Simulation and Optimization of Highly Efficacious Polymer Solar Cell
Bibliographic record
Abstract
This work presents design recommendations to enhance the behavior of polymer solar cells (PSC) through solar cell simulations. The investigated polymer cell comprises a PBDB-T: PZT blend as the active layer, initially structured as ITO/PEDOT-PSS/PBDB-T: PZT/PFN-Br/Ag, achieving a power conversion efficiency (ETA) of approximately 14.91%. Validation of simulation models with experimental data validates the implemented material parameters in the SCAPS-1D simulator. To improve efficiency different materials for the electron transport layer (ETL) are proposed. A suitable ETL should have an energy band offset that matches the absorber material. Various ETLs, such as ZnO, WO3, ZnOS, and ZnO0.3S0.7 are proposed. The front and back contacts’ work functions are optimized besides the thicknesses of all layers to improve the efficiency of the proposed PSC. Furthermore, the defect density in the polymer material is studied. Finally, the inverted structure of the simulated PSC is investigated. All previous steps Leeds to increase the ETA of higher than 44%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".