Vapor-Phase Atomic-Controllable Growth of Amorphous Li<sub>2</sub>S for High-Performance Lithium–Sulfur Batteries
Bibliographic record
Abstract
Lithium-sulfur (Li-S) batteries hold great promise to meet the formidable energy storage requirements of future electrical vehicles but are prohibited from practical implementation by their severe capacity fading and the risks imposed by Li metal anodes. Nanoscale Li(2)S offers the possibility to overcome these challenges, but no synthetic technique exists for fine-tailoring Li(2)S at the nanoscale. Herein we report a vapor-phase atomic layer deposition (ALD) method for the atomic-scale-controllable synthesis of Li(2)S. Besides a comprehensive investigation of the ALD Li(2)S growth mechanism, we further describe the high performance of the resulting amorphous Li(2)S nanofilms as cathodes in Li-S batteries, achieving a stable capacity of ∼ 800 mA · h/g, nearly 100% Coulombic efficiency, and excellent rate capability. Nanoscale Li(2)S holds great potential for both bulk-type and thin-film high-energy Li-S batteries.
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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".