Preparation and characterization of natural zeolite supported nano TiO <sub>2</sub> photocatalysts by a modified electrostatic self‐assembly method
Bibliographic record
Abstract
Natural zeolite supported nano TiO 2 photocatalysts were prepared by a modified electrostatic self‐assembly (ESA) method. First, γ‐mercaptopropyltrimethoxysilane with sulfhydryl (―SH) functional groups was modified on the zeolite powders by using a ‘dry process’. Second, silane with ―SH functional groups was oxidized to sulfonate (―SO 3 H) groups by using a hydrogen peroxide/glacial acetic acid mixed solution, and the surface of ―SO 3 H silane–zeolite was electronegative charged due to the ionization of ―SO 3 H. Third, the hydrolytic titanium polycation from TiCl 4 solution assembled onto the electronegative charged zeolite under electrostatic attraction in the reaction solutions. Finally, zeolite supported nano TiO 2 photocatalysts can be obtained after the above compounds calcined at certain temperature. The samples were characterized by X‐ray diffraction (XRD), Brunauer–Emmett–Teller (BET) surface areas, Fourier transform infrared spectroscopy (FT‐IR), X‐ray photoelectron spectroscopy (XPS) and X‐ray fluorescence (XRF). The photocatalytic activities of the samples were evaluated by the degradation of methyl orange in aqueous solution. The results showed that ESA method effectively improved the composite efficiency of zeolite with TiO 2 . The photocatalysts prepared by ESA method exhibited higher photocatalytic and recycling activities than that of traditional method. Copyright © 2014 John Wiley & Sons, Ltd.
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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) | 0.000 | 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 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".