Colloidal quantum dot photodetectors (Presentation Recording)
Bibliographic record
Abstract
Colloidal quantum dots (CQDs) are emerging solution processed materials combining low cost, easy deposition on large and flexible substrates, and bandgap tunability. The latter feature, which allows spectral tuning of the absorption profile of the semiconductor, makes these materials particularly attractive for light detection applications. Lead sulfide (PbS) CQDs, in particular, have shown astonishing performance as a light sensitive material operating at visible and infrared (IR) wavelengths. Early studies of PbS CQDs used as a photosensitive resistor (photoconductor) showed an impressive responsivity - exceeding 1000 A/W - and a detectivity (D*) higher then 10^13 Jones. This impressive D* was preserved in the successive development of the first PbS CQD photodiode, showing the possibility to realize fast - f_3db > 1Mhz - and sensitive IR detectors. Currently, the field is moving toward the development of hybrid devices and phototransitors. PbS CQDs have been combined in field effect transistors (FETs) with graphene and MoS2 channels, showing ultra-high gain (exceeding 10^8 electrons/photons) and high D*. Recently a photo-junction FET (photo-JFET) has been reported that breaks the inherent dark current/gain/bandwidth compromise affecting photoconductive light detectors. With this presentation we offer a broad overview on CQD photodetection highlighting the past achievements, the benefits, the challenges and the prospects for the future research on this field.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".