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Record W3093641434 · doi:10.3390/en13215574

Heat Generated Using Luminescent Solar Concentrators for Building Energy Applications

2020· article· en· W3093641434 on OpenAlexafffund
Quinn Daigle, Paul G. O’Brien

Bibliographic record

VenueEnergies · 2020
Typearticle
Languageen
FieldChemistry
TopicPhotochemistry and Electron Transfer Studies
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRenewable energyPhotovoltaic systemSolar energyThermalRadiationMaterials scienceElectricity generationOpticsOptoelectronicsThermal energyElectricityEngineering physicsEnvironmental scienceProcess engineeringElectrical engineeringPower (physics)EngineeringPhysicsMeteorology

Abstract

fetched live from OpenAlex

Luminescent solar concentrators (LSCs) are a promising technology for integration and renewable energy generation in buildings because they are inexpensive, lightweight, aesthetically versatile, can concentrate both direct and diffuse light and offer wavelength-selective transparency. LSCs have been extensively investigated for applications involving photovoltaic electricity generation. However, little work has been done to investigate the use of thermal energy generated at the edges of LSCs, despite the potential for harnessing a broad range of solar thermal energy. In this work, Newton’s law of cooling is used to measure the thermal power generated at the edge of LSC modules subjected to solar-simulated radiation. Results show that the dye in single-panel LSC modules can generate 17.9 W/m2 under solar-simulated radiation with an intensity of 23.95 mW/cm2 over the spectral region from 360 to 1000 nm. Assuming a mean daily insolation of 5 kWh/m2, the dye in the single-panel LSC modules can generate ~100 kWh/m2 annually. If the surface area of a building is comparable to its floor space, thermal energy generated from LSCs on the buildings surface could be used to substantially reduce the buildings energy consumption.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.780

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 head0.023
GPT teacher head0.251
Teacher spread0.229 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations16
Published2020
Admission routes2
Has abstractyes

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