Sorption, kinetic, thermodynamics and artificial neural network modelling of phenol and 3-amino-phenol in water on composite iron nano-adsorbent

Abstract

Sorption, kinetic, thermodynamics and artificial neural network modelling of phenol and 3‑amino‑phenol in water on composite iron nano-adsorbent are described. The optimized conditions were 100 g/L conc., 40 min contact time, 11 pH, 5 mg/10 mL nanoparticles amounts, and 298 K temperature. The data followed Langmuir, Freundlich, Temkin and Dubinin-Radushkevich models. The values of ΔG0 (average value = −7.14 kJ mol−1 for phenol and 7.07 kJ mol−1 for amino-phenol), ΔH0 (−4.92 kJ mol−1 for phenol and − 4.00 kJ mol−1 for amino-phenol) and ΔS0 (−7.0 × 10−3 kJ mol−1 K−1 for phenol and 6.89 × 10−3 for amino-phenol) confirmed spontaneous adsorption. The mechanism of sorption was through film diffusion. The maximum percent uptakes of phenol and p‑amino‑phenol and were 85.0 and 80.0%. The method is fast, economic and capable to work at natural water pH. Therefore, the presented method may be used for the removal of phenol and amino-phenol from any water source.