Tin Oxide Electron Transport Layers for Air-/Solution-Processed Conventional Organic Solar Cells
Bibliographic record
Abstract
Commercialization of organic solar cells (OSC) is imminent. Interlayers between the photoactive film and the electrodes are critical for high device efficiency and stability. Here, the applicability of SnO 2 nanoparticles (SnO 2 NPs) as the electron transport layer (ETL) in conventional OSCs is evaluated. A commercial SnO 2 NPs solution in butanol is mixed with ethanol (EtOH) as a processing co-solvent to improve film formation for spin and slot-die coating deposition procedures. When processed with 200% v/v EtOH, the SnO 2 NPs film presents uniform film quality and low photoactive layer degradation. The optimized SnO 2 NPs ink is coated, in air, on top of two polymer:fullerene-based systems and a nonfullerene system, to form an efficient ETL film. In every case, addition of SnO 2 NPs film significantly enhances photovoltaic performance, from 3.4 and 3.7% without the ETL to 6.0 and 5.7% when coated on top of PBDB-T:PC 61 BM and PPDT2FBT:PC 61 BM, respectively, and from 3.7 to 7.1% when applied on top of the PTQ10:IDIC system. Flexible, all slot-die-coated devices, in air, are also fabricated and tested, demonstrating the versatility of the SnO 2 NPs ink for efficient ETL formation on top of organic photoactive layers, processed under ambient condition, ideal for practical large-scale production of OSCs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".