Harnessing AI to revolutionize photocatalytic degradation of Tetracycline via optimized UV/ZrO2/NaOCl reaction pathways
Abstract This paper assesses the presentation of Gradient Boosting Regression (GBR), Ridge Regression (RR), and Particle Swarm Optimization (PSO) models in improving the photocatalytic destruction of antibiotic utilizing a UV/ZrO₂/NaOCl system. The GBR model indicated the strength of exhibited preci...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-03814-x |
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| author | Amir Sheikhmohammadi Saeed Hosseinpour Zahra Jalilzadeh Hossein Azarpira Mahmood Yousefi |
| author_facet | Amir Sheikhmohammadi Saeed Hosseinpour Zahra Jalilzadeh Hossein Azarpira Mahmood Yousefi |
| author_sort | Amir Sheikhmohammadi |
| collection | DOAJ |
| description | Abstract This paper assesses the presentation of Gradient Boosting Regression (GBR), Ridge Regression (RR), and Particle Swarm Optimization (PSO) models in improving the photocatalytic destruction of antibiotic utilizing a UV/ZrO₂/NaOCl system. The GBR model indicated the strength of exhibited precision, with high R² and Explained Variance Score (EVS) esteems, however gave indications of overfitting. Remarkably, the RR model obtained had many significant values and the model had a high data fitness with an R² of 0.81; however, error bars were observed in some areas that could be optimized by fine-tuning. Feature significance examination indicated that X2, X1, and X5 fundamentally affected the model’s performance. At last, PSO was instrumental in finding the ideal limit mix to support removal performance. Altogether the presented models help to improve the photocatalytic degradation process and provide useful tools for further investigation in this field. |
| format | Article |
| id | doaj-art-d512c52f71814ca7accfd8cb8b9b54f3 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d512c52f71814ca7accfd8cb8b9b54f32025-08-20T02:03:35ZengNature PortfolioScientific Reports2045-23222025-05-0115112210.1038/s41598-025-03814-xHarnessing AI to revolutionize photocatalytic degradation of Tetracycline via optimized UV/ZrO2/NaOCl reaction pathwaysAmir Sheikhmohammadi0Saeed Hosseinpour1Zahra Jalilzadeh2Hossein Azarpira3Mahmood Yousefi4Department of Environmental Health Engineering, School of Health, Khoy University of Medical SciencesDepartment of Environmental Health Engineering, School of Public Health, Urmia University of Medical SciencesStudent Research Committee, Khoy University of Medical SciencesDepartment of Environmental Health Engineering, Social Determinants of Health Research Center, Saveh University of Medical SciencesDepartment of Environmental Health Engineering, School of Health, Khoy University of Medical SciencesAbstract This paper assesses the presentation of Gradient Boosting Regression (GBR), Ridge Regression (RR), and Particle Swarm Optimization (PSO) models in improving the photocatalytic destruction of antibiotic utilizing a UV/ZrO₂/NaOCl system. The GBR model indicated the strength of exhibited precision, with high R² and Explained Variance Score (EVS) esteems, however gave indications of overfitting. Remarkably, the RR model obtained had many significant values and the model had a high data fitness with an R² of 0.81; however, error bars were observed in some areas that could be optimized by fine-tuning. Feature significance examination indicated that X2, X1, and X5 fundamentally affected the model’s performance. At last, PSO was instrumental in finding the ideal limit mix to support removal performance. Altogether the presented models help to improve the photocatalytic degradation process and provide useful tools for further investigation in this field.https://doi.org/10.1038/s41598-025-03814-xPhotocatalytic degradationTetracycline removalUV/ZrO₂/NaOCl optimizationArtificial intelligence modelsReaction pathway enhancement |
| spellingShingle | Amir Sheikhmohammadi Saeed Hosseinpour Zahra Jalilzadeh Hossein Azarpira Mahmood Yousefi Harnessing AI to revolutionize photocatalytic degradation of Tetracycline via optimized UV/ZrO2/NaOCl reaction pathways Scientific Reports Photocatalytic degradation Tetracycline removal UV/ZrO₂/NaOCl optimization Artificial intelligence models Reaction pathway enhancement |
| title | Harnessing AI to revolutionize photocatalytic degradation of Tetracycline via optimized UV/ZrO2/NaOCl reaction pathways |
| title_full | Harnessing AI to revolutionize photocatalytic degradation of Tetracycline via optimized UV/ZrO2/NaOCl reaction pathways |
| title_fullStr | Harnessing AI to revolutionize photocatalytic degradation of Tetracycline via optimized UV/ZrO2/NaOCl reaction pathways |
| title_full_unstemmed | Harnessing AI to revolutionize photocatalytic degradation of Tetracycline via optimized UV/ZrO2/NaOCl reaction pathways |
| title_short | Harnessing AI to revolutionize photocatalytic degradation of Tetracycline via optimized UV/ZrO2/NaOCl reaction pathways |
| title_sort | harnessing ai to revolutionize photocatalytic degradation of tetracycline via optimized uv zro2 naocl reaction pathways |
| topic | Photocatalytic degradation Tetracycline removal UV/ZrO₂/NaOCl optimization Artificial intelligence models Reaction pathway enhancement |
| url | https://doi.org/10.1038/s41598-025-03814-x |
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