Slot-die coated bulk heterojunction vs layer-by-layer organic photovoltaics: Device architecture dependent degradation
Bibliographic record
Abstract
In recent years, there have been notable success stories in commercializing organic photovoltaics (OPVs) for new applications, and their record power conversion efficiency has, in parallel, increased beyond the 20% milestone. However, the still relatively low performance for large OPV modules, with limited thermal and light stability, has remained a bottleneck for wider adoption. In this study, we investigated the scalability and performance of two commonly employed OPV device architectures: inverted and conventional stacks. We explored two different strategies for photoactive layer formation: the bulk heterojunction (BHJ) method and the layer-by-layer method. All solution-processable layers were fabricated from green solvents in the air and optimized using the scalable slot-die coating technique, with PM6 as the electron donor and Y7-12 as the non-fullerene electron acceptor. In addition, we studied the degradation of the devices under thermal and light stress, placing particular emphasis on identifying thermal degradation pathways for the different device architectures. Our findings suggest that, irrespective of the device architecture, the scalable slot-die coating method can be employed to achieve high-efficiency devices, with the best power conversion efficiencies of 15.24%. The investigation indicates that minor thermal degradation occurs predominantly at the transport layer and electrode interface. Notably, inverted BHJ devices demonstrated impressive light stability, maintaining initial high performance for over 800 hours. In addition, a mini-module was fabricated, achieving a performance of 13.06%, comparable to small-scale devices. This work demonstrates the potential of slot-die coating under ambient conditions for producing high-efficiency OPV cells and modules and paves the way for developing more stable organic photovoltaics.
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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".