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Investigation of the influencing factors and optimization study for the removal of levofloxacin in water using commercial TiO2 photocatalyst under UV-LED irradiation
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Abstract
Photocatalysis holds significant promise for effectively eliminating toxins, hard-to-biodegrade, and persistent organic pollutants in water. In this study, we used commercial TiO2 as a photocatalyst under UVA-LED irradiation to remove levofloxacin from water. In the photocatalytic tests, levofloxacin removal efficiency in water reached 68.21% after 120 minutes at pH 4 with an antibiotic/catalyst concentration ratio of 1:20. ANOVA revealed that the model achieved significance, as indicated by a p-value of 0.009. LEVO degradation under UVA-LED is three times higher than that under natural light. Optimal conditions for LEVO removal include pH 4, LEVO/TiO2 ratio of 1:20, and UVA-LED irradiation. The regression model predicts LEVO removal with high accuracy (R2 = 0.8469). This study highlights the use of photocatalysis with TiO2 as a promising approach for pharmaceutical pollutant removal, emphasizing the need for further research in sustainable water treatment technologies.
Title: Investigation of the influencing factors and optimization study for the removal of levofloxacin in water using commercial TiO2 photocatalyst under UV-LED irradiation
Description:
Abstract
Photocatalysis holds significant promise for effectively eliminating toxins, hard-to-biodegrade, and persistent organic pollutants in water.
In this study, we used commercial TiO2 as a photocatalyst under UVA-LED irradiation to remove levofloxacin from water.
In the photocatalytic tests, levofloxacin removal efficiency in water reached 68.
21% after 120 minutes at pH 4 with an antibiotic/catalyst concentration ratio of 1:20.
ANOVA revealed that the model achieved significance, as indicated by a p-value of 0.
009.
LEVO degradation under UVA-LED is three times higher than that under natural light.
Optimal conditions for LEVO removal include pH 4, LEVO/TiO2 ratio of 1:20, and UVA-LED irradiation.
The regression model predicts LEVO removal with high accuracy (R2 = 0.
8469).
This study highlights the use of photocatalysis with TiO2 as a promising approach for pharmaceutical pollutant removal, emphasizing the need for further research in sustainable water treatment technologies.
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