Impact of Various Thermochromic Materials for Thermal Storage Applications on Multilayer Thin Film Coatings
Bibliographic record
Abstract
Solar radiation causes a remarkably high amount of thermal energy to be gained or lost in buildings.Appropriate window layout and design as well as intelligent roof coatings help a building maintain a suitable temperature.Applied to glass or roof surfaces, single-or multilayer thin film coatings with spectrally desired properties can significantly increase a building's energy efficiency.Thermochromic materials present some advantages due to their reversible color and phase change behavior near ambient temperature.For example, a thermochromic coating on a roof's surface can change color (black to white) reversibly when heated at around 30 o C, reflecting solar radiation and reducing a building's cooling needs.In the present study, various thermochromic coatings on glass slides are investigated.In this work, the effect of non-toxic titanium dioxide (TiO2), used as a UV radiation protective coating on thermochromic particles (such as a three-component blue dye synthesized in the lab or a commercially available black dye), is discussed.The composite coatings of the chosen components can reduce a building's cooling and heating energy needs and provide an environmentally sustainable solution for a green economy.Several physico-chemical characterization techniques were used to understand the surface, interfacial, spectroscopic, and thermal behavior of TiO2 or SiO2 coatings and phase change thermochromic materials for applications in thermal storage and enhanced energy efficiency.
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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) | 0.001 | 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".