Approaching the Theoretical Maximum Performance of Highly Transparent Thermochromic Windows
Bibliographic record
Abstract
Thermochromic window coatings represent a promising technology to improve the energy efficiency of buildings in intermediate climates. With the technology approaching market introduction it is important to investigate its performance limits within smart windows and to identify existing development challenges. Here we analyze the theoretical maximum performance of thermochromic window coatings that modulate IR transmission whilst retaining high visible transparency. The set limitations lead to a theoretical maximum solar modulation of 39.1%. Within an insulated glazing unit (IGU), where at least 2 glass panes and a conventional low-e coating are required, this value is further reduced to 12.9%. We show that by carefully selecting a low-e coating with the highest compatibility to a thermochromic coated glass and by allowing 10% of modulation in the visible spectral range, the theoretical maximum can be increased to 23.1%, illustrating the importance to codesign and match both coatings within a smart window to reach optimum performance. Furthermore, we compared our current best-performing VO2:SiO2 composite coating within an IGU to the theoretical maximum. The analysis shows that with a solar modulation of 13.4%, the coating is currently at 59% of the theoretical maximum. Finally, we propose and discuss several strategies to proceed further toward the theoretical maximum.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".