Stable Organic Solar Cells Enabled by Interlayer Engineering
Bibliographic record
Abstract
The development of high-performance organic solar cells (OSCs) with high operational stability is essential to accelerate their commercialization. Unfortunately, there is currently a lack of detailed understanding of the origin of instabilities in state-of-the-art OSCs based on bulk heterojunction (BHJ) featuring non-fullerene acceptors (NFAs). Herein, we developed NFA-based OSCs using different charge extraction interlayer materials and studied their storage, thermal, and operational stabilities. Despite the high power conversion efficiency (PCE) of the OSCs (17.54%), we found that cells featuring self-assembled monolayers (SAMs) as hole-extraction interlayers exhibited poor stability. The time required for these OSCs to reach 80% of their initial performance (T80) was only 6 h under continuous thermal stress at 85 °C in a nitrogen atmosphere and 1 h under maximum power point tracking (MPPT) in a vacuum. Inserting MoOx between ITO and SAM enhanced the T80 to 50 h and ~15 h after the thermal and operational stability tests, respectively, while maintaining a PCE of 16.9%. Replacing the organic PDINN electron transport layer with ZnO NPs further enhances the cells’ thermal and operational stability, boosting the T80 to 1000 and 170 h, respectively. Our work reveals the synergistic role of charge interlayers and device architecture in developing efficient and stable OSCs.
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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".