α-MnO_2 nanotubes and their supported Au-Pd alloy nanocatalysts were prepared using hydrothermal and polyvinyl alcohol-protected reduction methods, respectively. Their catalytic activity for the oxidation of toluene/m-xylene, acetone/ethyl acetate, acetone/m-xylene and ethyl acetate/m-xylene mixtures was evaluated. It was found that the interaction between Au-Pd alloy nanoparticles and α-MnO_2 nanotubes significantly improved the reactivity of lattice oxygen, and the 0.91 wt.% Au0.48 Pd/α-MnO_2 nanotube catalyst outperformed the α-MnO_2 nanotube catalyst in the oxidation of toluene, m-xylene, ethyl acetate and acetone. Over the0.91 wt.% Au0.48 Pd/α-MnO_2 nanotube catalyst,(i) toluene oxidation was greatly inhibited in the toluene/m-xylene mixture, while m-xylene oxidation was not influenced;(ii) acetone and ethyl acetate oxidation suffered a minor impact in the acetone/ethyl acetate mixture; and(iii) m-xylene oxidation was enhanced whereas the oxidation of the oxygenated VOCs(volatile organic compounds) was suppressed in the acetone/m-xylene or ethyl acetate/m-xylene mixtures. The competitive adsorption of these typical VOCs on the catalyst surface induced an inhibitive effect on their oxidation, and increasing the temperature favored the oxidation of the VOCs. The mixed VOCs could be completely oxidized into CO_2 and H_2 O below 320°C at a space velocity of 40,000 m L/(g·hr). The 0.91 wt.% Au0.48 Pd/α-MnO_2 nanotube catalyst exhibited high catalytic stability as well as good tolerance to water vapor and CO_2 in the oxidation of the VOC mixtures. Thus, the α-MnO_2 nanotube-supported noble metal alloy catalysts hold promise for the efficient elimination of VOC mixtures. 相似文献
Environmental Science and Pollution Research - Coal mine pollution is a serious threat to the mine safe production and occupational health of miners. Chemical dust suppression can effectively... 相似文献
The ecological footprint value (abbreviated as EF) is the quantitative indicator on evaluating the sustainable development status of a region. How to simulate the EF’s trend with a long-time data series has been heatedly discussed. The economic development of Suzhou, one of the most developed cities in Yangtze Delta, China, has been accelerated in the past 20 years, and it is necessary to evaluate the influence of the socioeconomic growth on local natural resources. The EF values of Suzhou from 1999 to 2018 were calculated and simulated using both the ARIMA model and the GM(1,1) model. The ARIMA model has been used in the prediction of EF values in several cases. However, the EF data series of the city consisted of white noise and could not be fitted by the ARIMA model. The GM(1,1) model, an approach forecasting nonlinear data series, was not found in the studies of the EF simulation. Through the model precision test, the GM(1,1) model introduced fit the EF data series well and was considered to be appropriate to simulate the EF values for Suzhou. The fitting performance was accurate, and the EF values of the city could be forecasted by the model in short term. With the proposed model, the ecological sustainability status of the city was analyzed.