Insights into interfacial effect and local lithium-ion transport in polycrystalline cathodes of solid-state batteries
Bibliographic record
Abstract
Interfacial issues commonly exist in solid-state batteries, and the microstructural complexity combines with the chemical heterogeneity to govern the local interfacial chemistry. The conventional wisdom suggests that "point-to-point" ion diffusion at the interface determines the ion transport kinetics. Here, we show that solid-solid ion transport kinetics are not only impacted by the physical interfacial contact but are also closely associated with the interior local environments within polycrystalline particles. In spite of the initial discrete interfacial contact, solid-state batteries may still display homogeneous lithium-ion transportation owing to the chemical potential force to achieve an ionic-electronic equilibrium. Nevertheless, once the interior local environment within secondary particle is disrupted upon cycling, it triggers charge distribution from homogeneity to heterogeneity and leads to fast capacity fading. Our work highlights the importance of interior local environment within polycrystalline particles for electrochemical reactions in solid-state batteries and provides crucial insights into underlying mechanism in interfacial transport.
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 | 0.001 | 0.001 |
| 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".