Assessing Individual Material Degradation toward Organic Solar Cells Using Accelerated Nanolayer Lifetime Protocols: Implications for Solar Cell Longevity
Bibliographic record
Abstract
Organic solar cells (OSCs) can be highly affected by environmental stresses like heat, moisture, and sunlight during their service life if they are not encapsulated or if the encapsulation leaks. A deep understanding of how each individual organic layer changes/reacts to various environmental factors is a crucial aspect in designing an effective OSC architecture to ensure the longevity and stability of the materials toward the device’s performance. While there are numerous examples of encapsulated OSCs operating outdoors for extended periods of time, there is an insufficiency of information available about the individual stability of the materials involved. The focus of this study is to provide a quantitative assessment of the individual unencapsulated OSC layers when they are exposed to combinations of heat, humidity, and light. Ideally, a similar process can be applied to different organic nanolayers in the future, and the results can be used as a reference. Throughout the accelerated aging process, the most impactful environmental stressor was the presence of strong light. Via UV–vis and fluorescence data acquisition, the chloro-boron subphthalocyanine (Cl-BsubPc) layer was observed to be altered by some combination of hydrolysis and nanostructural change, from the strong incident light, which was not observed if aged in the dark. We also observed significant nanolayer film crystallization for other materials when exposed to humid heat and an increase in film hydrophilicity during the aging process. The nanolayer film crystallization could have also contributed to the loss of π-conjugation/color, which may not have undergone complete photobleaching. Though there were property changes throughout the accelerated aging process, we feel that the relatively long time scale of most changes highlights a characteristic material stability that would translate strongly to standard operating conditions in encapsulated devices. Adopting these methodologies can also be useful to guide further material development broadly in particularly susceptible materials in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".