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dc.contributor.authorKim, Hye-Bin-
dc.contributor.authorEhsan, Muhammad Fahad-
dc.contributor.authorAlshawabkeh, Akram N.-
dc.contributor.authorKim, Jong-Gook-
dc.date.accessioned2025-05-22T05:30:05Z-
dc.date.available2025-05-22T05:30:05Z-
dc.date.created2025-05-21-
dc.date.issued2025-08-
dc.identifier.issn0960-8524-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/152449-
dc.description.abstractAlum sludge (AlS) has emerged as an effective adsorbent for anionic contaminants, with traditional activation methods like acid/base treatments and calcination employed to enhance its adsorption capacity. However, these approaches encounter significant drawbacks, including excessive waste generation, structural degradation, and limited efficacy for cationic contaminants. To overcome these challenges, this study proposes electrochemical activation as a sustainable method to enhance alum sludge adsorption performance by generating oxygen containing functional groups (O-FGs) on its surface. In particular, cathodic activated AlS (E-AlS) leads to the formation of hydroxyl (-OH) and carboxyl (-COOH) groups, which served as key active sites for Pb(II) adsorption through complexation mechanisms. E-AlS effectively removed both Pb(II) and As within 4 h, showcasing its dual functionality for cationic and anionic contaminants. While HCl-and KOH-activated AlS also achieved 100 % Pb (II) removal, they caused substantial aluminum (Al) leaching, exceeding 1,000 mg/L, due to structural instability. In contrast, E-AlS minimized Al leaching, preserved structural integrity, and exhibited a 6.5-fold higher Pb (II) adsorption capacity than raw AlS. X-ray photoelectron spectroscopy (XPS) and machine learning (ML) validated the enhanced adsorption performance of E-AlS. These findings highlight electrochemical activation as cost-effective and environmentally friendly remediation.-
dc.languageEnglish-
dc.publisherElsevier BV-
dc.titleElectrochemical activation of alum sludge for the adsorption of lead (Pb(II)) and arsenic (As): Mechanistic insights and machine learning (ML) analysis-
dc.typeArticle-
dc.identifier.doi10.1016/j.biortech.2025.132563-
dc.description.journalClass1-
dc.identifier.bibliographicCitationBioresource Technology, v.430-
dc.citation.titleBioresource Technology-
dc.citation.volume430-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.identifier.wosid001477759000001-
dc.identifier.scopusid2-s2.0-105002896681-
dc.relation.journalWebOfScienceCategoryAgricultural Engineering-
dc.relation.journalWebOfScienceCategoryBiotechnology & Applied Microbiology-
dc.relation.journalWebOfScienceCategoryEnergy & Fuels-
dc.relation.journalResearchAreaAgriculture-
dc.relation.journalResearchAreaBiotechnology & Applied Microbiology-
dc.relation.journalResearchAreaEnergy & Fuels-
dc.type.docTypeArticle-
dc.subject.keywordPlusWASTE-WATER TREATMENT-
dc.subject.keywordPlusREMOVAL-
dc.subject.keywordPlusPHOSPHATE-
dc.subject.keywordPlusREUSE-
dc.subject.keywordPlusSIO2-
dc.subject.keywordPlusPH-
dc.subject.keywordPlusENHANCED ADSORPTION-
dc.subject.keywordAuthorElectrochemical activation-
dc.subject.keywordAuthorAdsorption-
dc.subject.keywordAuthorLead-
dc.subject.keywordAuthorFunctional group-
dc.subject.keywordAuthorMachine learning-
dc.subject.keywordAuthorAlum sludge-
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