Multi-junction laser power converters exceeding 50% efficiency in the short wavelength infrared
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Photonic or laser power converters are crucial components in power-by-light systems. However, their use in long-distance applications has been hindered by low efficiencies and output voltages within the optical fiber transmission window of 1.3–1.6 μm laser wavelengths. Here, we improve and simplify the design and characterization processes for photonic power converters, exceeding 50% efficiency under 1.446 μm laser light. We develop a calibrated model predicting efficiency gains with increasing bandgap, reaching up to 57% efficiency at a 1.3-μm wavelength. As a first demonstration, we produce a high-efficiency device designed by the model: a four-junction InGaAsP photonic power converter with a conversion efficiency of 53.6% ± 1.3% and an output voltage above 2 V under 15.2 W/cm 2 of 1.446 μm laser light. These advances open new, practical pathways for integrating photonic power converters into telecommunication systems and unlock the potential to further optimize their design with machine learning algorithms trained with our predictive model.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it