A novel approach for immobilizing Ag/ZnO nanorods on a glass substrate: Application in solar light-driven degradation of micropollutants in water
Bibliographic record
Abstract
One of the main challenges in applying photocatalysts for water treatment is the complex separation and recycling process. In this study, we developed highly stable, porous zinc oxide nanorods (ZnO NRs) immobilized on glass vials using a solvent exchange process (SEP) and hydrothermal calcination. Key parameters, including oleic acid concentration and hydrothermal growth time, were optimized to maximize the active surface area, significantly enhancing photodegradation performance. Under the best conditions, ZnO NRs-coated vials achieved nearly 100% degradation of sulfamethoxazole (SMX) in 10 h of simulated solar irradiation. Depositing silver nanoparticles on the surface of ZnO NRs (Ag/ZnO NRs) further improved performance, reducing degradation time to 4 h and increasing photocatalyst stability. The Ag/ZnO NRs-coated vials, optimized with an Ag precursor concentration of 0.05 M, also demonstrated high degradation rates (>99%) for eight organic micropollutants at environmentally relevant concentrations over multiple reuse cycles and with minimal metal leaching. This study presents an innovative, tunable method for immobilizing photocatalysts on glass substrates, offering high surface area, excellent photocatalytic activity, and mechanical properties, making it highly suitable for water treatment applications.
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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".