Effective transport network driven by tortuosity gradient enables high-electrochem-active solid-state batteries
Bibliographic record
Abstract
Simultaneously achieving high electrochemical activity and high loading for solid-state batteries has been hindered by slow ion transport within solid electrodes, in particular with an increase in electrode thickness. Ion transport governed by 'point-to-point' diffusion inside a solid-state electrode is challenging, but still remains elusive. Herein, synchronized electrochemical analysis using X-ray tomography and ptychography reveals new insights into the nature of slow ion transport in solid-state electrodes. Thickness-dependent delithiation kinetics are spatially probed to identify that low-delithiation kinetics originate from the high tortuous and slow longitudinal transport pathways. By fabricating a tortuosity-gradient electrode to create an effective ion-percolation network, the tortuosity-gradient electrode architecture promotes fast charge transport, migrates the heterogeneous solid-state reaction, enhances electrochemical activity and extends cycle life in thick solid-state electrodes. These findings establish effective transport pathways as key design principles for realizing the promise of solid-state high-loading 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 | 0.000 | 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".