Atomic Layer Deposited Lithium Silicates as Solid-State Electrolytes for All-Solid-State Batteries
Bibliographic record
Abstract
Development of solid-state electrolyte (SSE) thin films is a key toward the fabrication of all-solid-state batteries (ASSBs). However, it is challenging for conventional deposition techniques to deposit uniform and conformal SSE thin films in a well-controlled fashion. In this study, atomic layer deposition (ALD) was used to fabricate lithium silicate thin films as a potential SSE for ASSBs. Lithium silicates thin films were deposited by combining ALD Li 2 O and SiO 2 subcycles using lithium tert -butoxide, tetraethylorthosilane, and H 2 O as precursors. Uniform and self-limiting growth was achieved at temperatures between 225 and 300 °C. X-ray absorption spectroscopy analysis disclosed that the as-deposited lithium silicates were composed of SiO 4 tetrahedron structure and lithium oxide as the network modifier. X-ray photoelectron spectroscopy confirmed the chemical states of Li in the thin films were the same with that in standard lithium silicate. With one to one subcycle of Li 2 O and SiO 2 the thin films had a composition close to Li 4 SiO 4 whereas one more subcycle of Li 2 O delivered a higher lithium content. The lithium silicate thin film prepared at 250 °C exhibited an ionic conductivity of 1.45× 10 –6 S cm –1 at 373 K. The high ionic conductivity of lithium silicate was due to the higher lithium concentration and lower activation energy.
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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 | 0.001 |
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".