High‐Performance P‐N Junction Heterostructure with Carbon Quantum Dot and Nitrogen Self‐Doped Graphitic Carbon Nitride for Visible Light Photodetection
Bibliographic record
Abstract
Abstract Organic photodetectors (OPDs) hold immense promise for optoelectronic applications. Here a zero‐biased, high‐performance organic photodetector employing a 2D organic heterostructure is introduced. The structure combines carbon quantum dots (CQDs) with nitrogen self‐doped graphitic carbon nitride (g‐C 3 N 4+ ) and is tested for alternating current (AC) photodetection on an interdigitated electrode platform. The study reveals extraordinary performance driven by the synergistic effects of efficient charge excitation, separation, and emission within the 2D/2D CQD/g‐C 3 N 4 + heterostructure, leveraging mechanisms of photoconduction, photogating, and fluorescence. A unique convergence to similar rise and decay times in the order of 2.9 ms is observed at higher frequencies in the visible (Vis) spectrum. Benchmarking against state‐of‐the‐art OPDs shows ultrahigh specific detectivity (4.60 × 10 18 Jones), ultrahigh responsivity (1.43 × 10 7 A W −1 ), high external quantum efficiency (43 × 10 7 %) at an optical intensity of 3.56 × 10 −4 mW cm −2 and a wavelength of 405 nm while delivering competitive performance at 532 and 635 nm as well. Moreover, a large linear dynamic range of 86–162 dB in the Vis spectrum is obtained. These enhancements promise development of a new generation of OPDs to advance light sensing and imaging applications at high frequency, marking a significant milestone in optoelectronic device engineering.
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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) | 0.000 | 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".