Synergistic Integration of Dye Molecule and Semiconducting Polymer for Near Infrared Organic Phototransistors
Bibliographic record
Abstract
Abstract Highly sensitive near‐infrared (NIR) photodetectors are critical for biomedical applications demanding precision and performance. Organic phototransistors (OPTs) offer superior photo‐sensing due to field‐effect modulation. Yet, narrow bandgap organic semiconducting polymers (SCPs) are rarely used in high‐performance NIR‐OPTs because of low mobility and high dark off‐current (DOC). Although bulk heterojunction can address these challenges, spin‐coating often leads to non‐uniform, randomly oriented domains. This study introduces an effective strategy: blending newly synthesized NIR‐dye 2‐((50‐(4‐(50‐((4,5‐bis(hexylthio)‐1,3‐dithiol‐2‐ylidene)methyl)‐[2,20‐bithiophen]‐5‐yl)‐2,5‐bis(2‐ethylhexyl)‐3,6‐dioxo‐2,3,5,6‐tetrahydropyrrolo[3,4‐c]pyrrol‐1‐yl)‐[2,20 bithiophen]‐5‐yl)methylene)malononitrile (DPPCN) with poly[2,5‐bis (3‐tetradecylthiophen‐2‐yl) thieno[3,2‐b]thiophene] (PBTTT), a p‐type SCP with excellent charge transport properties. Then using a novel Floating Film Transfer Method (FTM) to control molecular self‐assembly at the air–liquid interface, the PBTTT/DPPCN system achieves uniaxial molecular orientation (DR ≈3.29) and improved film crystallinity. OPTs made of PBTTT/DPPCN(2%) exhibit remarkable photosensitivity of 2.8 × 103 under NIR and 2.2 × 10⁴ under red light (1 mW cm−2). Optimized devices achieve high photoresponsivity of 4.82 × 103 A W−1 in NIR with EQE reaching ≈1 × 10⁷%, with even greater responsivity to red light. Improved performance is attributed to enhanced charge‐transfer interaction between PBTTT and DPPCN, efficient exciton dissociation, and superior charge transport by oriented PBTTT backbones. This approach successfully delivers high‐performance OPTs, advancing the potential of organic electronics for biomedical applications and beyond.
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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 | 0.000 |
| 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".