Solution-Processed Quantum Dot Photodetectors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Digital imaging has traditionally been enabled by single-crystalline photodetectors. This approach typically either mandates the use of silicon as photon-to-electron converter or requires a hybrid-integrated solution. In contrast, solution-processed optoelectronic materials offer convenient integration of light-sensing materials atop an electronic readout circuit. Colloidal quantum dots offer particular advantages, combining solution-processing with the spectral tunability afforded by the quantum size effect. Here we review recent progress in solution-processed quantum dot photodetectors and their application in future imaging systems. We focus on progress towards high responsivity (photon-to-electron gains exceeding 1000) and sensitivity (normalized detectivity D* ~ 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">13</sup> Jones) in the visible, the near infrared, and the short-wavelength infrared. We also highlight the achievement of solution-processed photoconductive photodetectors combining photoconductive gain and temporal responses faster than 30 ms, devices therefore compatible with video-frame-rate imaging. We conclude with a discussion of recent colloidal quantum dot photodiodes having megahertz bandwidth and detectivity of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> Jones.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 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 it