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Record W3175329791 · doi:10.1038/s41598-022-19448-2

Transparent planar solar absorber for winter thermal management

2022· article· en· W3175329791 on OpenAlexafffund
Muhammad Saad Asad, Md. Zahangir Alam

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPlanarThermalThermal management of electronic devices and systemsMaterials scienceComputer scienceEnvironmental scienceMedicineMeteorologyPhysicsComputer graphics (images)EngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Indoor heating during winters accounts for a significant portion of energy consumed by buildings in regions of cold climate. Development of transparent coatings for windows that efficiently harvest solar energy can play a major role in reducing energy consumption and fuel costs incurred for winter heating. In recent years, there has been a great research effort towards designing transparent solar absorber coatings using nanophotonic structures. The potential of coatings based on planar multilayer structures, however, has received very little attention. In this work we investigate the performance of planar multilayer thin films using low-cost materials for design of transparent solar absorber window coatings. Our study led to the proposal of two planar multilayer designs. Simulation results predict that an increase in surface temperature by 27 K and 25 K, while maintaining mean visible transmittance of over 50% is possible using these designs. These results illustrate the great promise planar multilayer structures hold for winter thermal management of buildings.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.243
Teacher spread0.215 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2022
Admission routes2
Has abstractyes

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