Broadband Sensing with High-Performance Non-Fullerene Acceptor-Based Organic Photodetectors
Bibliographic record
Abstract
Organic photodetectors (OPDs) are promising optoelectronic technologies due to their low cost, versatility, and ease of processing. OPDs based on conventional fullerene acceptors show narrow absorption window, which limits performance of the OPDs. In this work we present fabrication and characterization of non-fullerene acceptor (NFA), 3,9-bis(2-methylene-((3-(1,1-dicyanomethylene)-6,7-difluoro)-indanone))-5,5,11,11-tetrakis(4-hexylphenyl)-dithieno[2,3-d:2′,3′-d']-s indaceno[1,2-b:5,6-b']dithiophene (IT-4F) based organic photodetectors to have broadband sensing. Two types of NFA based OPDs are fabricated. For the first type only IT-4F is used as an acceptor material, whereas for the second type IT-4F mixed with a fullerene acceptor, [6, 6]-Phenyl-C61-butyric acid methyl ester (PC61BM) is used as an acceptor layer. Although both types of NFA based photodetectors show broadband sensing, the second type exhibits better charge transportation in the active layer than the first type, indicating their potential for broadband light sensing.
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) | 0.001 | 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 | 0.001 | 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".