End-Group Engineering of Low-Bandgap Compounds for High-Detectivity Solution-Processed Small-Molecule Photodetectors
Bibliographic record
Abstract
Several π-conjugated compounds based on diketopyrrolopyrrole and trithiophene substituted with different end groups (alkyl, alkyloxy, and alkylthio) were designed and synthesized for investigation of the material properties and photodetector performance brought by subtle changes in the end groups. Among all, compound 4 with hexylthio groups exhibits the most red-shifted absorption, strongest molecular stacking, highest mobility, and ideal film morphology. These unique properties make it a promising material for use in small-molecule photodetectors. Photodetector SMPD-4 based on compound 4 exhibits broad response from 300 to 900 nm and a high specific detectivity ( D* ) of 1.3 × 10 13 Jones at 650 nm under −0.1 V. This result is among the best values reported for solution-processed small-molecule photodetectors and even in the same order of conventional silicon photodetector. The molecular structure–material property–device performance relationships are established with these compounds. This work suggests that end-group engineering is a useful method in tuning the material properties and device performance of organic semiconductors.
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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".