Near-infrared photon upconversion devices based on GaNAsSb active layer lattice matched to GaAs
Bibliographic record
Abstract
Room-temperature full GaAs-based near-infrared (NIR) upconversion has been demonstrated by connecting lattice-matched GaNAsSb/GaAs p-i-n photodetectors in series with commercial GaAs/AlGaAs light-emitting diodes (LEDs). Due to the avalanche gain in GaNAsSb/GaAs photodetectors and high internal efficiency in GaAs/AlGaAs LEDs, the upconversion efficiency of the integrated system reaches 0.048 W/W under −7 V bias, much higher than any existing NIR upconverters without amplifying structures. We have further investigated the dependence of the upconversion efficiency on applied bias and incident light intensity. The present work establishes an experimental base for direct epitaxial growth of full GaAs-based NIR upconverters with high upconversion efficiencies.
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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".