<i>(Keynote)</i> Ultrahigh-Gain Colloidal Quantum Dot Infrared Photodetectors
Bibliographic record
Abstract
Colloidal quantum dots (CQDs) are promising for infrared photodetectors (IRPDs) with high detectivity ( D * ) and low-cost production. While CQDs enable photo-induced charge multiplication, thermal noise in low-bandgap materials limits their performance in IR detectors. Herein, we present a pioneering architecture of a CQD-based IRPD that employs kinetically pumped avalanche multiplication. By applying a strong electric field to a thick CQD layer (>540 nm), electrons acquire kinetic energy beyond the bandgap of the CQD material, initiating kinetically pumped charge multiplication. Optimizing the dot-to-dot distance to approximately 4.1 nm improves performance by balancing impact ionization and electron-hopping. Our optimized CQD-based IRPD achieved a maximum multiplication gain of 85 and a peak detectivity ( D * ) of 1.4×10 14 Jones at 940 nm 1 . This architecture offers potential for single-photon detection and ultra-high detectivity applications. 1 Lee et al. Nature Nanotechnology 20, 237 (2025)
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.008 |
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".