Analysis of the steady-state behavior of pseudo-first-order EC-catalytic mechanism at a rotating disk electrode

Separation of dye from aqueous solution by a new gravity compression and aeration system

Filtration is one of the important technologies for separating suspended particles. Under the condition of gravity compression, the filtration density can be increased and the separation effect of suspended particles can be improved. Considering the complex composition and the difficulty in degrading dye in industrial wastewater, a gravity compression aeration system with a modified polyester fibre ball (denoted as MPFB) was evaluated for the separation of dye from water. Congo red azo dye solution (0-40 mg/L) was selected as the model treatment compound. The MPFB was prepared by adjusting the concentrations of alkali (Quality score 0-25%), β-cyclodextrin (0∼80 g/L), reaction temperature (40-90°C), and silane coupler concentration (Concentration fractions 0-0.8%). We used Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS) to characterise the MPFB. The separation was affected significantly by adsorption conditions such as MPFB dose and pH. The lower the MPFB dose, the higher the expected adsorption capacity. For the treatment of a dye solution at 500 mg/L, 100% removal was achieved with 48 g/L MPFB, at pH 8 during adsorption under non-circulation aeration. For 24 h of reaction, the system could reach the maximum adsorption capacity of 11.2 mg/g, which followed the pseudo-first order kinetics model and the intraparticle diffusion model. We discovered that circulation aeration provided the best adsorption and electrostatic and hydrogen bonding were the dominant components of adsorption. Overall, the system is a promising technology and has the potential to treat large volumes of dye wastewater.

Solution of a Complex Nonlinear Fractional Biochemical Reaction Model

This paper discusses a complex nonlinear fractional model of enzyme inhibitor reaction where reaction memory is taken into account. Analytical expressions of the concentrations of enzyme, substrate, inhibitor, product, and other complex intermediate species are derived using Laplace decomposition and differential transformation methods. Since different rate constants, large initial concentrations, and large time domains are unavoidable in biochemical reactions, different dynamics will result; hence, the convergence of the approximate concentrations may be lost. In this case, the proposed analytical methods will be coupled with Padé approximation. The validity and accuracy of the derived analytical solutions will be established by direct comparison with numerical simulations.

Mathematical Analysis of Reaction-Diffusion Equations Modeling the Michaelis-Menten Kinetics in a Micro-Disk Biosensor

In this study, we have investigated the mathematical model of an immobilized enzyme system that follows the Michaelis-Menten (MM) kinetics for a micro-disk biosensor. The film reaction model under steady state conditions is transformed into a couple differential equations which are based on dimensionless concentration of hydrogen peroxide with enzyme reaction (H) and substrate (S) within the biosensor. The model is based on a reaction-diffusion equation which contains highly non-linear terms related to MM kinetics of the enzymatic reaction. Further, to calculate the effect of variations in parameters on the dimensionless concentration of substrate and hydrogen peroxide, we have strengthened the computational ability of neural network (NN) architecture by using a backpropagated Levenberg-Marquardt training (LMT) algorithm. NNs-LMT algorithm is a supervised machine learning for which the initial data set is generated by using MATLAB built in function known as "pdex4". Furthermore, the data set is validated by the processing of the NNs-LMT algorithm to find the approximate solutions for different scenarios and cases of mathematical model of micro-disk biosensors. Absolute errors, curve fitting, error histograms, regression and complexity analysis further validate the accuracy and robustness of the technique.