A Lithium Oxythioborosilicate Solid Electrolyte Glass with Superionic Conductivity
Bibliographic record
Abstract
Abstract As potential next‐generation energy storage devices, solid‐state lithium batteries require highly functional solid state electrolytes. Recent research is primarily focused on crystalline materials, while amorphous materials offer advantages by eliminating problematic grain boundaries that can limit ion transport and trigger dendritic growth at the Li anode. However, simultaneously achieving high conductivity and stability in glasses is a challenge. New quaternary superionic lithium oxythioborate glasses are reported that exhibit high ion conductivity up to 2 mS cm−1 despite relatively high oxygen: sulfur ratios of more than 1:2, that exhibit greatly reduced H2S evolution upon exposure to air compared to Li7P3S11. These monolithic glasses are prepared from vitreous melts without ball‐milling and exhibit no discernable XRD pattern. Solid‐state NMR studies elucidate the structural entities that comprise the local glass structure which dictates fast ion conduction. Stripping/plating onto lithium metal results in very low polarization at a current density of 0.1 mA cm−2 over repeated cycling. Evaluation of the optimal glass composition as an electrolyte in an all‐solid‐state battery shows it exhibits excellent cycling stability and maintains near theoretical capacity for over 130 cycles at room temperature with Coulombic efficiency close to 99.9%, opening up new avenues of exploration for these quaternary compositions.
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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".