Engineering the Temporal Response of Photoconductive Photodetectors via Selective Introduction of Surface Trap States
Bibliographic record
Abstract
Photoconductive photodetectors fabricated using simple solution-processing have recently been shown to exhibit high gains (>1000) and outstanding sensitivities ( D* > 10(13) Jones). One ostensible disadvantage of exploiting photoconductive gain is that the temporal response is limited by the release of carriers from trap states. Here we show that it is possible to introduce specific chemical species onto the surfaces of colloidal quantum dots to produce only a single, desired trap state having a carefully selected lifetime. In this way we demonstrate a device that exhibits an attractive photoconductive gain (>10) combined with a response time ( approximately 25 ms) useful in imaging. We achieve this by preserving a single surface species, lead sulfite, while eliminating lead sulfate and lead carboxylate. In doing so we preserve the outstanding sensitivity of these devices, achieving a specific detectivity of 10(12) Jones in the visible, while generating a temporal response suited to imaging 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 | 0.001 |
| 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".