The Asymmetric Charge-Discharge Kinetics in Li<sub>1-X</sub>Ni<sub>1+X</sub>O<sub>2</sub> from First Principles
Bibliographic record
Abstract
The ever-increasing demand on Li-ion batteries requires the cathode materials to be inexpensive and environmentally friendly. LiNiO2 is such a promising Co-free cathode. However, the presence of Ni in the Li layer (NiLi) becomes more common in LiNiO2 than its cousin layered compounds, which limits its electrochemical performance. These excess Ni might randomly distribute in the bulk due to Li deficiency in synthesis, or/and form a surface densified phase due to oxygen loss in cycling. This study combines density functional theory (DFT), cluster expansion and kinetic Monte Carlo (KMC) simulations to identify the effects of these defects on Li transport. Both types of NiLi were found to impede Li transport at the end of charge and discharge, but not at the beginning. This asymmetry kinetics cannot be solely explained by the Li diffusivity as a function of Li contents but stems from the phase boundary between Li orderings. NiLi from synthesis smooths the voltage plateaus and contributes to the 1st cycle capacity loss. NiLi from degradation hinders Li transport more severely when the densified phase fully covers the particle surface. Interestingly, during charge the surface phase traps the last 25% Li for an extremely long time but shows little impedance when Li%>25%. 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".