Comparative Analysis of Graded‐Index and Quarter‐Wave One‐Dimensional Photonic Crystal Filters for GaSb Thermophotovoltaic Cells
Bibliographic record
Abstract
Thermophotovoltaic (TPV) systems are important for the clean energy transition due to their applications in waste heat recovery, solar energy harvesting, and thermal energy grid storage. This study presents a comprehensive investigation of the performance of TPV systems equipped with one‐dimensional photonic crystal filters. The optical characteristics of the filter comprised of porous SiO 2 nanoparticle and dense ZrO 2 films are numerically evaluated. The results demonstrate the choice of filter significantly influences the emitter temperature, power density, system efficiency, and spectral performance. Further, the analysis underscores the inherent tradeoff in designing optical filters between achieving elevated in‐band transmittance and maximizing out‐of‐band reflectance. Under a constant flux of 60 W cm −2 from a heat source a conventional double‐stack quarter‐wave optical filter achieves a TPV system efficiency of 28.9%. In contrast, an optimized filter structure, consisting of a double‐stack modified quarter‐wave optical filter, increases TPV system efficiency to 29.1%. Introducing the optimized filter with a graded index profile into the TPV system as a photon recycling tool results in a 27% TPV system efficiency. This is a significant improvement compared to the case without a filter, which has a system efficiency of 15.8%.
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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 | 0.001 | 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".