Dual-Passivation Interfacial Engineering with Creatinol-O-Phosphate for High-Efficiency and Stable Perovskite Solar Cells
Bibliographic record
Abstract
Tin oxide (SnO 2 ) is widely utilized as the electron transport layer (ETL) in n-i-p perovskite solar cells (PSCs) due to its excellent performance and ease of fabrication. However, defects on the surface of SnO 2 can impede carrier migration, and the chemical and physical properties of the ETL substrate can adversely affect the quality of the perovskite film grown on it, thus limiting the photovoltaic efficiency of PSCs. In this study, we introduced a bifunctional small molecule, creatinol-O-phosphate (COP), as an interlayer between SnO 2 and the perovskite layers. COP facilitates dual passivation by simultaneously addressing oxygen vacancies on the SnO 2 surface and undercoordinated Pb 2+ ions within perovskite film, leading to an effective reduction in trap-state densities. This dual-passivation approach promotes perovskite grain growth, resulting in an alignment of grains perpendicular to the substrate and enhancing the overall crystalline quality of the perovskite film. Consequently, the power conversion efficiency (PCE) of the COP-modified PSC reached 24.45%, accompanied by improved storage stability. These findings underscore the potential of COP as a promising interface modifier for advancing the performance of PSCs.
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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".