Morphology changes upon scaling a high-efficiency, solution-processed solar cell
Bibliographic record
Abstract
Solution processing via roll-to-roll (R2R) coating promises a low cost, low thermal budget, sustainable revolution for the production of solar cells. Poly[(5,6-difluoro-2,1,3-benzothiadiazol-4,7-diyl)-alt-(3,3‴-di(2-octyldodecyl)-2,2';5',2″;5″,2‴-quaterthiophen-5,5-diyl)], PffBT4T-2OD, has recently been shown to achieve high power conversion efficiency (>10%) paired with multiple acceptors when thick films are spun-coat from hot solutions. We present detailed morphology studies of PffBT4T-2OD based bulk heterojunction films deposited by the volume manufacturing compatible techniques of blade-coating and slot-die coating. Significant aspects of the film morphology, the average crystal domain orientation and the distribution of the characteristic phase separation length scales, are remarkably different when deposited by the scalable techniques vs spun-coat. Yet, we find that optimized blade-coated devices achieve PCE >9.5%, nearly the same as spun-coat. These results challenge some widely accepted propositions regarding what is an optimal BHJ morphology and suggest the hypothesis that diversity in the morphology that supports high performance may be a characteristic of manufacturable systems, those that maintain performance when coated thicker than ≈200 nm. In situ measurements reveal the key differences in the solidification routes for spin- and blade- coating leading to the distinct film structures.
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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 | 0.001 | 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".