Selective Oxidation of Diethylamine on CuO/ZSM-5 Catalysts: The Role of Cooperative Catalysis of CuO and Surface Acid Sites

Synergistic Catalytic Removal of NOx and n-Butylamine via Spatially Separated Cooperative Sites

Synergistic control of nitrogen oxides (NOx) and nitrogen-containing volatile organic compounds (NVOCs) from industrial furnaces is necessary. Generally, the elimination of n-butylamine (n-B), a typical pollutant of NVOCs, requires a catalyst with sufficient redox ability. This process induces the production of nitrogen-containing byproducts (NO, NO2, N2O), leading to lower N2 selectivity of NH3 selective catalytic reduction of NOx (NH3-SCR). Here, synergistic catalytic removal of NOx and n-B via spatially separated cooperative sites was originally demonstrated. Specifically, titania nanotubes supported CuOx-CeO2 (CuCe-TiO2 NTs) catalysts with spatially separated cooperative sites were creatively developed, which showed a broader active temperature window from 180 to 340 °C, with over 90% NOx conversion, 85% n-B conversion, and 90% N2 selectivity. A synergistic effect of the Cu and Ce sites was found. The catalytic oxidation of n-B mainly occurred at the Cu sites inside the tube, which ensured the regular occurrence of the NH3-SCR reaction on the outer Ce sites under the matching temperature window. In addition, the n-B oxidation would produce abundant intermediate NH2*, which could act as an extra reductant to promote NH3-SCR. Meanwhile, NH3-SCR could simultaneously remove the possible NOx byproducts of n-B decomposition. This novel strategy of constructing cooperative sites provides a distinct pathway for promoting the synergistic removal of n-B and NOx.

Graphene oxide promotes V-Cu-Ce-ZSM-5 to catalyze SO2 and NO at low temperature: performance and mechanism

A catalyst (V-Cu-Ce-ZSM-5) was explored to simultaneously remove the SO₂ and NO_x from flue gas by use of the ZSM-5 molecular sieve as the carrier, V and Cu as the active components, and Ce as the additive in low temperature of 150 °C. The performance of V-Cu-Ce-ZSM-5 was evaluated for the oxidation of NO and SO₂ before and after the addition of graphene oxide (GO). The results showed that V-Cu-Ce-ZSM-5@GO0.5 had the best performance at a reaction temperature of 150 °C, and the oxidation efficiency of SO₂ and NO was 94.60% and 83.64%, respectively. The multiple structural characterizations (BET, SEM, Raman, XRD, and XPS) revealed that the loading of V and Cu with the additive Ce expanded the specific surface area and pore volume of ZSM-5, provided more adsorption sites for SO₂ and NO, and had good desulfurization and denitration activity. The addition of GO further improved the dispersibility of active components and auxiliaries, increased the number of active sites in the reaction process, and significantly improved catalytic activity.

COVID-19 deep classification network based on convolution and deconvolution local enhancement

Computer Tomography (CT) detection can effectively overcome the problems of traditional detection of Corona Virus Disease 2019 (COVID-19), such as lagging detection results and wrong diagnosis results, which lead to the increase of disease infection rate and prevalence rate. The novel coronavirus pneumonia is a significant difference between the positive and negative patients with asymptomatic infections. To effectively improve the accuracy of doctors' manual judgment of positive and negative COVID-19, this paper proposes a deep classification network model of the novel coronavirus pneumonia based on convolution and deconvolution local enhancement. Through convolution and deconvolution operation, the contrast between the local lesion region and the abdominal cavity of COVID-19 is enhanced. Besides, the middle-level features that can effectively distinguish the image types are obtained. By transforming the novel coronavirus detection problem into the region of interest (ROI) feature classification problem, it can effectively determine whether the feature vector in each feature channel contains the image features of COVID-19. This paper uses an open-source COVID-CT dataset provided by Petuum researchers from the University of California, San Diego, which is collected from 143 novel coronavirus pneumonia patients and the corresponding features are preserved. The complete dataset (including original image and enhanced image) contains 1460 images. Among them, 1022 (70%) and 438 (30%) are used to train and test the performance of the proposed model, respectively. The proposed model verifies the classification precision in different convolution layers and learning rates. Besides, it is compared with most state-of-the-art models. It is found that the proposed algorithm has good classification performance. The corresponding sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and precision are 0.98, 0.96, 0.98, and 0.97, respectively.