Impact of the Synthesis Conditions on the Performance of LiNi<sub>x</sub>Co<sub>y</sub>Al<sub>z</sub>O<sub>2</sub> with High Ni and Low Co Content
Bibliographic record
Abstract
One way to lower the cost of lithium ion batteries using LiNi x Mn y Co z O 2 (NMC) or LiNi 0.80 Co 0.15 Al 0.05 O 2 is to lower the Co content in the positive electrode materials. This work systematically studied the impact of the synthesis conditions on the performance of LiNi x Co y Al z O 2 (x ≥ 0.8, z ≤ 0.05 and x + y + z = 1) (NCA) with high Ni and low Co content. The impacts of oxygen flow rate, sintering temperature, initial Li/TM ratio and sintering time on the structural and electrochemical properties of NCA were systematically studied. The conditions when impurity phases such as Li 2 CO 3 and Li 5 AlO 4 appear were carefully examined. The impact of residual lithium compounds on the electrochemical performance was also discussed. It was found that the synthesis conditions, which affect the a-axis, c-axis and Ni Li content of the NCA samples, have strong impacts on the lithium diffusion at low and high states of charge based on differential capacity vs. voltage (dQ/dV vs. V) measurements at various rates and temperatures. Additionally, the reversible capacity and cycling stability correlated strongly with the intensity of the dQ/dV vs V peak at 3.5 V (discharge) measured with a C/20 rate at 30°C.
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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 | 0.000 |
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".