The Effects of Small Amounts of Cobalt in LiNi<sub>1-X</sub>Co<sub>x</sub>O<sub>2</sub> on Lithium Diffusion
Bibliographic record
Abstract
Lithium-ion batteries with Co-free Ni-rich cathodes have attracted interest due to lower cost and supply chain issues compared to cobalt-containing cathodes1. However, cobalt is thought to provide some benefits in cycling stability and rate performance. In this work, cobalt substitution for nickel in the positive electrode material LiNi1-xCoxO2 at 0 ≤ x ≤ 0.10 is systematically investigated to determine the impact of Co and material synthesis conditions on Li diffusivity, measured using the Atlung Method for Intercalant Diffusion (AMID)2,3 in coin cells vs Li metal negative electrodes. Cobalt was found to have no impact on Li diffusivity in the intermediate voltage range (4.2 V to 3.7 V), while cation mixing (%Ni in Li layer) was found to have a strong correlation to slow Li diffusivity. At high voltage (4.3 V to 4.2 V), 0 to 10% cobalt incrementally suppresses the H2-H3 phase transition4,5 enabling significantly faster lithium diffusion. Cation mixing can be minimized through synthesis conditions, improving Li diffusivity for the low and intermediate voltage regions, without using Co. In summary, with optimal positive electrode material synthesis conditions and limiting cell upper cutoff voltage to 4.2 V, Co-free Ni-rich materials can be made with similar rate performance as 10% Cobalt-containing materials. Additionally, cobalt was found to have minimal impact on the following material properties: crystallinity of LiNi1-xCoxO2, 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 3.0 V and 4.3 V and improve capacity retention in coin cell cycling vs Li metal negative electrodes. The capacity retention improvement is most likely due to the suppression of the H2-H3 phase transition as Co is added5. Figure 1. (a), (b) shows %Ni in Li layer vs Cobalt content, obtained from powder XRD Rietveld refinement from a series of LiNi1-xCoxO2 materials. (a), (b) were synthesized under two different O2 flow velocities, both at 700oC for 20 hours, with a Li:TM ratio of 1.02:1. (c), (d) Show the lithium chemical diffusion coefficient vs cell voltage for the materials from (a), (b), respectively, measured using the Atlung Method for Intercalant Diffusion (AMID), in coin cells at 30oC. The protocol consists of 2C to C/160-rate discharge steps per voltage interval (0.1V), with OCV relaxation in between discharge steps. Figure 1
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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 | 0.000 |
| 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".