Study of lithium transport in Li2O component of the solid electrolyte interphase in lithium-ion batteries
Bibliographic record
Abstract
In lithium-ion batteries (LIBs), as a promising energy storage device, materials with fast lithium (Li) transport are required for high-power applications such as electric vehicles. Thus, a deeper understanding of Li transport in components of LIBs is crucial for improving their rate capability. In this study, the Li transport in lithium oxide (Li2O), as one of the key components of the solid electrolyte interphase (SEI) layer, was examined by a multiscale computational approach ranging from density functional theory (DFT) to Monte Carlo simulations. The DFT calculations were used to investigate the recombination of Frenkel pairs, their first-principles total energies, and the Li diffusion mechanisms. The effect of atomic configurations on both first-principles total energies and diffusion barrier energies was formulated by periodic and local cluster expansions. These formalisms were then incorporated into the Monte Carlo and Kinetic Monte Carlo (KMC) simulations to calculate the diffusion coefficient of Li. Our calculations revealed that the vacancy-mediated jump along the 〈100〉 direction in the antifluorite structure of Li2O possesses the lowest barrier energy compared to other diffusion mechanisms. The KMC simulations indicated that the diffusion coefficient of Li better converged with the direct experimental measurement when the recombination of Frenkel pairs was integrated into the simulations. At a temperature of 300 K, the KMC simulation yielded a Li diffusion coefficient of 3.8×10-12cm2/s in Li2O. This is only one order of magnitude larger than indirect experimental measurement, suggesting the accuracy of our formalism. Thus, our formalism for studying Li transport in Li2O will pave the path to a rational design of inorganic SEI in the future development of LIBs.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".