Revealing the crystal facet effect on NO formation during the NH-SCR over α-MnO catalysts.

The detailed atomic-level mechanism of the effect induced by engineering the crystal facet of α-MnO catalysts on NO formation during ammonia-selective catalytic reduction (NH-SCR) was ascertained by combining density functional theory (DFT) calculations and thermodynamics/kinetic analysis. The surface energies of α-MnO with specific (100), (110), and (310) exposed planes were calculated, and the adsorptions of NH, NO, and O on three surfaces were analyzed. The adsorption energies showed that NH and NO molecules could be strongly adsorbed on the surface of the α-MnO catalyst, while the adsorption of O was weak.

Mechanistic and thermodynamic insights into the SO oxidation on MnO catalysts: A combined theoretical and experimental study.

Manganese oxide (especially manganese dioxide [MnO]) is an excellent catalytic material for SO removal in flue gas desulfurization. In this study, the effect of crystalline structure of MnO (α-MnO, β-MnO, γ-MnO and δ-MnO) on their activity for SO oxidation was studied based on density functional theory with Hubbard U corrections (DFT + U). The calculated results showed that α-MnO has mild energy barriers of 0.

SharpGAN: Dynamic Scene Deblurring Method for Smart Ship Based on Receptive Field Block and Generative Adversarial Networks.

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.