The Effect of Interspersed Nanoparticles on Long Wavelength Heat Radiation through Opaque and Transparent Passive Skylight Glass
Bibliographic record
Abstract
The materials of passive radiant cooling (PRC) skylights play a critical role in achieving the PRC performance required for building cooling.However, finding window materials that provide a cooling effect at an affordable price remains a challenge, although it is a prerequisite for widespread market acceptance.Research conducted at bo Akademi University led to the development of a passive cooling skylight technology that enables higher heat transfer rates by combining thermal convection and thermal radiation (Qconv > > Qcond) instead of thermal conduction and thermal radiation.This prototype enabled significant PRC during the night, but extending its use to PRC during the day presents a greater challenge.A coating of dispersed TiO2-SiO2 or ZnS-SiO2 nanoparticles (NPs) was used on simple window glass (WG) and on long-wavelength (LW) translucent Cleartran ZnS glass to control the surface temperature (TS) and [absorptivity ()/emissivity ()] at different heat source temperatures.These NPs were selected for their chemical, optical, and thermal properties, which make them suitable for a wide range of applications, including radiative cooling.By capturing IR images with a thermal imaging camera (wavelength range 7.5-14 m), heat flux measurements were experimentally performed for both window and Cleartran glasses with NPs randomly distributed on one side of the glass surface.The amount of NP (in g/m 2 ) and surface coverage by NPs (in %) were determined by SEM analysis of the window samples in this work.The results showed that the average amount of TiO2-SiO2 NP and ZnS-SiO2 was 0.25 mg/m2 and 0.3 mg/m 2 , respectively, and the surface coverage was 8% for TiO2-SiO2 NPs and 15% for ZnS-SiO2 NPs.Increasing the surface coverage of NPs and optimizing the interspersed NPs could still increase the net cooling performance of a skylight to be effective for daytime use.While for simple glass the heat flux is increased by a factor of 2 to 4, for the Cleartran glass the change is only a few %.However, this work needs to be extended to shortwave radiation (SW) for further investigation to verify the performance of the material under solar irradiation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".