Enhanced Charge-Transfer Kinetics Enabled by ZrO <sub>2</sub> –Based Dielectric Layers in Lithium-Ion Batteries
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The development of high-rate capability lithium-ion batteries (LIBs) requires suppression of charge-transfer resistance ( R CT ) at electrode–electrolyte interfaces. Here, zirconia-based dielectric oxides (MZ; M = Y, Gd, Sm, Er, etc.) were introduced onto LiCoO 2 (LCO) surfaces as electronically and ionically insulating modifiers to accelerate interfacial ion transport. Electrochemical impedance spectroscopy showed that Y 2 O 3 modified ZrO 2 (YZ) decoration reduced R CT from 75.8 Ω in reference LCO to 38.3 Ω, accompanied by a 2.3-fold improvement in capacity retention at 20C. Density functional theory molecular dynamics (DFT–MD) simulations showed that solvated Li ions coordinate with surface oxygen atoms in discharging, and that adsorption energies are governed by local charge distributions determined by stabilizing cations. Optimal adsorption activity, and thus the lowest R CT, occurred when the surface charge corrugation was balanced. These findings provide design principles for dielectric interface engineering to enhance rate capability of LIBs.
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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 | 0.000 |
| 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".