Photodegradation of ciprofloxacin using an alginate/TiO2 hydrogel for water remediation
Bibliographic record
Abstract
The presence of antibiotics in aquatic environments has contributed to the emergence of multi-resistant bacteria, which can pose significant risks to human health through contaminated drinking water and food. These antibiotics enter water bodies primarily due to inefficient wastewater treatment and inadequate disposal practices in hospitals, pharmaceutical industries, and households. Therefore, the development of efficient water decontamination methods, such as adsorption and photodegradation, is crucial to mitigate water pollution. In this study, a photocatalytic hydrogel composite based on sodium alginate and TiO₂ was developed for the degradation of ciprofloxacin (CIP) in water. The hydrogel composite was characterized using various analytical techniques, including scanning electron microscopy, energy-dispersive X-ray spectroscopy, Fourier-transform infrared spectroscopy, and thermogravimetric analysis. To optimize the CIP degradation process, a fractional factorial design (2k⁵⁻²) was employed, which identified TiO₂ concentration, hydrogel dosage (g), and UV lamp distance as the most significant factors influencing degradation efficiency. The hydrogel composite's performance was assessed under varying pH conditions and CIP concentrations. Complete degradation of CIP was achieved after 300 minutes of UV exposure. Additionally, a recyclability study demonstrated the hydrogel's stability over three cycles, with 100 % CIP removal efficiency maintained in each cycle. Notably, the adsorption capacity increased from 10 % in the first cycle to 31 % in the third cycle, which may be attributed to increased porosity of the hydrogel matrix following photodegradation. • Sodium hydrogel beads loaded with TiO2 efficiently degrade ciprofloxacin. • Adsorption – photocatalysis showed good removal of ciprofloxacin from water. • The hydrogel beads demonstrated feasibility for reuse.
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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 | 0.000 | 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 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".