Unveiling Photovoltaic Performance Enhancement Mechanism of Polymer Solar Cells via Synergistic Effect of Binary Solvent Additives
Bibliographic record
Abstract
Binary solvent additive engineering is an effective strategy to optimize photoactive films for high‐efficiency organic solar cells, however, the effect of single components on device performance and the combination principle of binary solvent additives remain unclear. Herein, synchrotron‐based grazing incident X‐ray diffraction, Derjaguin–Muller–Toporov modulus imaging, and plasmon energy shift imaging acquired by scanning transmission electron microscopy to investigate the effect of new binary solvent additive of 1,8‐diiodooctane (DIO) and less‐toxic and p‐anisaldehyde (AA) on device performance of solar cells based on poly[(5,6‐difluoro‐2,1,3‐benzothiadiazol‐4,7‐diyl)‐alt‐(3,3‴‐di(2‐octyldodecyl)2,2′;5′,2″;5″,2‴‐quaterthio‐phen‐5,5‴‐diyl)] (PffBT4T‐2OD) and [6,6]‐phenyl‐C61‐butyric acid methyl ester (PC61BM) are used. It is found that AA mainly favors polymer order and high crystallinity of PffBT4T‐2OD. Differently, DIO mainly enables PC61BM diffusing into PffBT4T‐2OD polymer matrix, leading to enlarged donor–acceptor (D–A) interface. As expected, by combining AA and DIO, the composite film provides large D–A interface and more balanced charge carrier transport. Consequently, their beneficial synergistic effect results in enhanced short circuit current and fill factor, and thereby increased power conversion efficiency of 10.64%, improved by 16% with respect to the control device. Herein, a general mechanism of enhancing device performance via the combination of solvent additives with different contributions to photoactive film is unveiled.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".