Utilizing an alternative composite material for effective copper(II) ion capturing from wastewater

The novel ligand based functionalized composite materials (CpMA) was fabricated using a highly porous silica and deployed as an effective materials for the effective monitoring and adsorption of copper (Cu(II)) ions from contaminated water. The application of CpMA was significantly intensified the monitoring and adsorption of Cu(II) ion at optimum experimental protocol. The organic ligand onto the mesoporous silica was the key factor for an efficient monitoring and adsorption of Cu(II) ion with optimum color formation. The effects of diverse experimental parameters such as solution pH, contact time, initial concentration, selectivity and sensitivity were measured systematically. The solution pH was played the key role for monitoring and adsorption and the present CpMA was worked well in acidic pH region at 3.50. The data clarified that the CpMA was able to detected with significant color formation even in the presence of ultra-trace Cu(II) ions, which was unique feature of the CpMA. The CpMA was offered simple, one-step monitoring procedure without the need of highly sophisticated apparatus. The low limit of detection was 0.36 mg/L based on the calibration curve. The CpMA was exhibited significant ion-selectivity toward the Cu(II) ion in the multi-mixture solution as environmental samples. The data revealed that the CpMA was selectively captured Cu(II) ions from binary and multi mixtures even in the presence of various competing ions. The adsorption isotherm was well described and the maximum adsorption capacity was as high as 189.35 mg/g. The elution of Cu(II) ions from the saturated CpMA was desorbed successfully with 0.30 M HCl. The regenerated material that remained maintained the high selectivity to Cu(II) ions and exhibited almost the same functionality as that of the original adsorbent. Therefore, the proposed CpMA offered a cost-effective and considered an alternative for effectively toxic Cu(II) ion capturing in real sample treatment. (C) 2021 Elsevier B.V. All rights reserved.