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Electrochemical activation of alum sludge for the adsorption of lead (Pb(II)) and arsenic (As): Mechanistic insights and machine learning (ML) analysis

  • Hye Bin Kim
  • , Muhammad Fahad Ehsan
  • , Akram N. Alshawabkeh
  • , Jong Gook Kim*
  • *Corresponding author for this work
  • National Institute of Animal Science (NIAlS)
  • Northeastern University
  • Korea Institute of Science and Technology (KIST)

Research output: Contribution to journalJournal articlepeer-review

Abstract

Alum 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 a cost-effective and environmentally friendly remediation.

Original languageEnglish
Article number132563
JournalBioresource Technology
Volume430
DOIs
StatePublished - 2025.08

Keywords

  • Adsorption
  • Alum sludge
  • Electrochemical activation
  • Functional group
  • Lead
  • Machine learning

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