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Preparation of Biomass-based Mesoporous Carbon with Higher Nitrogen-/Oxygen-chelating Adsorption for CuII Through Microwave Pre-Pyrolysis
Published on: February 12, 2019
Sustainable activated carbon from palm waste for aqueous nickel II adsorption
W A Hammad1, Mohamed S Abdel-Latif2, Samah A Hawash3
1Faculty of Engineering, Tanta University, Tanta, Egypt. wafaaahmed_10@f-eng.tanta.edu.eg.
Abstract:
Industrial wastewater discharge remains a major environmental concern, with heavy metal contamination posing significant risks to ecosystems and public health. Among these, Ni (II) is commonly detected in effluents and is particularly hazardous when present in drinking water. Adsorption has gained attention as a simple, cost-effective, and efficient method for removing such contaminants.This study investigates the use of AC derived from palm fronds and treated with H₃PO4 (PFTAC) for the adsorption of Ni(II) ions using batch experiment tests. The material demonstrated outstanding performance, achieving a 99.65% removal efficiency within 90 min at an initial concentration of 50 ppm under neutral pH conditions. Nitrogen sorption analysis revealed the mesoporous structure of PFTAC, supported by characteristic adsorption-desorption isotherms. Surface area and pore characteristics were evaluated using BET, t-plot, and BJH methods. Comprehensive material characterization was conducted using SEM (surface morphology), FT-IR (functional groups), and XRD (crystalline structure). These analyses confirmed the suitability of PFTAC as a high-performance, sustainable adsorbent for Ni (II) removal from industrial wastewater. Adsorption onto PFTAC followed pseudo-second-order kinetics (R² = 0.9957), indicating chemisorption. Thermodynamic results confirmed a spontaneous, endothermic process. Langmuir isotherm best describes the equilibrium (R² = 0.9998) with a maximum capacity of 166.7 mg/g.Ni(II) removal using PFTAC was optimized using Box-Behnken Design. The model showed high accuracy (R² = 0.991) and significance (p < 0.05). ANOVA and residual analysis confirmed model reliability. Key factors were time, concentration, and temperature.
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