自愈水凝胶
废水
吸附
环境修复
材料科学
氧化物
纤维素
复合数
主成分分析
化学工程
污染
复合材料
朗缪尔吸附模型
化学
环境工程
环境科学
高分子化学
有机化学
冶金
计算机科学
生物
生态学
工程类
人工智能
作者
Nimer Murshid,Omar Mouhtady,Mahmoud Abu-samha,Emil Obeid,Yahya Kharboutly,Hamdi Chaouk,Jalal Halwani,Khaled Younes
出处
期刊:Gels
[MDPI AG]
日期:2022-10-30
卷期号:8 (11): 702-702
被引量:20
摘要
Water pollution is caused by multiple factors, such as industrial dye wastewater. Dye-contaminated water can be treated using hydrogels as adsorbent materials. Recently, composite hydrogels containing metal oxide nanoparticles (MONPs) have been used extensively in wastewater remediation. In this study, we use a statistical and artificial intelligence method, based on principal component analysis (PCA) with different applied parameters, to evaluate the adsorption efficiency of 27 different MONP composite hydrogels for wastewater dye treatment. PCA showed that the hydrogel composites CTS@Fe3O4, PAAm/TiO2, and PEGDMA-rGO/Fe3O4@cellulose should be used in situations involving high pH, time to reach equilibrium, and adsorption capacity. However, as the composites PAAm-co-AAc/TiO2, PVPA/Fe3O4@SiO2, PMOA/ATP/Fe3O4, and PVPA/Fe3O4@SiO2, are preferred when all physical and chemical properties investigated have low magnitudes. To conclude, PCA is a strong method for highlighting the essential factors affecting hydrogel composite selection for dye-contaminated water treatment.
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