Fast Near‐Infrared Photodetection Using III–V Colloidal Quantum Dots
Bibliographic record
Abstract
Abstract Colloidal quantum dots (CQDs) are promising materials for infrared (IR) light detection due to their tunable bandgap and their solution processing; however, to date, the time response of CQD IR photodiodes is inferior to that provided by Si and InGaAs. It is reasoned that the high permittivity of II–VI CQDs leads to slow charge extraction due to screening and capacitance, whereas III–Vs—if their surface chemistry can be mastered—offer a low permittivity and thus increase potential for high‐speed operation. In initial studies, it is found that the covalent character in indium arsenide (InAs) leads to imbalanced charge transport, the result of unpassivated surfaces, and uncontrolled heavy doping. Surface management using amphoteric ligand coordination is reported, and it is found that the approach addresses simultaneously the In and As surface dangling bonds. The new InAs CQD solids combine high mobility (0.04 cm 2 V −1 s −1 ) with a 4× reduction in permittivity compared to PbS CQDs. The resulting photodiodes achieve a response time faster than 2 ns—the fastest photodiode among previously reported CQD photodiodes—combined with an external quantum efficiency (EQE) of 30% at 940 nm.
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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".