(Plenary) Solid State Li-Ion Batteries: Material Advances and a Reality Check
Bibliographic record
Abstract
The development of safe and high-performance all-solid-state batteries (ASSB) is contingent on creating fast ion conductors that combine high ionic conductivity with good ductility and chemical stability in a large voltage window, while – especially - mastering the interface of the solid electrolyte with the electrode materials. This presentation will examine ways to address these factors with new materials, while also shedding light on design concepts for ion conductivity. The talk will cover an overview of the state-of-the art in the field, followed by a focus on recent findings in our laboratory concerning a) synthesis of thiophosphate-halide argyrodites, where very significant increases in conductivity above that of the parent Li6PS5Cl phase have been attained by both tuning composition and developing “clean” solution-engineering processing routes to these materials; b) creation of novel thiophosphate-halide and related structures that exhibit both ion conductivities above 1 mS/cm and good chemical stability; c) understanding the critical role that the anion framework plays in dictating ion conductivity using a combination of room/high temperature X-ray/neutron diffraction, NMR, and ab initio molecular dynamics simulations; d) examination of the interface of the solid state electrolytes at the positive and negative electrodes in practical ASSBs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.054 | 0.031 |
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".