Improving High-Temperature Cycle Stability and Rate Performance of LiNi0.82Co0.11Mn0.07O2 Cathode Materials Using Hydrogen Peroxide Solution Washing System
Bibliographic record
Abstract
In this study, for the removal of residual lithium (Li2CO3,LiOH) from a nickel-rich cathode material surface, LiNi0.82Co0.11Mn0.07O2 cathode materials were washed with an aqueous solution of hydrogen peroxide (H2O2).H2O2 ( pH6.04), a weak acid, can easily decompose Li2CO3 and LiOH as an oxidizing agent. On titration of residual lithium, the amounts of LiOH and Li2CO3 are 390 and 605ppm,25 - and 47 -times lower, after H2O2 washing compared to 10,296 and 28,440ppm, respectively, in case of cathode materials before washing. On DSC thermal analysis, the peak temperature and calorific value of the cathode material washed with H2O2 were 245.5∘C and 602.0 J/g, respectively, whereas the bare case was 208.6∘C and 1,071 J/g, respectively. Therefore, H2O2-washed LiNi0.82Co0.11Mn0.07O2 cathode materials had higher capacity heat retention after 100 cycles at 55∘C(85.6% at 0.5C) than the bare LiNi0.82Co0.11Mn0.07O2 cathode materials (78.6% at 0.5C).
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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 | 0.001 | 0.001 |
| 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".