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Record W4399667320 · doi:10.11159/jffhmt.2024.010

The Impact of Dispersed Nanoparticles on Long Wavelength Heat Radiation through Opaque and Transparent Passive Cooling Skylight Glass

2024· article· en· W4399667320 on OpenAlex
Gopalakrishna Gangisetty, Jan‐Henrik Smått, Ron Zevenhoven

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Fluid Flow Heat and Mass Transfer · 2024
Typearticle
Languageen
FieldEngineering
TopicThermal Radiation and Cooling Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsOpacitySkylightWavelengthMaterials scienceRadiationOpticsNanoparticleThermal radiationBlack-body radiationHeat waveOptoelectronicsNanotechnologyPhysicsThermodynamicsGeologyMechanical engineering

Abstract

fetched live from OpenAlex

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 <sub>2</sub>-SiO <sub>2</sub> or ZnS-SiO <sub>2</sub> 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 <sub>2</sub>-SiO <sub>2</sub> NPs and 0.3 mg/m² for ZnS-SiO <sub>2</sub> 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 <sub>2</sub> SiO <sub>2</sub> NPs and 1.50 % with ZnS-SiO <sub>2</sub> NPs, while transmittance is 69.6% with TiO <sub>2</sub> -SiO <sub>2</sub> NPs and 85.5 % with ZnSSiO <sub>2</sub> ZnSSiO <sub>2</sub> NPs. Solar Reflective Index (SRI) quantifies solar radiation reflection, with maximum SRI being 70.5 with TiO <sub>2</sub> -SiO <sub>2</sub> NPs and 68.2 with ZnS-SiO <sub>2</sub> NPs.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.372

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.241
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it