Genetic algorithm-assisted artificial neural network modelling for remediation and recovery of Pb (II) and Cr(VI) by manganese and cobalt spinel ferrite super nanoadsorbent

MnFe₂O₄ and CoFe₂O₄ nanoparticles were hydrothermally synthesized to examine their capability in adsorption of Pb (II) and Cr (VI). The adsorbents exhibited a high rate of adsorption, reaching 90% of their adsorption capacity in less than 30 min. Furthermore, the adsorption capability of the Magnetic Nanoparticles (MNPs) was noticeably greater at initial pollutant concentrations smaller than 40 mg/L. Maximum adsorption capacity on MnFe₂O₄ and CoFe₂O₄ nanoparticles were 40 and 25.38 mg/g for Cr (VI) and 523.32 and 476.19 mg/g for Pb (II), respectively. A data-driven model of Artificial Neural Network was used for prediction of adsorption capacity at both equilibrium and non-equilibrium condition. The model parameters including the numbers of neuron (n = 7) and data portioning for training (49.5%), validation (40.5%), and testing (10%) were obtained using Genetic Algorithm. The results indicated that the model could predict the data with high accuracy (R² = 0.998). The input parameters were initial concentration, time, pH, temperature, adsorbent dosage, and other parameters that is dependent to the physico-chemical properties of ions and adsorbents' surface (ε, α₁, α₂). The mechanism involved in Cr(VI) and Pb(II) adsorption are electrostatic physisorption and a combination of ion exchange chemisorption and electrostatic physisorption, respectively. Desorption capability and adsorbent reuse capability were also examined.

Development and characterization of electrochemical sensors based on carbon modified with TiO2 nanoparticles

The aim of this study is the development and characterization of a carbon-based electrochemical sensor, modified with TiO2 nanoparticles for potential application in electroanalytical techniques. The influence of binder and modifier contents on morphological, physicochemical and electrochemical characteristics of the electrode material was investigated in order to determine the optimal ratio of the carbon material/binder/modifier. Carbon pastes were prepared from mixtures containing graphite powder, TiO2 nanoparticles and liquid hydrocarbons. Scanning electron microscopy showed that the electrode material becomes more compact with the addition and the increase in the binder material content, while increasing the proportion of TiO2 nanoparticles did not have any significant effect on the material morphology showing fairly homogeneous nanoparticle distribution in the graphite electrode material. The test results indicate that the modified carbon paste with 40 vol.% paraffin oil (PO) and 6-8 wt.% TiO2 nanoparticles is characterized by the lowest value of specific resistance. By applying cyclic voltammetry, the most pronounced degree of reversibility was obtained in relation to the standard reversible redox system ([Fe (CN)]-3/-4) for the electrode material with 30-40 vol.% PO and 8-10 wt.% TiO2 nanoparticles. Characterization of the electrode material based on carbon modified with TiO2 nanoparticles indicated that the optimal composition contains 40 vol.% PO and 6-8 wt.% TiO2 nanoparticles, which is important for application in electroanalytical techniques.