Photojunction Field-Effect Transistor Based on a Colloidal Quantum Dot Absorber Channel Layer
Bibliographic record
Abstract
The performance of photodetectors is judged via high responsivity, fast speed of response, and low background current. Many previously reported photodetectors based on size-tuned colloidal quantum dots (CQDs) have relied either on photodiodes, which, since they are primary photocarrier devices, lack gain; or photoconductors, which provide gain but at the expense of slow response (due to delayed charge carrier escape from sensitizing centers) and an inherent dark current vs responsivity trade-off. Here we report a photojunction field-effect transistor (photoJFET), which provides gain while breaking prior photoconductors' response/speed/dark current trade-off. This is achieved by ensuring that, in the dark, the channel is fully depleted due to a rectifying junction between a deep-work-function transparent conductive top contact (MoO3) and a moderately n-type CQD film (iodine treated PbS CQDs). We characterize the rectifying behavior of the junction and the linearity of the channel characteristics under illumination, and we observe a 10 μs rise time, a record for a gain-providing, low-dark-current CQD photodetector. We prove, using an analytical model validated using experimental measurements, that for a given response time the device provides a two-orders-of-magnitude improvement in photocurrent-to-dark-current ratio compared to photoconductors. The photoJFET, which relies on a junction gate-effect, enriches the growing family of CQD photosensitive transistors.
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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".