Bibliographic record
Abstract
Lithium ion mobility is one of the key properties that all battery materials need to exhibit for a Li-ion battery that functions reversibly and effectively. Within the battery, active materials are responsible for reversible intercalation, that requires the transport of Li-ions within the solid lattice. Outside the active material, the electrolyte shuttles Li-ions between the two electrodes. In between those materials, ion transport phenomena at the interfaces has been reported as having potentially significant impact on battery performance. We are here reporting our efforts in tracking Li+ transport in LiFePO4 and solid electrolyte composites. Having found a significant role of microstructure in ionic transport, this presentation is further dedicated to our recent exploration of microstructure and improved electrode interfaces. Using active microscopy techniques, new methods are being developed in the imaging and quantification of microstructural properties. These techniques are applied to the comparison of a new conductive binder system to the traditional PVDF/C electrode matrix. These studies are contributing to the underexplored issue of microstructural effects on battery performance, and highlight alternatives to composite electrode composition and microstructure control.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".