Atomistic origins of asymmetric charge-discharge kinetics in off-stoichiometric LiNiO$_2$
Bibliographic record
Abstract
LiNiO$_2$ shows poor Li transport kinetics at the ends of charge and discharge in the first cycle, which significantly reduces its available capacity in practice. The atomistic origins of these kinetic limits have not been fully understood. Here, we examine Li transport in LiNiO$_2$ by first-principles-based kinetic Monte Carlo simulations where both long time scale and large length scale are achieved, enabling direct comparison with experiments. Our results reveal the rate-limiting steps at both ends of the voltage scan and distinguish the differences between charge and discharge at the same Li content. The asymmetric effects of excess Ni in the Li layer (Ni$_\textrm{Li}$) are also captured in our unified modelling framework. In the low voltage region, the first cycle capacity loss due to high overpotential at the end of discharge is reproduced without empirical input. While the Li concentration gradient is found responsible for the low overpotential during charge at this state of charge. Ni$_\textrm{Li}$ increases the overpotential of discharge but not 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 the amount of Ni$_\textrm{Li}$ and temperature agree with experiments. In the high voltage region, charge becomes the slower process. The bottleneck becomes moving a Li from the Li-rich phase (H2) into the Li-poor phase (H3), while the Li hopping barriers in both phases are relatively low. The roles of preexisting nucleation sites and Ni$_\textrm{Li}$ are discussed. These results provide new atomistic insights of the kinetic hindrances, paving the road to unleash the full potential of high-Ni layered oxide cathodes.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".