Effect of different oxygen precursors on alumina deposited using a spatial atomic layer deposition system for thin-film encapsulation of perovskite solar cells
Bibliographic record
Abstract
Abstract An atmospheric-pressure spatial atomic layer deposition system operated in atmospheric-pressure spatial chemical vapor deposition conditions is employed to deposit alumina (AlO x ) thin films using trimethylaluminum and different oxidants, including water (H 2 O), hydrogen peroxide (H 2 O 2 ), and ozone (O 3 ). The impact of the oxygen precursor on the structural properties of the films and their moisture-barrier performance is investigated. The O 3 -AlO x films, followed by H 2 O 2 -AlO x , exhibit higher refractive indexes, lower concentrations of OH− groups, and lower water-vapor-transmission rates compared to the films deposited using water (H 2 O-AlO x ). The AlO x films are then rapidly deposited as thin-film-encapsulation layers on perovskite solar cells at 130 °C without damaging the temperature-sensitive perovskite and organic materials. The stability of the p–i–n formamidinium methylammonium lead iodide solar cells under standard ISOS-D-3 testing conditions (65 °C and 85% relative humidity) is significantly enhanced by the encapsulation layers. Specifically, the O 3 -AlO x and H 2 O 2 -AlO x layers result in a six-fold increase in the time required for the cells to degrade to 80% of their original efficiency compared to un-encapsulated cells.
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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".