Regulation of Interface Ion Transport by Electron Ionic Conductor Construction toward High‐Voltage and High‐Rate LiNi<sub>0.5</sub>Co<sub>0.2</sub>Mn<sub>0.3</sub>O<sub>2</sub> Cathodes in Lithium Ion Battery
Bibliographic record
Abstract
Abstract Simultaneously achieving high‐energy‐density and high‐power‐density is a crucial yet challenging objective in the pursuit of commercialized power batteries. In this study, atomic layer deposition (ALD) is employed combined with a coordinated thermal treatment strategy to construct a densely packed, electron‐ion dual conductor (EIC) protective coating on the surface of commercial LiNi0.5Co0.2Mn0.3O2 (NCM523) cathode material, further enhanced by gradient Al doping (Al@EIC‐NCM523). The ultra‐thin EIC effectively suppresses side reactions, thereby enhancing the stability of the cathode‐electrolyte interphase (CEI) at high‐voltages. The EIC's dual conduction capability provides a potent driving force for Li+ transport at the interface, promoting the formation of rapid ion deintercalation pathways within the Al@EIC‐NCM523 bulk phase. Moreover, the strategic gradient doping of Al serves to anchor the atomic spacing of Ni and O within the structure of Al@EIC‐NCM523, curbing irreversible phase transitions at high‐voltages and preserving the integrity of its layered structure. Remarkably, Al@EIC‐NCM523 displays an unprecedented rate capability (114.7 mAh g−1 at 20 C), and a sustained cycling performance (capacity retention of 74.72% after 800 cycles at 10 C) at 4.6 V. These findings demonstrate that the proposed EIC and doping strategy holds a significant promise for developing high‐energy‐density and high‐power‐density lithium‐ion batteries (LIBs).
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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".