Heavy-Metal-Free Solution-Processed Nanoparticle-Based Photodetectors: Doping of Intrinsic Vacancies Enables Engineering of Sensitivity and Speed
Bibliographic record
Abstract
Photodetection in semiconductors enables digital imaging, spectroscopy, and optical communications. Integration of solution-processed light-sensing materials with a range of substrates offers access to new spectral regimes, the prospect of enhanced sensitivity, and compatibility with flexible electronics. Photoconductive photodetectors based on solution-cast nanocrystals have shown tremendous progress in recent years; however, high-performance reports to date have employed Pb- and Cd-containing materials. Here we report a high-sensitivity (photon-to-electron gain >40), high-speed (video-frame-rate-compatible) photoconductive photodetector based on In(2)S(3). Only by decreasing the energetic depth of hole traps associated with intrinsic vacancies in beta-phase In(2)S(3) were we able to achieve this needed combination of sensitivity and speed. Our incorporation of Cu(+) cations into beta-In(2)S(3)'s spinel vacancies that led to acceptable temporal response in the devices showcases the practicality of incorporating dopants into nanoparticles. The devices are stable in air and under heating to 215 degrees C, advantages rooted in the reliance on the stable inclusion of dopants into available sites instead of surface oxide species.
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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 | 0.001 |
| Open science | 0.001 | 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".