Active Broadband Absorber Based on Phase-Change Materials Optimized via Evolutionary Algorithm
Bibliographic record
Abstract
This article proposes a temperature-controlled absorber based on VO2, which consists of five layers: a disk-shaped VO2 layer array, a dielectric layer, a circular hole VO2 array, a SiO2 layer, and a gold substrate from top to bottom. We optimized the thickness of the other four layers of the absorber, except for the gold layer, using PSO. After ten iterations, we determined that the optimal parameters for the top-to-bottom four-layer thicknesses were 0.183 μm, 0.452 μm, 0.557 μm and 1.994 μm. At this point, our absorber reached the optimal absorption parameters, and we plotted the absorption spectrum under these conditions. We found that the absorption rate at 29.1–47.2 THz was higher than 90%, and the absorption bandwidth was as high as 18.1 THZ. This frequency band covers most of the atmospheric window area (23–37.5 THz), so it will have good practicality. At 30.8 THz and 43.12 THz, there were perfect absorption peaks with absorption rates of 99.99% and 99.99%, respectively. We explained the cause of absorption from the perspective of electric field, and then we studied the change in the absorption curve of the absorber when the temperature of VO2 changed, and we can directly observe the changes in the electric field to explain this. Finally, we can tune the bandwidth and absorption rate of the absorber by changing the structure of the VO2 pattern. After comparing with other absorbers developed in recent years, our absorber still has good competitiveness, and we believe that our solution is expected to have outstanding performance in fields such as photothermal conversion and thermal stealth in the future.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".