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Phosphate buffer solution has been commonly used in MFC studies to maintain a suitable pH for electricity-generating bacteria and/or to increase the solution conductivity. However, addition of a high concentration of phosphate buffer in MFCs could be expensive, especially for wastewater treatment. In this study, the performances of MFCs with cloth electrode assemblies (CEA) were evaluated using bicarbonate buffer solutions. A maximum power density of 1550 W/m3 (2770 mW/ m2) was obtained at a current density of 0.99 Ma/cm2 using a pH 9 bicarbonate buffer solution. Such a power density was 38.6% higher than that using a pH 7 phosphate buffer at the same concentration of 0.2 M. Based on the quantitative comparison of free proton transfer rates, diffusion rates of pH buffer species, and the current generated, a facilitated proton transfer mechanism was proposed for MFCs in the presence of the pH buffers. The excellent performance of MFCs using bicarbonate as pH buffer and proton carrier indicates that bicarbonate buffer could be served as a low-cost and effective pH buffer for practical applications, especially for wastewater treatment. [ABSTRACT FROM AUTHOR]

Sonochemical degradation which combines destruction of the target compounds by free radical reaction and thermal cleavage is one of the recent advanced oxidation processes (AOP) and proven to be effective for removing low concentration organic pollutants from aqueous streams. This work describes the degradation of several organic compounds of varying volatility in aqueous solution in two types of ultrasonic reactors. The process variables studied include initial concentration of the organics, temperature, and type of saturated gas. The effects of additional oxidant and electrolyte were also examined. A kinetic model was tested to determine its ability to predict the degradation rate constant of different volatile organic compounds at different initial conditions. A figure of merit for the electrical energy consumption for the two types of ultrasonic reactors is also presented. [Copyright &y& Elsevier]

The authors respond to comments on their paper "Sustainable Power Generation in Microbial Fuel Cells Using Bicarbonate Buffer and Proton Transfer Mechanisms," by Y. H. Ahn. They state that Ahn overestimated the possible bicarbonate concentration in the experiments. They comment that equilibrium is established between dissolved carbon dioxide in the liquid phase and carbon dioxide gas in the headspace while under steady state conditions, and that the carbon dioxide levels remain constant due to effluent removal of produced carbon dioxide.

The influencing factors of coal and gas outburst are complex, and now the accuracy and efficiency of outburst prediction are not high. In order to obtain the effective features from influencing factors and realize the accurate and fast dynamic prediction of coal and gas outburst, this article proposes an outburst prediction model based on the coupling of feature selection and intelligent optimization classifier. Firstly, in view of the redundancy and irrelevance of the influencing factors of coal and gas outburst, we use Boruta feature selection method to obtain the optimal feature subset from influencing factors of coal and gas outburst. Secondly, based on Apriori association rules mining method, the internal association relationship between coal and gas outburst influencing factors is mined, and the strong association rules existing in the influencing factors and samples that affect the classification of coal and gas outburst are extracted. Finally, svm is used to classify coal and gas outburst based on the above obtained optimal feature subset and sample data, and Bayesian optimization algorithm is used to optimize the kernel parameters of svm, and the coal and gas outburst pattern recognition prediction model is established, which is compared with the existing coal and gas outburst prediction model in literatures. Compared with the method of feature selection and association rules mining alone, the proposed model achieves the highest prediction accuracy of 93% when the feature dimension is 3, which is higher than that of Apriori association rules and Boruta feature selection, and the classification accuracy is significantly improved. However, the feature dimension decreased significantly, the results show that the proposed model is better than other prediction models, which further verifies the accuracy and applicability of the coupling prediction model. [ABSTRACT FROM AUTHOR]