Radiator Tailoring for Enhanced Performance in InAs-Based Near-Field\n Thermophotovoltaics
Bibliographic record
Abstract
Near-field thermophotovoltaics (NFTPV) systems have significant potential for\nwaste heat recovery applications, with both high theoretical efficiency and\npower density, up to 40% and $11 \\ \\mathrm{W/cm^{2}}$ at 900 K. Yet\nexperimental demonstrations have only achieved up to 14% efficiency and modest\npower densities (i.e., $0.75 \\ \\mathrm{W/cm^{2}}$). While experiments have\nrecently started to focus on photovoltaic (PV) cells custom-made for NFTPV,\nmost work still relies on conventional doped silicon radiators. In this work,\nwe design an optimized NFTPV radiator for an indium arsenide-based system and,\nin the process, investigate models for the permittivity of InAs in the context\nof NFTPV. Based on existing measurements of InAs absorption, we find that the\ntraditional Drude model overestimates free carrier absorption in InAs. We\nreplace the Drude portion of the InAs dielectric function with a revised model\nderived from ionized impurity scattering. Using this revised model, we maximize\nthe spectral efficiency and power density of a NFTPV system by optimizing the\nspectral coupling between a radiator and an InAs PV cell. We find that when the\nradiator and the PV cell are both made of InAs, a nearly threefold improvement\nof spectral efficiency is possible compared to a traditional silicon radiator\nwith the same InAs cell. This enhancement reduces subgap thermal transfer while\nmaintaining power output.\n
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".