Scalable Precise Nanofilm Coating and Gradient Al Doping Enable Stable Battery Cycling of LiCoO<sub>2</sub> at 4.7 V
Bibliographic record
Abstract
Abstract The quest for smart electronics with higher energy densities has intensified the development of high‐voltage LiCoO 2 (LCO). Despite their potential, LCO materials operating at 4.7 V faces critical challenges, including interface degradation and structural collapse. Herein, we propose a collective surface architecture through precise nanofilm coating and doping that combines an ultra‐thin LiAlO 2 coating layer and gradient doping of Al. This architecture not only mitigates side reactions, but also improves the Li + migration kinetics on the LCO surface. Meanwhile, gradient doping of Al inhibited the severe lattice distortion caused by the irreversible phase transition of O3−H1−3−O1, thereby enhanced the electrochemical stability of LCO during 4.7 V cycling. DFT calculations further revealed that our approach significantly boosts the electronic conductivity. As a result, the modified LCO exhibited an outstanding reversible capacity of 230 mAh g −1 at 4.7 V, which is approximately 28 % higher than the conventional capacity at 4.5 V. To demonstrate their practical application, our cathode structure shows improved stability in full pouch cell configuration under high operating voltage. LCO exhibited an excellent cycling stability, retaining 82.33 % after 1000 cycles at 4.5 V. This multifunctional surface modification strategy offers a viable pathway for the practical application of LCO materials, setting a new standard for the development of high‐energy‐density and long‐lasting electrode materials.
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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".