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Reducing NOx emission in the exhaust gas under the extremely low temperature of engine cold start was important for the ecological environment and human health.MnO2 has high catalytic activity for NOx in the low temperature range, which is a promising nanocatalyst.The intrinsic relationship between the surface structural characteristics and denitrification activity of MnO2-Fe2O3-CeO2/Al2O3 nanocatalysts produced by ultrasonic assisted coprecipitation method with different manganese contents was discussed. The results show that when the manganese content is 15%(mass fraction), the nanocatalyst has the largest specific surface area and the smallest grain size. At the same time, the distribution of active substances is the most uniform. Meanwhile, the crystallinity of metal oxides is suppressed, and Mn, Fe and Ce are in the best coexistence state, which can promote the enrichment of chemisorption oxygen. The catalytic oxidation of NO to NO2 improves the reaction rate of fast SCR, and the NO2 conversion reaches 88.5% at 50 ℃. Optimizing manganese content is an extremely effective method to improve the structure-activity relationship of catalytic materials and promote the performance of NO preoxidation and fast SCR reaction.

Different with terrestrial plants, algae contains alginic acid and alginate, and the alginate mainly exists in cell walls in the form of calcium alginate and magnesium alginate. Six kinds of algae, sargassum, enteromorpha, asparagus, undaria, kelp and ulva, were used as raw materials, carbon products from algae carbonization were pickled with hydrochloric acid to remove Ca2+ and Mg2+ ions,etc. from alginate and form "egg-box" initial pore structure. Then the algae-based super activated carbon was prepared by KOH activation method, the effect of pickling pretreatment on the pore structure and electrochemical properties of algae-based activated carbon was studied. The results show that the hydrochloric acid pretreatment can not only increase the specific surface area, but also improve the number of mesoporous of the algae-based activated carbons. The electrochemical performance of algae-based activated carbons is improved after hydrochloric acid pretreatment.

Gulfweed was used as raw material, and KOH activation method was adopted for producing activated carbons with high specific surface area.The prepared super activated carbon not only has large specific surface area, rich pore structure, but also reduces the production cost of activated carbon by using algae as precursor material. In order to explore the experimental approaches and technological conditions, single factor method was used to analyze the influence of impregnation ratio, activation time and activation temperature on specific surface area, pore structure and surface properties, and the electrochemical performance of super-capacitor based on the prepared activated carbon was studied. Measurements such as N2 adsorption, XRD, SEM, constant current charge-discharge techniques, and the cyclic voltammetry were carried out to investigate specific surface area, pore structure and electrochemical performance. The results show:the best preparation condition is concluded as:impregnation ratio at 4:1, activation time for 120 min and activation temperature at 800℃, on the occasion, the largest specific surface area is 2926m2/g, and the biggest pore volume is 1.536cm3/g with all the pore diameters almost under 4nm evenly. The super-capacitor has a large specific capacitance as 358.5F/g in the 6mol/L KOH electrolyte, which means a very good performance.

In the era of big data, with the continuous popularization of social media, various text data are growing in the network and in life. It is of great significance to analyze and manage text data using text classification technology. Text classification is a basic research field in the field of artificial intelligence natural language processing. Under the given criteria, it classifies text according to content. The application scenarios of text classification are very extensive, such as sentiment analysis, topic classification, relationship classification, etc. Deep learning is a method of representation learning based on data in machine learning, and it shows good classification effect in text data processing. Chinese text and English text have differences in form, sound, and image. Focusing on the uniqueness of Chinese text classification, this paper analyzes and expounds the deep learning methods used for Chinese text classification, and finally sorts out commonly used datasets for Chinese text classification. [ABSTRACT FROM AUTHOR]