Field-emission from quantum-dot-in-perovskite solids
Bibliographic record
Abstract
Abstract Quantum dot and well architectures are attractive for infrared optoelectronics, and have led to the realization of compelling light sensors. However, they require well-defined passivated interfaces and rapid charge transport, and this has restricted their efficient implementation to costly vacuum-epitaxially grown semiconductors. Here we report solution-processed, sensitive infrared field-emission photodetectors. Using quantum-dots-in-perovskite, we demonstrate the extraction of photocarriers via field emission, followed by the recirculation of photogenerated carriers. We use in operando ultrafast transient spectroscopy to sense bias-dependent photoemission and recapture in field-emission devices. The resultant photodiodes exploit the superior electronic transport properties of organometal halide perovskites, the quantum-size-tuned absorption of the colloidal quantum dots and their matched interface. These field-emission quantum-dot-in-perovskite photodiodes extend the perovskite response into the short-wavelength infrared and achieve measured specific detectivities that exceed 10 12 Jones. The results pave the way towards novel functional photonic devices with applications in photovoltaics and light emission.
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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".