Photothermal management of mechano-chromic window based on stretchable metasurface
Bibliographic record
Abstract
As global climate change and the energy crisis intensify, the development of a window that effectively transmits visible (VIS) light and simultaneously optimizes near-infrared (NIR) reflectance and atmospheric window (AW) emissivity has become an effective solution to increase energy efficiency and improve living comfort. In this paper, we propose a novel mechano-chromic window-driven photothermal management system using a composite array structure composed of polydimethylsiloxane substrate and cladding of Si 3 N 4 and Ag. The parametric structure is systematically optimized by using the particle swarm optimization algorithm. The system can adjust the transmittance and emissivity of the solar spectrum and AW at different temperatures by mechanical stretching. When unstretched at high temperatures, the window has 57.4% VIS transmittance, while the NIR transmittance drops to 17.4%. After stretching the structure in relatively warm weather, the VIS transmittance increased to 69.8% and the NIR transmittance increased to 47.5%. In the AW, the structure before and after stretching exhibits high emissivity to achieve the radiative cooling effect. The design shows remarkable flexibility in balancing VIS transmittance and AW emissivity. This technology has the potential for a vast application in thermoregulation in buildings, transportation and public facilities.
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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".