Revealing Additional Size-Dependent Defect Suppression Channels Governing Detectivity in InAs Colloidal Quantum Dot Photodiodes
Bibliographic record
Abstract
Indium arsenide (InAs) colloidal quantum dot (CQD) photodiodes combine tunable bandgaps with solution processing, offering a versatile platform for infrared detection. Using high-dynamic-range external quantum efficiency (HDR-EQE) measurements, we probe defect signatures and quantify their impact on performance. Analysis of Urbach tails and Gaussian sub-bandgap states shows that trap densities decrease with increasing nanocrystal size, exceeding predictions from simple surface-to-volume scaling and underscoring the influence of surface chemistry on bandedge disorder. These defect states affect the dark saturation current ( J 0 ), enabling us to estimate their contribution to detectivity and noise. The results connect nanocrystal size, defect population, and device performance, distinguishing intrinsic trap-mediated effects from extrinsic loss channels. We find that while intrinsic defects play a role, today’s InAs CQD photodiodes are primarily limited by contact and interface properties, highlighting these as key targets for further improvement.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.002 | 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 teacher head, 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".