Solar Cells, Photodetectors, and Optical Sources from Infrared Colloidal Quantum Dots
Bibliographic record
Abstract
Abstract Optoelectronic devices made via spin‐coating of soft materials onto an arbitrary substrate enable ready integration, low cost, and physical flexibility. The use of solution‐processed colloidal quantum dots offers the added advantage of quantum‐size‐effect tuning of material bandgap. Tuning across the near‐ and short‐wavelength infrared (SWIR) spectral regions enables applications in fiber‐optic communications, night vision and biomedical imaging, and efficient solar energy collection. Here we review progress in infrared solar cells, light sensors, and optical sources based on solution‐processed materials. The latest solution‐processed photovoltaics now provide 4.2% power conversion efficiencies in the infrared, placing them a factor of three away from enabling a doubling in overall solar power conversion efficiency of visible‐wavelength solution‐processed photovoltaics. The best solution‐processed photodetectors now provide sensitivities of 1013 Jones D* (normalized detectivity), exceeding the sensitivity of the best epitaxially grown SWIR photodetectors. Infrared optical sources, both broadband light‐emitting diodes and, more recently, lasers, have now also been reported at 1.5 µm.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".