Results in Engineering (Dec 2024)

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

Journal volume & issue
Vol. 24
p. 102991

Abstract

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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.

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