TiO<sub>2</sub>-coated Hollow Glass Microspheres with Superhydrophobic and High IR-reflective Properties Synthesized by a Soft-chemistry Method
Bibliographic record
Abstract
This manuscript proposes a soft-chemistry method to develop superhydrophobic and highly IR-reflective hollow glass microspheres (HGM). The anatase TiO2 and a superhydrophobic agent were coated on the HGM surface in one step. TBT and PFOTES were selected as the Ti source and the superhydrophobic agent, respectively. They were both coated on the HGM, and after the hydrothermal process, the TBT turned to anatase TiO2. In this way, a PFOTES/TiO2-coated HGM (MCHGM) was prepared. For comparison, PFOTES single-coated HGM (F-SCHGM) and TiO2 single-coated HGM (Ti-SCHGM) were synthesized as well. The PFOTES and TiO2 coatings on the HGM surface were demonstrated through X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive detector (EDS) characterizations. The MCHGM showed a higher contact angle (153°) but a lower sliding angle (16°) than F-SCHGM, with a contact angle of 141.2° and a sliding angle of 67°. In addition, both Ti-SCHGM and MCHGM displayed similar IR reflectivity values, which were about 5.8% higher than the original HGM and F-SCHGM. Also, the PFOTES coating barely changed the thermal conductivity. Therefore, F-SCHGM, with a thermal conductivity of 0.0479 W/(m·K), was quite like the original HGM, which was 0.0475 W/(m·K). MCHGM and Ti-SCHGM were also similar. Their thermal conductivity values were 0.0543 W/(m·K) and 0.0543 W/(m·K), respectively. The TiO2 coating slightly increased the thermal conductivity, but with the increase in reflectivity, the overall heat-insulation property was enhanced. Finally, since the IR-reflecting property is provided by the HGM coating, if the coating is fouled, the reflectivity decreases. Therefore, with the superhydrophobic coating, the surface is protected from fouling, and its lifetime is also prolonged.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".