The Impact of Dispersed Nanoparticles on Long Wavelength Heat Radiation through Opaque and Transparent Passive Cooling Skylight Glass
Bibliographic record
Abstract
Selecting materials for passive radiative cooling (PRC) skylights is crucial, but finding affordable options for widespread use is challenging. Åbo Akademi University (ÅAU) introduced a passive skylight enhancing heat transfer through thermal convection and radiation, effective at night but challenging for daytime use. Dispersions of randomly distributed TiO 2-SiO 2 or ZnS-SiO 2 nanoparticles (NPs) were used on conventional window glasses (WG) and on long-wavelength (LW) translucent Cleartran® ZnS glasses (CG®) to control the surface temperature and absorptivity (α)/emissivity (ε) at different heat source temperatures. The tested NPs are known for their optical properties underwent testing via capturing IR imaging with a thermal camera (wavelength: 7.5-14 μm) and pyrgeometer (wavelength: 4.5-42 μm). LW heat flux measurements through the glass samples were taken on conventional WGs and CG®s, each with randomly dispersed NPs on one side, with the thermal camera or pyrgeometer positioned at different distances from the heat source. The data analysis compared heat fluxes from the different distances, forming the basis for determining glass sample LW emissivities via a mathematical model. Additionally, scanning electron microscope (SEM) analysis conducted on WG samples allowed for precise determination of NP quantity (in g/m²) and NP surface coverage (%). The results showed an average of 0.25 mg/m² for TiO 2-SiO 2 NPs and 0.3 mg/m² for ZnS-SiO 2 NPs, with surface coverages approximate . Although conventional WG glass exhibited a heat flux increase when using NPs of 2 to 4 times, CG® indicated only marginal change by the NPs. The findings indicate that a larger quantity, possibly five times the current amount of NPs, may be required. Further, Vis-NIR spectrophotometry measures reflectance and transmittance in the 0.25-2.5 m range for all WGs with NPs and without NPs for comparison. Maximum reflectance is 4.20% with TiO 2 SiO 2 NPs and 1.50 % with ZnS-SiO 2 NPs, while transmittance is 69.6% with TiO 2 -SiO 2 NPs and 85.5 % with ZnSSiO 2 ZnSSiO 2 NPs. Solar Reflective Index (SRI) quantifies solar radiation reflection, with maximum SRI being 70.5 with TiO 2 -SiO 2 NPs and 68.2 with ZnS-SiO 2 NPs.
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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".