The Effects of Small Amounts of Cobalt in LiNi<sub>1−x</sub>Co<sub>x</sub>O<sub>2</sub> on Lithium-ion Diffusion
Bibliographic record
Abstract
Cobalt substitution for nickel in the positive electrode material LiNi1-xCoxO2 at 0 ≤ x ≤ 0.10 is investigated to determine the impact of cobalt on Li diffusivity, measured using the Atlung Method for Intercalant Diffusion (AMID) in coin cells. Cobalt was found to have little to no impact on Li diffusivity in the intermediate voltage range (4.2 V to 3.7 V). At high voltage (4.3 V to 4.2 V), 0 to 10% cobalt incrementally suppresses the H2–H3 phase transition and enables faster lithium diffusion. Additionally, at low voltage in the kinetic hindrance region (3.7 V to 3.0 V) cobalt can improve lithium diffusion by reducing cation mixing (nickel in the lithium layer). However, cation mixing can also be minimized through synthesis conditions, improving diffusivity without using cobalt. Cobalt was found to have minimal impact on the following material properties of LiNi1-xCoxO2: crystallinity, surface impurities, particle size, and electronic conductivity. Cobalt substituted for nickel from 0% to 10% was found to decrease first cycle discharge capacity in the voltage range between 4.3 V to 3.0 V and improve capacity retention in coin cell cycling vs Li metal negative electrodes. The latter impact is most likely due to the suppression of the H2–H3 phase transition as Co is added.
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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 | 0.001 |
| 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 | 0.000 |
| 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".