High-Temperature Selective Emitter Design and Materials: Titanium Aluminum Nitride Alloys for Thermophotovoltaics
Bibliographic record
Abstract
The efficiency of a thermophotovoltaic (TPV) system depends critically upon the spectral selectivity and stability of an emitter, which may operate most effectively at temperatures in excess of 1000 °C. We computationally design and experimentally demonstrate a novel selective emitter design based on multilayer nanostructures, robust to off-normal emission angles. A computational search of the material and temperature compatibility space of simple emitter designs motivates new material classes and identifies several promising multilayer nanostructure designs for both TPV absorber and emitter applications. One such structure, comprising a thin (<100 nm) tunable Ti x Al 1– x N (TiAlN) absorber and refractory oxide Bragg reflector is grown on W metal foil. In agreement with simulations, the emitter achieves record spectral efficiency (43.4%) and power density (3.6 W/cm 2 ) for an emitter with at least 1 h of high temperature (>800 °C) operation.
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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 | 0.000 |
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".