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 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 | 0.001 |
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".