Reducing structural degradation of high‐voltage single‐crystal Ni‐rich cathode through in situ doping strategy
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Bibliographic record
Abstract
Abstract Polycrystalline Ni‐rich layered oxide (LiNi x Co y Mn z O 2 (NCM), x > 0.8) cathode material with high specific capacity and low cost is considered as one of the most promising candidate materials for lithium‐ion batteries (LIBs). However, it suffers from severe structural and capacity degradation during practical cycling, especially under harsh operation condition (ultrahigh cutoff voltage and elevated temperature, etc.). One promising approach to mitigate these issues is to develop a single‐crystal Ni‐rich NCM cathode, which could enhance structural integrity and improve capacity retention, due to its robust and stable micro‐sized primary particles. However, the improved cyclic stability comes at the expense of reversible capacity and rate capability, owing to the relatively low Li + diffusion efficiency for its micron‐sized primary particles. Moreover, the structural degradation and exacerbation of interfacial reactions for the Ni‐rich NCM cathode under high‐voltage (≥ 4.5 V) would quickly trigger the poor electrochemical performance, limiting its practical applications. Herein, LiNi 0.827 Co 0.11 Zr 0.003 Mn 0.06 O 2 (Zr@SC‐N 83 ) cathode material was successfully synthesized via the in situ doping strategy. It could not only effectively maintain the reversibility of phase transition between H2 and H3 after long‐term cycling at high voltage (4.6 V), but also enhance lithium‐ion diffusion, thus improving the cycling performance and good rate performance for the Zr@SC‐N 83 cathode. As a result, 0.3 wt% Zr‐doping cathode delivers an initial discharging capacity of 200.1 mAh·g −1 at 1.0C and at the high cutoff voltage of 4.6 V, exhibiting the satisfactory capacity retention of 85.5% after 100 cycles. It provides an effective route toward low‐cost and higher energy density for lithium‐ion batteries with Ni‐rich cathode.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it