Fully Printed and Industrially Scalable Semitransparent Organic Photovoltaic Modules: Navigating through Material and Processing Constraints
Bibliographic record
Abstract
While the power conversion efficiency (PCE) of organic photovoltaics (OPV) on small‐area lab cells has rapidly increased during the last few years, the performance on module level and the availability of OPV modules on the market is still limited, primarily due to specific constraints imposed by the industrial production process. This work deals with the upscaling process of latest‐generation OPV from small‐area lab cells to fully solution‐processed modules, which are compatible to industrial roll‐to‐roll (R2R) printing. This transfer is demonstrated step by step from material selection and process optimization for every single layer of the stack (photoactive layer, charge transporting layers, and solution‐processed top electrode)–including long‐term stability investigations (thermal and light)–to scaling up the device area by a factor of >100. Thus, a semitransparent OPV module with 10.8% PCE on 10.2 cm2 active area is achieved, which is among the highest performances for semitransparent, fully solution‐processed OPV modules. The individual developments all meet the requirements for industrial R2R printing (green solvents, processing in air, annealing ≤140 °C, etc.), which ensures that both the optimized layer stack and the fabrication process are fully scalable and easily transferable to large‐scale production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".