Mixed Nanostructured Ti‐W Oxides Films for Efficient Electrochromic Windows
Bibliographic record
Abstract
With the aim to enhance the electrochromic (EC) efficiency and electrochemical stability of electrochromic devices (ECD), mixed nanostructured TiO2/WO3 films were prepared by an electrochemical deposition method with the purpose of adding WO3 nanoparticles to porous nanocrystalline doctor‐blade TiO2 (nc‐TiO2) films. The results of the characterization of electrochromic properties in 1 M LiClO4 + propylene carbonate (LiClO4 + PC) of both the nc‐TiO2/F‐doped tin oxide (FTO) and WO3/TiO2/FTO configurations showed the reversible coloration and bleaching of the ECDs. The response time of the ECD coloration of WO3/TiO2/FTO was found to be as small as 2 sec, and its coloration efficiency (CE) as high as 35.7 cm2 × C−1. By inserting WO3 nanoparticles into the porous TiO2 structures, WO3/TiO2 heterojunctions were formed in the films, consequently enabling both the CE and electrochemical stability of the working electrodes to be considerably enhanced. Since a large‐area WO3/TiO2 can be prepared by the doctor‐blade technique followed by the electrochemical deposition process, mixed nanostructured Ti‐W oxides electrodes constitute a good candidate for smart window applications, taking advantage of the excellent coloration and stability properties as well as the simple and economical fabrication process involved.
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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".