Energy Level Tuning in Conjugated Donor Polymers by Chalcogen Exchange for Low Dark Current Organic Photodetectors
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The performance of organic photodetectors (OPDs) using conjugated polymer donors and molecular acceptors has improved rapidly, but many polymers are difficult to upscale due to their complex structures. This study examines two low-complexity thiophene copolymers with substituted benzooxadiazole ( FO6-BO-T ) or benzothiadiazole ( FO6-T ). Substituting sulfur with oxygen in FO6-BO-T increased its ionization energy without affecting the optical gap. When blended with the nonfullerene acceptor IDSe, FO6-BO-T showed a significantly lower dark current density (2.06·10 –9 A cm –2 at −2 V) compared to FO6-T . Grazing incidence wide-angle X-ray scattering (GIWAXS) measurements demonstrated that pristine FO6-BO-T exhibited a more ordered morphology than FO6-T . However, blending resulted in a significant disruption to the ordered domains in both cases, with a loss of orientational order, suggesting that FO6-BO-T ’s improved performance is largely related to its increased ionization energy. This study demonstrates the potential of chalcogen atom engineering to enhance the performance of the OPD in scalable polymers.
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".