Preparation and characterization of a novel functionalized agricultural waste-based adsorbent for Cu2+ removal: Evaluation of adsorption performance using response surface methodology

2021 
Abstract In this study, it was aimed to the improvement of adsorption capability with a novel modification method based on increasing surface activity of flaxseed waste (FW), an agricultural waste product, and the investigation of its usability as an effective adsorbent for Cu2+ removal. The modification method involves functionalization of FW with iron by adding FeCl3 to medium in presence of N, N-Dimethyl-formamide, poly (N-vinyl-pyrrolidone), and hexamethylenetetramine. The effect of parameters was investigated by conventional univariate analysis. In addition, Response Surface Methodology (RSM) based on multivariate analysis was used to improve the performance of Cu2+ adsorption onto iron-modified flaxseed waste (M − FW). Cu2+ removal efficiency was achieved as 91.46% ± 2.34 (N = 2) at an equilibrium time of only 15 min under determined optimum conditions as Co: 75 ppm, pH: 4.7, and m: 0.23 g. RSM was successfully applied for the prediction of adsorption. Adsorption nature was as a single-layer adsorption with a maximum adsorption capacity (Qmax) of 7.64 mg/g. The adsorption mechanism, determined to be chemically controlled, an exothermic and non-spontaneous process. Furthermore, pH-dependent adsorption showed that electrostatic interactions between M − FW and Cu2+ ions play an important role in adsorption mechanism. The results of characterization studies showed that a large surface area was provided with increased porosity of structure and desired changes occurred in target functional structures with modification. Moreover, modification and reusability of M − FW were evaluated in terms of overall sustainability and waste management. The results indicated that M − FW has potential for usability to remove heavy metals like Cu2+ in environmental remediation applications.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    70
    References
    1
    Citations
    NaN
    KQI
    []