Radiator Tailoring for Enhanced Performance in InAs-Based Near-Field\n Thermophotovoltaics
Bibliographic record
Abstract
Near-field thermophotovoltaics (NFTPV) systems have significant potential for waste heat recovery applications, with both high theoretical efficiency and power density, up to 40% and $11 \ \mathrm{W/cm^{2}}$ at 900 K. Yet experimental demonstrations have only achieved up to 14% efficiency and modest power densities (i.e., $0.75 \ \mathrm{W/cm^{2}}$). While experiments have recently started to focus on photovoltaic (PV) cells custom-made for NFTPV, most work still relies on conventional doped silicon radiators. In this work, we design an optimized NFTPV radiator for an indium arsenide-based system and, in the process, investigate models for the permittivity of InAs in the context of NFTPV. Based on existing measurements of InAs absorption, we find that the traditional Drude model overestimates free carrier absorption in InAs. We replace the Drude portion of the InAs dielectric function with a revised model derived from ionized impurity scattering. Using this revised model, we maximize the spectral efficiency and power density of a NFTPV system by optimizing the spectral coupling between a radiator and an InAs PV cell. We find that when the radiator and the PV cell are both made of InAs, a nearly threefold improvement of spectral efficiency is possible compared to a traditional silicon radiator with the same InAs cell. This enhancement reduces subgap thermal transfer while maintaining power output.
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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".