The effect of Zr on the catalytic performance of Pd/y-A1203 for the methane combustion was investigated. The results show that the addition of Zr can improve the activity and stability of Pd/γ-Al2O3 catalyst, which, based on the catalyst characterization （N2 adsorption, XRD, CO- Chemisorption, XPS, CHa-TPR and O2-TPO）, is ascribed to the interaction between Pd and Zr. The active phase of methane combustion over supported palladium catalyst is the Pd^0/Pd^2＋ mixture. Zr addition inhibits Pd aggregation and enhances the redox properties of active phase Pd^0/ Pd^2＋. H2 reduction could effectively reduce the oxidation degree of Pd species and regenerate the active sites （Pd^0/ pd^2＋）. 相似文献
The novel microwave catalyst MgFe2O4-SiC was synthesized via sol-gel method, to remove azo dye Direct Black BN (DB BN) through adsorption and microwave-induced catalytic reaction. Microwave-induced catalytic degradation of DB BN, including adsorption behavior and its influencing factors of DB BN on MgFe2O4-SiC were investigated. According to the obtained results, it indicated that the pseudo-second-order kinetics model was suitable for the adsorption of DB BN onto MgFe2O4-SiC. Besides, the consequence of adsorption isotherm depicted that the adsorption of DB BN was in accordance with the Langmuir isotherm, which verified that the singer layer adsorption of MgFe2O4-SiC was dominant than the multi-layer one. The excellent adsorption capacities of MgFe2O4-SiC were kept in the range of initial pH from 3 to 7. In addition, it could be concluded that the degradation rate of DB BN decreased over ten percent after the adsorption equilibrium had been attained, and the results from the result of comparative experiments manifested that the adsorption process was not conducive to the process of microwave-induced catalytic degradation. The degradation intermediates and products of DB BN were identified and determined by GC-MS and LC-MS. Furthermore, combined with the catalytic mechanism of MgFe2O4-SiC, the proposed degradation pathways of DB BN were the involution of microwave-induced $OH and holes in this catalytic system the breakage of azo bond, hydroxyl substitution, hydroxyl addition, nitration reaction, deamination reaction, desorbate reaction, dehydroxy group and ring-opening reaction.
The phytotoxicity of added copper (Cu) and nickel (Ni) is influenced by soil properties and field aging. However, the differences in the chemical behavior between Cu and Ni are still unclear. Therefore, this study was conducted to investigate the extractability of added Cu and Ni in 6-year field experiments, as well as the link with their phytotoxicity. The results showed that the extractability of added Cu decreased by 6.63% (5.10%–7.90%), 22.5% (20.6%–23.9%), and 6.87% (0%–17.9%) on average for acidic, neutral, and alkaline soil from 1 to 6 years, although the phytotoxicity of added Cu and Ni did not change significantly from 1 to 6 years in the long term field experiment. Because of dissolution of Cu, when the pH decreased below 7.0, the extractability of Cu in alkaline soil by EDTA at pH 4.0 could not reflect the effects of aging. For Ni, the extractability decreased by 18.1% (10.1%–33.0%), 63.0% (59.2%–68.8%), and 22.0% (12.4%–31.8%) from 1 to 6 years in acidic, neutral, and alkaline soils, respectively, indicating the effects of aging on Ni were greater than on Cu. The sum of ten sequential extractions of Cu and Ni showed that added Cu was more extractable than Ni in neutral and alkaline soil, but similar in acidic soil.
• UV-vis absorption analyzer was applied in drainage type online recognition.• The UV-vis spectrum of four drainage types were collected and evaluated.• A convolutional neural network with multiple derivative inputs was established.• Effects of different network structures and input contents were compared. Optimizing sewage collection is important for water pollution control and wastewater treatment plants quality and efficiency improvement. Currently, the urban drainage pipeline network is upgrading to improve its classification and collection ability. However, there is a lack of efficient online monitoring and identification technology. UV-visible absorption spectrum probe is considered as a potential monitoring method due to its small size, reagent-free and fast detection. Because the performance parameters of probe like optic resolution, dynamic interval and signal-to-noise ratio are weak and high turbidity of sewage raises the noise level, it is necessary to extract shape features from the turbidity disturbed drainage spectrum for classification purposes. In this study, drainage network samples were online collected and tested, and four types were labeled according to sample sites and environment situation. Derivative spectrum were adopted to amplify the shape features, while convolutional neural network algorithm was established to conduct nonlinear spectrum classification. Influence of input and network structure on classification accuracy was compared. Original spectrum, first-order derivative spectrum and a combination of both were set to be three different inputs. Artificial neural network with or without convolutional layer were set be two different network structures. The results revealed a convolutional neural network combined with inputs of first and zero-order derivatives was proposed to have the best classification effect on domestic sewage, mixed rainwater, rainwater and industrial sewage. The recognition rate of industrial wastewater was 100%, and the recognition rate of domestic sewage and rainwater mixing system were over 90%. 相似文献
