Titania immobilized over Fe‐functionalized beta and silicalite zeolites for tetracycline photocatalytic degradation under visible light
Bibliographic record
Abstract
Abstract Titania photocatalyst is widely employed in the removal of organic pollutants from water streams. However, TiO2 suffers from agglomeration and is mostly active under UV light resulting in low system efficiency. In this study, we prepared sol–gel TiO2 immobilized on beta (BEA) and silicalite zeolites for the photocatalytic degradation of tetracycline antibiotics under visible light. Ferric ions were incorporated into the supported Titania photocatalyst via ion exchange method to enhance its visible light absorption. The deposition of the Titania over BEA zeolite greatly enhanced its adsorption efficiency (from 5.6% to 24%) and surface area (from 90 to 305.9 m2/g) while silicalite support only slightly affected the adsorption of Titania. The scanning electron microscope (SEM) characterization of the photocatalysts indicated that the zeolite structure was conserved after modification and the UV–VIS DRS characterization confirmed the enhancement of visible light absorption. The TiO2/Fe‐Beta was able to degrade 100% of tetracycline (TC) in solution under blue light after 90 min compared to only 30% by TiO2/Fe‐silicalite and 28% by TiO2. When the weight percentage of TiO2 in the TiO2/Fe‐silicalite photocatalyst increased from 20% to 60%, its efficiency increased from 87% to 99% after 300 min. Similar results were also obtained under white light, where the TiO2/Fe‐Beta achieved the highest efficiency (81.5%) as compared to TiO2/Fe‐silicalite (66.6%) and TiO2 (44.7%). We attribute this enhanced performance of TiO2/Fe‐Beta to enhanced adsorption capacity due to BEA immobilization and improved visible light absorption.
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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".