Transparent Photonic Crystal Heat Mirrors for Solar Thermal Applications
Bibliographic record
Abstract
Numerical calculations are performed to determine the potential of using one-dimensional transparent photonic crystal heat mirrors (TPCHMs) as transparent coatings for solar receivers. At relatively low operating temperatures of 500 K, the TPCHMs investigated herein do not provide a significant advantage over conventional transparent heat mirrors that are made using transparent conducting oxide films. However, the results show that TPCHMs can enhance the performance of transparent solar receiver covers at higher operating temperatures. At 1000 K, the amount of radiation reflected by a transparent cover back to the receiver can be increased from 40.4% to 60.0%, without compromising the transmittance of solar radiation through the cover, by using a TPCHM in the place of a conventional transparent mirror with a In2O3:Sn film. For a receiver operating temperature of 1500 K, the amount of radiation reflected back to the receiver can be increased from 25.7% for a cover that is coated with a In2O3:Sn film to 57.6% for a cover with a TPCHM. The TPCHM that is presented in this work might be useful for high-temperature applications where high-performance is required over a relatively small area, such as the cover for evacuated receivers or volumetric receivers in Sterling engines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".