Addition of Zr to Photochromic WO3-Based Composite Smart Window to Improve the Coloring and Bleaching Rates
Bibliographic record
Abstract
WO3-based photochromic composite films were fabricated using transparent urethane resin and iso-polytungstic acid peroxide (W-IPA). We attempted to improve its photochromic properties by adding elements on the 5th period: Y(+3), Zr(+4), Nb(+5), and Mo(+6). When yttrium chloride was added to the raw material, W-IPA, yttrium could not be added to WO3 because it reacted with W-IPA. The coloration rate of WO3 photochromic composites increased with the addition of Zr, Nb, and Mo. The bleaching rate of the WO3-based composite films increased significantly with Zr addition, approximately 2.3 times faster than that of the non-doped film. The photochromic properties of the Zr/WO3 composite films were also evaluated under sun light irradiation. All the films exhibited reversible photochromic properties in a one-day cycle. The degree of coloration significantly changed with sun light irradiation in summer (during the summer solstice) and winter (during the winter solstice). In summer, the coloration properties were adequate after 25 min of sunlight irradiation, and the bleaching properties returned to near the initial transmittance after 6 h of storage in the dark. These results indicate that the Zr/WO3 composite films could be effectively controlled under sunlight in both winter and summer. Therefore, these films can be applied as smart windows.
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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".