Flexible Filter‐Free Narrowband Photodetector with High Gain and Customized Responsive Spectrum
Bibliographic record
Abstract
Conventional narrowband photodetection is enabled by coupling broadband photodetectors with complex optical filters. The recently reported charge collection narrowing, an alternative filter‐free strategy, attains very narrowband photodetection at the sacrifice of sensitivity. Herein, a new strategy is proposed to customize the responsive spectrum with high gain by using dye molecules with intrinsically versatile and narrowband absorption. The device configuration is organic dye/Zn0.9Mg0.1O nanoparticles/graphene, where the organic dye serves as the narrowband absorber, graphene serves as the fast carrier transport channel, and Zn0.9Mg0.1O nanoparticles play a triple role of enhancing dye loading, suppressing dye aggregation and blocking charge back recombination. A high responsivity of 8 × 103 A W−1 is thus obtained at a 530 nm response peak with a 60 nm full‐width at half maximum, a four orders of magnitude increase in sensitivity compared to the best narrowband photodetectors reported to date under the comparable electric field. Organic dyes with dual‐band absorption to demonstrate narrowband photodetectors with customized responsive spectrum are further implemented. The approach opens the way to the realization of efficient flexible narrowband photodetection for electronic skin and wearable electronic applications.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".