Role of Nonfullerene Acceptor Impurities and Purification on the Efficiency and Stability of Organic Photovoltaics
Bibliographic record
Abstract
The introduction of nonfullerene acceptors (NFAs) has pushed the power conversion efficiency and organic photovoltaics (OPV) device stability to new standards. In this aspect, removal of trace impurities from one purification stage to the next is frequently stressed throughout the synthesis of photoactive OPV materials and NFAs to obtain the highest‐purity material. However, detailed studies of the effect of purification on device performance are less reported. Herein, the role of NFA trace impurities on the optoelectronic characteristics and lifetime of resulting OPV devices is studied. The optimization of PBDB‐T:ITIC‐X devices, with various ITIC purity levels (X), has been thoroughly studied via a combination of photophysical, chemical, morphological, electrical, and optical characterization techniques, to shine light on the role of these impurities on device performance and lifetime. The findings suggest that, even in materials with larger concentrations of trace impurities, careful tuning can produce high efficiencies. Interestingly, the less‐pure materials lead to longer device lifetimes along with an enhancement in accumulative power generation by a factor 3, compared to the purest ITIC‐based devices. This demonstrates that selecting a material with the highest purity may not always be the best option for NFA OPV and that any positive effects of NFA purification must be carefully considered in light of both the device efficiency and stability.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".