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Record W4413065584 · doi:10.5539/jmsr.v14n1p70

Addition of Zr to Photochromic WO3-Based Composite Smart Window to Improve the Coloring and Bleaching Rates

2025· article· W4413065584 on OpenAlexvenueno aff
Hidetoshi Miyazaki

Bibliographic record

VenueJournal of Materials Science Research · 2025
Typearticle
Language
FieldMaterials Science
TopicTransition Metal Oxide Nanomaterials
Canadian institutionsnot available
Fundersnot available
KeywordsPhotochromismMaterials scienceSolsticeIrradiationComposite numberTransmittanceYttriumSunlightComposite materialOpticsOptoelectronicsMetallurgyNanotechnologyOxide

Abstract

fetched live from OpenAlex

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.

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)0.046
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication
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 score0.042
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0460.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.391
Teacher spread0.349 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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