Spatial Atomic Layer Deposition for Energy and Electronic Devices
Bibliographic record
Abstract
Functional thin films play a critical role in efforts toward efficient devices for energy conversion and storage, as well as low-loss electronics. These films need to be manufactured at scale, cost-effectively, and with precision. Atmospheric pressure spatial atomic layer deposition (AP-SALD) has emerged as a promising technique for fulfilling these requirements. AP-SALD replicates the subnanometer control of thickness, uniformity, crystallinity, and conformality to the substrate featured in conventional atomic-layer-deposited films, but has the important advantage of depositing these films with growth rates that are orders of magnitude higher. This review discusses the opportunities and advantages that AP-SALD opens up in energy-conversion and storage devices, as well as low-loss electronics. In particular, the review features recent work on using AP-SALD-grown films for photovoltaics, light-emitting diodes, self-powered sensors, and photoelectrochemical cells and batteries, as well as in transparent conductive materials and the epitaxial growth of thin films. Perspectives on future unexplored opportunities for AP-SALD in energy and low-loss electronics are also discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".