Epi-grown broadband reflector for InAs-based thermophotovoltaics
Bibliographic record
Abstract
Reflecting sub-bandgap photons is crucial for maximizing the efficiency of thermophotovoltaic devices. However, existing metal-deposited reflectors rely on back-side metallization, which cannot be grown epitaxially, necessitating additional processing steps. In this study, we fabricate InAs-based thermophotovoltaic devices featuring a straightforward, epitaxially grown sub-bandgap reflector composed of a single layer of n-doped InAs at a doping concentration of 2.4 × 10 19 cm −3 . This high doping produces long-wavelength metallic-like reflection, and our devices demonstrate high sub-bandgap reflectivity from 3.5 to 17 μm, achieving up to 93 % reflectivity compared to 30–40 % for designs without the reflector. Using a calibrated optical model, we predict that the sub-bandgap reflectivity of this layer enhances spectral efficiency from 38 % to 79 % under a 600 K normally incident blackbody spectrum. This improvement rivals that of a standard gold back reflector, which achieves a spectral efficiency of 94 %. Additionally, our predictive electrical model, calibrated with fabricated devices, indicates that the reflective layer does not adversely affect the electrical properties of the thermophotovoltaic devices. This sub-bandgap reflector can be integrated into existing InAs-based thermophotovoltaic fabrication processes, eliminating complex substrate removal steps required for traditional gold reflectors. • Demonstrated sub-bandgap reflectivity of up to 93 % for devices with a n-InAs reflective layer. • The reflective layer can improve spectral efficiency from 38 % to 79 %, under a 600 K normally incident blackbody spectrum. • Measurement and simulation results reveal that the reflective layer does not adversely affect the electrical properties of the devices.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".