Highly Efficient Nonfullerene Organic Solar Cells: Morphology Control and Characterizations
Bibliographic record
Abstract
Nonfullerene acceptors (NFAs) are currently a major research focus in the development of organic solar cells (OSCs) because of their readily tunable optical and electronic properties, enabling bulk heterojunction (BHJ) NFA‐based OSCs to achieve photovoltaic efficiencies exceeding 19%. Significant efforts have been made to yield the optimal nanoscale morphology, enabling the achievement of highly efficient NFA‐based OSCs. This review discusses the structural characteristics of NFAs and their relationship with morphology. Subsequently, the correlation between the morphology and photovoltaic parameters is introduced, which provides a fundamental basis for morphology modulation. This review then points out the major challenges of morphological characterization of NFA‐based blend films while using some conventional real‐space techniques due to low phase contrast and summarizes recently emerging characterization techniques capable of characterizing high‐contrast phase morphologies at multiple length scales. Finally, strategies for targeted morphological optimization through materials design, processing solvents, posttreatment, and ternary strategies are presented, although it is challenging to obtain an ideal morphology (e.g., appropriate phase separation and ideal donor/acceptor molecular interactions) due to the anisotropic structural characteristics of NFAs in the as‐cast films. This review is expected to provide guidance to continuously advancing the success of NFA‐based OSCs from the morphology perspective in the future.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".