ABSTRACT
The detailed atomic-level mechanism of the effect induced by engineering the crystal facet of α-MnO2 catalysts on N2O formation during ammonia-selective catalytic reduction (NH3-SCR) was ascertained by combining density functional theory (DFT) calculations and thermodynamics/kinetic analysis. The surface energies of α-MnO2 with specific (100), (110), and (310) exposed planes were calculated, and the adsorptions of NH3, NO, and O2 on three surfaces were analyzed. The adsorption energies showed that NH3 and NO molecules could be strongly adsorbed on the surface of the α-MnO2 catalyst, while the adsorption of O2 was weak. Moreover, the key steps in the oxidative dehydrogenation of NH3 and the formation of NH2NO as well as dissociation of NH2 were studied to evaluate the catalytic ability of NH3-SCR reaction and N2 selectivity. The results revealed that the α-MnO2 catalyst exposed with the (310) plane exhibited the best NH3-SCR catalytic performance and highest N2 selectivity, mainly due to its low energy barriers in NH3 dehydrogenation and NH2NO generation, and difficulty in NH2 dissociation. This study deepens the comprehension of the facet-engineering of α-MnO2 on inhibiting N2O formation during the NH3-SCR, and points out a strategy to improve their catalytic ability and N2 selectivity for the low-temperature NH3-SCR process.
ABSTRACT
Manganese oxide (especially manganese dioxide [MnO2]) is an excellent catalytic material for SO2 removal in flue gas desulfurization. In this study, the effect of crystalline structure of MnO2 (α-MnO2, ß-MnO2, γ-MnO2 and δ-MnO2) on their activity for SO2 oxidation was studied based on density functional theory with Hubbard U corrections (DFT + U). The calculated results showed that α-MnO2 has mild energy barriers of 0.69 eV and 0.46 eV, and ß-MnO2 has poor redox performance on SO2 molecules, which has the highest energy barrier of 2.17 eV and the largest oxygen formation energy of 1.74 eV, making it difficult for the oxygen atom to remove from the surface lattice to form reactive sites. Thermodynamic calculations showed that α-MnO2 is suitable for SO2 oxidation for its low energy barriers, reaction energy close to zero in the first half, and relatively high spontaneity in the whole reaction. Experimental tests showed that α-MnO2 had the best catalytic oxidation effect, with the highest sulfur capacity (304.11 mg/g), but ß-MnO2 had poor catalytic oxidation performance, with a sulfur capacity of 41.59 mg/g. This work studies the catalytic performance and mechanism of SO2 removal and proposes a strategy to improve the catalytic activity by phase structure.
ABSTRACT
Complex marine environment has an adverse effect on the object detection algorithm based on the vision sensor for the smart ship sailing at sea. In order to eliminate the motion blur in the images during the navigation of the smart ship and ensure safety, we propose SharpGAN, a new image deblurring method based on the generative adversarial network (GAN). First of all, we introduce the receptive field block net (RFBNet) to the deblurring network to enhance the network's ability to extract blurred image features. Secondly, we propose a feature loss that combines different levels of image features to guide the network to perform higher-quality deblurring and improve the feature similarity between the restored images and the sharp images. Besides, we use the lightweight RFB-s module to significantly improve the real-time performance of the deblurring network. Compared with the existing deblurring methods, the proposed method not only has better deblurring performance in subjective visual effects and objective evaluation criteria, but also has higher deblurring efficiency. Finally, the experimental results reveal that the SharpGAN has a high correlation with the deblurring methods based on the physical model.
