Enhanced photocatalytic inactivation of E. coli by natural pyrite in presence of citrate and EDTA as effective chelating agents: Experimental evaluation and kinetic and ANN models. Issue 1 (February 2019) - Kalantary, Roshanak Rezaei
Graphical abstract: Highlights: Chelating agents enhanced photocatalytic inactivation of E . coli . Citrate was more effective than EDTA for inactivation of E. coli . Bulk hydroxyl radicals are the most active radical species in the inactivation process. H2 O2 and O2 increased photocatalytic inactivation process. ANN is able to model the process. Abstract: The effect of chelating agents (citrate and EDTA) on the visible light photocatalytic activity of natural pyrite for the inactivation of Escherichia coli ATCC 25922 (E. coli) was investigated. The influence of various parameters, such as chelating agent and H2 O2 concentration, light intensity and aeration rate, was evaluated experimentally and through kinetic models. The presence of a chelating agent enhanced the photocatalytic activity of natural pyrite, and citrate was more effective than EDTA, considerably improving bacteria inactivation in natural pH and reducing the drawbacks of the photocatalytic inactivation system. For both citrate-pyrite and EDTA-pyrite, the inactivation rate went through a maximum for a concentration of the chelating agent of 0.5 mM. The increase of the light intensity (10–60 mW cm −2 ), H2 O2 concentration (10–25 mg L -1 ) and aeration rate (2-8 L min -1 ) improved the radical generation, resulting in a high inactivation effect. The low effect of H2 O2 on the catalytic activity of pyrite in the presence of EDTA was likely due to the inhibition of the hydrogen peroxide-to-hydroxyl radical conversion by EDTA. On the other hand, degradation of citrate at high aeration rate occurred, decreasing the catalytic activity of the citrate-pyrite system. Scavenging tests indicated that the bulk hydroxyl radicals are the most reactive radical species in the bacterial inactivation, closely followed by valence band holes, while conduction band electrons play a less important role. In addition, in order to generate predictions and to better understand the process, artificial neural networks were used as models for the considered process: the obtained performance indexes indicated a good correlation between experimental and predicted values.