Thermally‐Induced Degradation in PM6:Y6‐Based Bulk Heterojunction Organic Solar Cells
Bibliographic record
Abstract
Abstract Thermally induced degradation of organic photovoltaic devices hinders the commercialization of this emerging PV technology. Thus, a precise understanding of the origin of thermal device instability, as well as identifying strategies to circumvent degradation is of utmost importance. Here, it investigates thermally‐induced degradation of state‐of‐the‐art PBDB‐T‐2F (PM6):BTP (Y6) bulk heterojunction solar cells at different temperatures and reveal changes of their optical properties, photophysics, and morphology. The open‐circuit voltage and fill factor of thermally degraded devices are limited by dissociation and charge collection efficiency differences, while the short‐circuit current density is only slightly affected. Energy‐resolved electrochemical impedance spectroscopy measurements reveal that thermally degraded samples exhibit a higher energy barrier for the charge‐transfer state to charge‐separated state conversion. Furthermore, the field dependence of charge generation, recombination, and extraction are studied by time‐delayed collection field and transient photocurrent and photovoltage experiments, indicating significant bimolecular recombination limits device performance. Finally, coupled optical‐electrical device simulations are conducted to fit the devices’ current‐voltage characteristics, enabling us to find useful correlations between optical and electrical properties of the active layers and device performance parameters.
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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".