Synthesis and application of MgCuAl-layered triple hydroxide/carboxylated carbon nanotubes/bentonite nanocomposite for the effective removal of lead from contaminated water

Usman M. Ismail, Ahmed I. Ibrahim, Sagheer A. Onaizi, Muhammad S. Vohra*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

A novel multicomponent adsorbent nano-composite was synthesized by intercalating bentonite clay and carboxylated carbon nanotubes (CNT-COOH) into MgCuAl-layered triple hydroxide (MgCuAl-LTH), abbreviated as LTH/CNT-COOH/Bent. The produced nano-composite, its parental materials, and their binary combinations were characterized using various techniques. Pore filling was noted based on the BET analysis, hence, indicating effective component integration in the nano-composite structure. Further, distinct peaks appearing on the FTIR and XRD spectra of the single materials also appeared on the synthesized composites (including the binary combinations of the parental materials), hence, further indicating the successful synthesis of the targeted adsorbents. The effects of process parameters including pH, adsorbent dose, lead initial concentration and contact time on lead removal, were explored. In general, the application of LTH/CNT-COOH/Bent adsorbent yielded a significant improvement in lead adsorption capacity (∼260.8 mg/g) relative to all parental materials and their binary combinations. It was revealed that the adsorbent dose and lead initial concentration are the most significant factors affecting lead uptake onto the synthesized adsorbent and the adsorption kinetics was fast. The obtained results indicated that the Avrami kinetic and Redlich-Peterson isotherm models best fit the lead adsorption kinetics and isotherm experimental results, respectively. Furthermore, the response surface methodology (RSM) technique was employed for optimization of lead removal, achieving near complete removal (>99 %) at LTH/CNT-COOH/Bent dose of 0.5 g/L, lead initial concentration of 40 ppm and a time of 2 h. Additionally, the machine learning (ML) and the ANN based models for lead removal using LTH/CNT-COOH/Bent nano-composite yielded good predictions.

Original languageEnglish
Article number102991
JournalResults in Engineering
Volume24
DOIs
StatePublished - Dec 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors

Keywords

  • Bentonite
  • Carboxylated carbon nanotubes (CNT-COOH)
  • Lead adsorption
  • Machine learning (ML) modeling
  • MgCuAl-Layered triple hydroxide (MgCuAl-LTH)
  • Response surface methodology (RSM)
  • Wastewater treatment

ASJC Scopus subject areas

  • General Engineering

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