Molybdenum‐Doped Cobalt‐Free Cathode Realizing the Electrochemical Stability by Enhanced Covalent Bonding
Bibliographic record
Abstract
Abstract The doping strategy effectively enhances the capacity and cycling stability of cobalt‐free nickel‐rich cathodes. Understanding the intrinsic contributions of dopants is of great importance to optimize the performances of cathodes. This study investigates the correlation between the structure modification and their performances of Mo‐doped LiNi 0.8 Mn 0.2 O 2 (NM82) cathode. The role of doped Mo's valence state has been proved functional in both lattice structural modification and electronic state adjustment. Although the high‐valence of Mo at the cathode surface inevitably reduces Ni valence for electronic neutrality and thus causes ion mixing, the original Mo valence will influence its diffusion depth. Structural analyses reveal Mo doping leads to a mixed layer on the surface, where high‐valence Mo forms a slender cation mixing layer, enhancing structural stability and Li‐ion transport. In addition, it is found that the high‐valence dopant of Mo 6+ ions partially occupies the unfilled 4d orbitals, which may strengthen the Mo─O bond through increased covalency and therefore reduce the oxygen mobility. This results in an impressive capacity retention (90.0% after 200 cycles) for Mo‐NM82 cathodes with a high Mo valence state. These findings underscore the valence effect of doping on layered oxide cathode performance, offering guidance for next‐generation cathode development.
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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 | 0.000 |
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".