Efficiency-optimized near-field thermophotovoltaics using InAs and InAsSbP
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Bibliographic record
Abstract
Waste heat is a free and abundant energy source, with 15% of global total energy use existing as waste heat above 600 K. For 600–900 K temperature range, near-field thermophotovoltaics (NFTPVs) are theorized to be the most effective technology to recycle waste heat into electrical power. However, to date, experimental efficiencies have not exceeded 1.5%. In this work, we optimize the efficiency of three modeled InAs/InAsSbP-based room-temperature NFTPV devices positioned 0.1 μm from a 750 K p-doped Si radiator. We couple a one-dimensional fluctuational electrodynamics model for the near field optics to a two-dimensional drift-diffusion model, which we validated by reproducing measured dark current–voltage curves of two previously published InAs and InAsSbP devices. The optimized devices show four to six times higher above-bandgap energy transfer compared to the blackbody radiative limit, yielding enhanced power density, while simultaneously lowering parasitic sub-bandgap energy transfer by factors of 0.68–0.85. Substituting InAs front- and back-surface field layers with InAsSbP show 1.5- and 1.4-times higher efficiency and power output, respectively, from lowered parasitic diffusion currents. Of our three optimized designs, the best performing device has a double heterostructure with an n–i–p doping order from front to back. For radiator-thermophotovoltaic gaps of 0.01–10 μm and radiators within 600–900 K, this device has a maximum efficiency of 14.2% and a maximum power output of 1.55 W/cm2, both at 900 K. Within 600–900 K, the efficiency is always higher with near- vs far-field illumination; we calculate up to 3.7- and 107-times higher efficiency and power output, respectively, using near-field heat transfer.
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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 | 0.000 | 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 it