Review—Surface Coatings for Cathodes in Lithium Ion Batteries: From Crystal Structures to Electrochemical Performance
Bibliographic record
Abstract
Lithium ion batteries (LIBs) have dominated the energy industry due to their unmatchable properties that include a high energy density, a compact design, and an ability to meet a number of required performance characteristics in comparison to other rechargeable systems. Both government agencies and industries are performing intensive research on Li-ion batteries for building an energy-sustainable economy. LIBs are single entities that consist of both organic and inorganic materials with features covering multiple length scales. Two vital parameters for LIBs are their stable and safe operation. Critical insights should be made for understanding the structure to property relationships and the behavior of components under the working condition of LIBs. Since, the cathode serves as a central component of LIBs, the overall cell performance is significantly affected by the chemical and physical properties of the cathode. Cathodes tend to react with the electrolytes and, hence, to undergo surface modifications accompanied by degradation. These side-reactions result in an erosion of battery performance, thereby causing a reduced battery life and power capacity. Recently, techniques for preparing surface coatings on cathode materials have been widely implemented as a measure to improve their stability, to enhance their electrochemical performance, and to prevent detrimental surface reactions between the electrode materials and electrolyte. This review will cover different types of surface coatings for cathode materials, as well as a comparison of the changes in electrochemical performance between those materials with and without an applied coating. In addition, a brief outlook is included for different cathode materials and their coatings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".