Efficiency-optimized near-field thermophotovoltaics using InAs and InAsSbP
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".