activity via Solid‐Acid Modification for Highly Stable Li‐Rich Mn‐Based Layered Cathodes
Bibliographic record
Abstract
Abstract Anionic redox activity can trigger structural instability in Li‐rich Mn‐based cathodes. Lattice oxygen activity can be tuned through liquid acid‐induced spinel phases and oxygen vacancies. However, the liquid‐acid‐modified surface is still attacked by the electrolyte. Besides, the underlying mechanism of spinel phase suppression of lattice oxygen activity is controversial. Here, a solid acid strategy for modification is proposed and the underlying mechanism is investigated in detail. Unique solid acid can in situ generate an interface protection layer and remarkably stabilize the structure. Theoretical calculations and experimental characterizations reveal that the spinel phase suppresses the irreversible loss of lattice oxygen by decreasing the O 2p non‐bonding energy level and enriching electrons at the layered/spinel phase interface. The inert layer on the surface prevents highly active O n− from being attacked by electrolytes. The obtained material exhibits significantly reduced irreversible lattice oxygen release and improved electrochemical performance. After 300 cycles, a slow capacity fading of 0.177 mAh g −1 per cycle and suppressed voltage fading are achieved. This study reveals the regulation method and mechanism for the anion activity of oxide cathodes in next‐generation Li‐ion 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) | 0.000 | 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".