Lithium‐Ion Conductive Coatings for Nickel‐Rich Cathodes for Lithium‐Ion Batteries
Bibliographic record
Abstract
Nickel (Ni)-rich cathodes are among the most promising cathode materials of lithium batteries, ascribed to their high-power density, cost-effectiveness, and eco-friendliness, having extensive applications from portable electronics to electric vehicles and national grids. They can boost the wide implementation of renewable energies and thereby contribute to carbon neutrality and achieving sustainable prosperity in the modern society. Nevertheless, these cathodes suffer from significant technical challenges, leading to poor cycling performance and safety risks. The underlying mechanisms are residual lithium compounds, uncontrolled lithium/nickel cation mixing, severe interface reactions, irreversible phase transition, anisotropic internal stress, and microcracking. Notably, they have become more serious with increasing Ni content and have been impeding the widespread commercial applications of Ni-rich cathodes. Various strategies have been developed to tackle these issues, such as elemental doping, adding electrolyte additives, and surface coating. Surface coating has been a facile and effective route and has been investigated widely among them. Of numerous surface coating materials, have recently emerged as highly attractive options due to their high lithium-ion conductivity. In this review, a thorough and comprehensive review of lithium-ion conductive coatings (LCCs) are made, aimed at probing their underlying mechanisms for improved cell performance and stimulating new research efforts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".