Ultrasonication process‐induced highly dispersed <scp> SiO <sub>2</sub> </scp> on <scp>ZnO</scp> nanoparticles for improving catalyst dispersibility and photocatalyst performances
Bibliographic record
Abstract
Abstract Creating a highly dispersed ZnO is one strategy to improve the photocatalytic activity of this material. However, pure ZnO always presents in an agglomerate state. The current study aimed to improve the dispersibility by adding SiO 2 particle through ultrasonication. The amount of SiO 2 particle is crucial to maintain a good dispersibility. The impact of silica ratio and ultrasonication time on particle morphology and photocatalytic performance were studied to obtain a nanocomposite with a good dispersibility. Composites were prepared with silica mass ratios ranging from 0.25% to 1.15% and exposed to ultrasonication for 10–240 min. Characterization methods, including x‐ray diffraction (XRD), Fourier transform infrared (FTIR), dynamic light scattering (DLS), and transmission electron microscopy (TEM), revealed that a 0.25% silica ratio resulted in smaller, uniformly distributed particles. Longer ultrasonication enhanced cavitation effects, improving particle dispersion and reducing agglomeration. Photocatalytic tests showed composites with lower silica ratios and optimized ultrasonication achieved 96.5% methylene blue (MB) degradation within 60 min of sunlight exposure ( k = 0.0200 min −1 ), linked to increased surface area and ZnO dispersion. Photoluminescence (PL) analysis confirmed that 0.25% silica produced the highest PL intensity, correlating with superior photocatalytic activity. This study emphasizes the importance of optimizing silica ratio and ultrasonication time for designing efficient photocatalytic materials.
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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".