Highly efficient ozone elimination by metal doped ultra-fine Cu_(2)O nanoparticles

Nowadays,ozone contamination becomes dominant in air and thus challenges the research and development of cost-effective catalyst.In this study,metal doped Cu_(2)O catalysts are synthesized via reduction of Cu^(2+)by ascorbic acid in base solutions containing doping metal ions.The results show that compared with pure Cu_(2)O,the Mg^(2+)and Fe^(2+)dopants enhance the O_(3)removal efficiency while Ni2+depresses the activity.In specific,Mg-Cu_(2)O shows high O3removal efficiency of 88.4%in harsh environment of 600,000 mL/(g·hr) space velocity and 1500 ppmV O_(3),which is one of the highest in the literature.Photoluminescence and electron paramagnetic spectroscopy characterization shows higher concentration of crystal defects induced by the Mg^(2+)dopants,favoring the O3degradation.The in-situ diffuse reflectance Fourier transform infrared spectroscopy shows the intermediate species in the O_(3)degradation process change from O_(2)^(2-)dominant of pure Cu_(2)O to O_(2)^(-)dominant of Mg-Cu2O,which would contribute to the high activity.All these results show the promising prospect of the Mg-Cu_(2)O for highly efficiency O_(3)removal.

Low-temperature conversion of ammonia to nitrogen in water with ozone over composite metal oxide catalyst

As one of the most important water pollutants, ammonia nitrogen emissions have increased year by year, which has attracted people＇s attention. Catalytic ozonation technology, which involves production of ·OH radical with strong oxidation ability, is widely used in the treatment of organic-containing wastewater. In this work, MgO-Co3O4 composite metal oxide catalysts prepared with different fabrication conditions have been systematically evaluated and compared in the catalytic ozonation of ammonia（50 mg/L） in water. In terms of high catalytic activity in ammonia decomposition and high selectivity for gaseous nitrogen, the catalyst with MgO-Co3O4 molar ratio 8：2, calcined at 500°C for 3 hr, was the best one among the catalysts we tested, with an ammonia nitrogen removal rate of 85.2% and gaseous nitrogen selectivity of44.8%. In addition, the reaction mechanism of ozonation oxidative decomposition of ammonia nitrogen in water with the metal oxide catalysts was discussed. Moreover, the effect of coexisting anions on the degradation of ammonia was studied, finding that SO2-4 and HCO-3 could inhibit the catalytic activity while CO2-3 and Br-could promote it. The presence of coexisting cations had very little effect on the catalytic ozonation of ammonia nitrogen. After five successive reuses, the catalyst remained stable in the catalytic ozonation of ammonia.

A tailored and rapid approach for ozonation catalyst design

Catalytic ozonation is widely employed in advanced wastewater treatment owing to its high mineralization of refractory organics.The key to high mineralization is the compatibility between catalyst formulation and wastewater quality.Machine learning can greatly improve experimental efficiency,while fluorescence data can provide additional wastewater quality information on the composition and concentration of organics,which is conducive to optimizing catalyst formulation.In this study,machine learning combined with fluorescence spectroscopy was applied to develop ozonation catalysts(Mn/g-Al_(2)O_(3)catalyst was used as an example).Based on the data collected from 52 different catalysts,a machine-learning model was established to predict catalyst performance.The correlation coefficient between the experimental and model-predicted values was 0.9659,demonstrating the robustness and good generalization ability of the model.The range of the catalyst formulations was preliminarily screened by fluorescence spectroscopy.When the wastewater was dominated by tryptophan-like and soluble microbial products,the impregnation concentration and time of Mn(NO_(3))_(2) were less than 0.3 mol L^(-1)and 10 h,respectively.Furthermore,the optimized Mn/g-Al_(2)O_(3)formulation obtained by the model was impregnation with 0.155 mol L^(-1)Mn(NO_(3))_(2)solution for 8.5 h and calcination at 600℃ for 3.5 h.The model-predicted and experimental values for total organic carbon removal were 54.48% and 53.96%,respectively.Finally,the improved catalytic performance was attributed to the synergistic effect of oxidation(·OH and ^(1)O_(2))and the Mn/g-Al_(2)O_(3) catalyst.This study provides a rapid approach to catalyst design based on the characteristics of wastewater quality using machine learning combined with fluorescence spectroscopy.

Comparison of different disinfection processes in the effective removal of antibiotic-resistant bacteria and genes

This study compared three different disinfection processes （chlorination, E-beam, and ozone） and the efficacy of three oxidants （H202, S2O8-, and peroxymonosulfate （MPS）） in removing antibiotic resistant bacteria （ARB） and antibiotic resistance genes （ARGs） in a synthetic wastewater. More than 30 mg/L of chlorine was needed to remove over 90% of ARB and ARG. For the E-beam method, only 1 dose （kGy） was needed to remove ARB and ARG, and ozone could reduce ARB and ARG by more than 90% even at 3 mg/L ozone concentration. In the ozone process, CT values （concentration × time） were compared for ozone alone and combined with different catalysts based on the 2-log removal of ARB and ARG. Ozone treatment yielded a value of 31 and 33 （mg·min）/L for ARB and ARGs respectively. On the other hand, ozone with persulfate yielded 15.9 and 18.5 （mg.min）/L while ozone with monopersulfate yielded a value of 12 and 14.5 （mg·min）/L. This implies that the addition of these catalysts significantly reduces the contact time to achieve a 2-log removal, thus enhancing the process in terms of its kinetics.