Atomic Layer Deposition of Lithium Tantalate Solid-State Electrolytes
Bibliographic record
Abstract
3D all-solid-state microbatteries are promising onboard power systems for autonomous devices. The fabrication of 3D microbatteries needs deposition of active materials, especially solid-state electrolytes, as conformal and pinhole free thin films in 3D architectures, which has proven very difficult for conventional deposition techniques, such as chemical vapor deposition and physical vapor deposition. Herein, we report an alternative technique, atomic layer deposition (ALD), for achieving ideal solid-state electrolyte thin films for 3D microbatteries. Lithium tantalate solid-state electrolytes, with well-controlled film composition and film thickness, were grown by ALD at 225 °C using subcycle combination of 1 × Li 2 O + n × Ta 2 O 5 (1 ≤ n ≤ 10). The film composition was tunable by varying Ta 2 O 5 subcycles ( n ), whereas the film thickness displayed a linear relationship with ALD cycle number, due to the self-limiting nature of the ALD process. Furthermore, the lithium tantalate thin films showed excellent uniformity and conformity in 3D anodic aluminum oxide template. Moreover, impedance testing showed that the lithium tantalate thin film exhibited a lithium ion conductivity of 2 × 10 –8 S/cm at 299 K. The lithium tantalate thin films by ALD, featured with well-controlled film thickness and composition, excellent step coverage, and moderate ionic conductivity at room temperature, would be promising solid-state electrolytes for 3D microbatteries.
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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".