Improvement of long-term cycling performance of high-nickel cathode materials by ZnO coating
Bibliographic record
Abstract
Abstract The high-nickel cathode material of LiNi 0.8 Co 0.15 Al 0.05 O 2 (LNCA) has a prospective application for lithium-ion batteries due to the high capacity and low cost. However, the side reaction between the electrolyte and the electrode seriously affects the cycling stability of lithium-ion batteries. In this work, Ni 2+ preoxidation and the optimization of calcination temperature were carried out to reduce the cation mixing of LNCA, and solid-phase Al-doping improved the uniformity of element distribution and the orderliness of the layered structure. In addition, the surface of LNCA was homogeneously modified with ZnO coating by a facile wet-chemical route. Compared to the pristine LNCA, the optimized ZnO-coated LNCA showed excellent electrochemical performance with the first discharge-specific capacity of 187.5 mA h g −1 , and the capacity retention of 91.3% at 0.2C after 100 cycles. The experiment demonstrated that the improved electrochemical performance of ZnO-coated LNCA is assigned to the surface coating of ZnO which protects LNCA from being corroded by the electrolyte during cycling.
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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".