Decoupling the Voltage Hysteresis of Li‐Rich Cathodes: Electrochemical Monitoring, Modulation Anionic Redox Chemistry and Theoretical Verifying
Bibliographic record
Abstract
Abstract Cathodes in lithium‐ion batteries with anionic redox can deliver extraordinarily high specific capacities but also present many issues such as oxygen release, voltage hysteresis, and sluggish kinetics. Identifying problems and developing solutions for these materials are vital for creating high‐energy lithium‐ion batteries. Herein, the electrochemical and structural monitoring is conducted on lithium‐rich cathodes to directly probe the formation processes of larger voltage hysteresis. These results indicate that the charge‐compensation properties, structural evolution, and transition metal (TM) ions migration vary from oxidation to reduction process. This leads to huge differences in charge and discharge voltage profile. Meanwhile, the anionic redox processes display a slow kinetics process with large hysteresis (≈0.5 V), compared to fast cationic redox processes without any hysteresis. More importantly, a simple yet effective strategy has been proposed where fine‐modulating local oxygen environment by the lithium/oxygen (Li/O) ratio tunes the anionic redox chemistry. This effectively improves its electrochemical properties, including the operating voltage and kinetics. This is also verified by theoretical calculations that adjusting anionic redox chemistry by the Li/O ratio shifts the TM 3d—O 2p bands and the non‐bonding O 2p band to a lower energy level, resulting in a higher redox reaction potential.
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) | 0.000 | 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 | 0.000 |
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".