Regulation of Surface Defect Chemistry toward Stable Ni‐Rich Cathodes
Bibliographic record
Abstract
Surface reconstruction of Ni-rich layered oxides (NLO) degrades the cycling stability and safety of high-energy-density lithium-ion batteries (LIBs), which challenges typical surface-modification approaches to build a robust interface with electrochemical activity. Here, a strategy of leveraging the low-strain analogues of Li- and Mn-rich layered oxides (LMR) to reconstruct a stable surface on the Ni-rich layered cathodes is proposed. The new surface structure not only consists of a gradient chemical composition but also contains a defect-rich structure regarding the formation of oxygen vacancies and cationic ordering, which can simultaneously facilitate lithium diffusion and stabilize the crystal structure during the (de)lithiation. These features in the NLO lead to a dramatic improvement in electrochemical properties, especially the cyclability under high voltage cycling, exhibiting the 30% increase in capacity retention after 200 cycles at the current density of 1 C (3.0-4.6 V). The findings offer a facile and effective way to regulate defect chemistry and surface structure in parallel on Ni-rich layered structure cathodes to achieve high-energy density 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 | 0.000 | 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) | 0.004 | 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".