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 LiNi 0.5 Co 0.2 Mn 0.3 O 2 (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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".