Preparation of NH2-Functionalized Fe2O3 and Its Chitosan Composites for the Removal of Heavy Metal Ions

Removal of Pb2+, CrT, and Hg2+ Ions from Aqueous Solutions Using Amino-Functionalized Magnetic Nanoparticles

In this paper, a circular economy approach with the adsorption and desorption of heavy metal (HM) ions—i.e., lead (Pb2+), chromium (CrT), and mercury (Hg2+)—from aqueous solutions was studied. Specific and selective binding of HM ions was performed on stabilized and amino-functionalized iron oxide magnetic nanoparticles (γ-Fe2O3@NH2 NPs) from an aqueous solution at pH 4 and 7. For this purpose, γ-Fe2O3@NH2 NPs were characterized by thermogravimetric analysis (TGA), Fourier-transform infrared spectroscopy (FTIR), specific surface area (BET), transmission electron microscopy (TEM), EDXS, and zeta potential measurements (ζ). The effects of different adsorbent amounts (mads = 20/45/90 mg) and the type of anions (NO3−, Cl−, SO42−) on adsorption efficiency were also tested. The desorption was performed with 0.1 M HNO3. The results showed improvement of adsorption efficiency for CrT, Pb2+, and Hg2+ ions at pH 7 by 45 mg of g-Fe2O3@NH2 NPs, and the sequence was as follows: CrT > Hg2+ > Pb2+, with adsorption capacities of 90.4 mg/g, 85.6 mg/g, and 83.6 mg/g, respectively. The desorption results showed the possibility for the reuse of γ-Fe2O3@NH2 NPs with HNO3, as the desorption efficiency was 100% for Hg2+ ions, 96.7% for CrT, and 91.3% for Pb2+.

Predicting Microbial Species in a River Based on Physicochemical Properties by Bio-Inspired Metaheuristic Optimized Machine Learning

The main goal of the analysis of microbial ecology is to understand the relationship between Earth’s microbial community and their functions in the environment. This paper presents a proof-of-concept research to develop a bioclimatic modeling approach that leverages artificial intelligence techniques to identify the microbial species in a river as a function of physicochemical parameters. Feature reduction and selection are both utilized in the data preprocessing owing to the scarce of available data points collected and missing values of physicochemical attributes from a river in Southeast China. A bio-inspired metaheuristic optimized machine learner, which supports the adjustment to the multiple-output prediction form, is used in bioclimatic modeling. The accuracy of prediction and applicability of the model can help microbiologists and ecologists in quantifying the predicted microbial species for further experimental planning with minimal expenditure, which is become one of the most serious issues when facing dramatic changes of environmental conditions caused by global warming. This work demonstrates a neoteric approach for potential use in predicting preliminary microbial structures in the environment.