Heat Generated Using Luminescent Solar Concentrators for Building Energy Applications
Bibliographic record
Abstract
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.
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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) | 0.002 | 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".