Synthesis of LiNi<sub>0.6</sub>Co<sub>0.2</sub>Mn<sub>0.2</sub>O<sub>2</sub> Using Supercritical Carbon Dioxide as a Cathode Material for Lithium‐Ion Batteries
Bibliographic record
Abstract
Abstract A supercritical carbonation method for the preparation of lithium‐ion battery precursor (Ni0.6Co0.2Mn0.2CO3) is proposed. With appropriate selection of reaction temperature, pressure and pH, a desirable stoichiometry of transition metal ratio (6 : 2 : 2) can be achieved. The precursor synthesis time plays a significant role in the battery performance as the transition metal ion distributions become more uniform with increasing reaction time. This results in a more stable layered structure, lower cation mixing level, higher electrochemical reversibility, and lower impedance. The battery made of the precursor prepared under the best synthesis conditions can achieve a discharge capacity of 174.2 mAh g−1 in the first cycle and a capacity retention of 93.0 % after 50 cycles using a current density of 60 mA g−1. This study proves the feasibility of using supercritical carbon dioxide as the reagent for the coprecipitation of lithium‐ion battery precursor Ni0.6Co0.2Mn0.2CO3. Furthermore, this method can possibly be applied to the synthesis of other carbonate‐based materials.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".