Atomistic Origins of Asymmetric Charge–Discharge Kinetics in Off-Stoichiometric LiNiO<sub>2</sub>
Bibliographic record
Abstract
LiNiO 2 and Ni-rich layered oxide cathodes exhibit slow Li transport at both ends of the charge and discharge processes, significantly reducing their practical capacity. The atomistic origins of these kinetic limits have not been fully understood. Here, we investigate Li transport by first-principles-based kinetic Monte Carlo simulations to achieve a large length scale and long time scale, enabling direct comparison with experiments. Our results explain the asymmetric overpotentials between charge and discharge at the same Li content and reveal different rate-limiting steps at both ends of the first cycle. The asymmetric effects of excess Ni in the Li layer (Ni Li ) are also captured. At low voltages, the first cycle irreversible capacity loss at the end of discharge is reproduced without empirical input. The concentration-dependent Li hopping barrier is explained by interactions beyond the first nearest neighbor. Ni Li increases the overpotential during discharge but not during charge because it only impedes Li diffusion in a particular range of Li concentration and does not change the equilibrium voltage profile. The trends from varying Ni Li content and temperature are consistent with the experimental observations. At high voltages, charge becomes the slower process. The transport bottleneck is moving Li from the Li-rich H2 phase into the Li-poor H3 phase, while the Li diffusion barriers in both phases are relatively low. The roles of pre-existing nucleation sites and Ni Li are also discussed. These results provide new atomistic insights into the kinetic hindrances, paving the road to unleash the full potential of high-Ni layered oxides.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".