C₂₊ hydrocarbons, especially C₂₊ olefins, as important basic chemical raw materials, mainly come from petroleum cracking. With the increasing scarcity of petroleum resources, the search for new olefins production routes has become the focus of research, and the production of olefins by the oxidative coupling of methane (OCM) process has attracted extensive attention. The OCM route is an important alternative to the production of olefins from petroleum resources and is also an important direction for the development of efficient and clean utilization of natural gas. In this paper, the mechanism, catalysts, and other key factors for the production of olefins by methane oxidative coupling are reviewed. The mechanism of OCM, including the reaction pathway and the formation of intermediate products, is introduced. Then, commonly used catalysts, such as alkali metal/alkaline earth metal oxides, rare earth metal oxides, composite metal oxides with special structures, and classical catalysts Mn/Na₂WO₄/SiO₂, and their mechanisms of action in the reaction are discussed. In addition, the application of chemical looping oxidative coupling of methane (CLOCM) in olefin production is also investigated, which is a promising alternative way due to the high selectivity of olefins and the low cost of catalysts owing to the excellent performance of the catalyst recycling. These studies will help to further understand the mechanism of OCM for olefin production and provide guidance and support for applications in related fields.

A plasma–catalyst hybrid system has been developed for the direct conversion of methane to C₂₊ hydrocarbons in dielectric barrier discharge (DBD) plasma. TiO₂ presented the highest C₂₊ yield of 11.63% among different dielectric materials when integrated with DBD plasma, which made us concentrate on the TiO₂-based catalyst. It was demonstrated that MnTi catalyst showed the best methane coupling performance of 27.29% C₂₊ yield with 150 V applied voltage, without additional thermal input. The catalytic performance of MnTi catalyst under various operation parameters was further carried out, and different techniques, such as X-ray diffraction, X-ray photoelectron spectroscopy, transmission electron microscopy, and H₂-temperature-programmed reduction were used to explore the effect of Mn loading on methane oxidative coupling (OCM) performance. The results showed that applied voltage and flow rate had a significant effect on methane activation. The dielectric particles of TiO₂ loaded with Mn not only synergistically affected the coupling reaction, but also facilitated charge deposition to generate a strong local electric field to activate methane. The synergy effects boosted the OCM performance and the C₂₊ yield became 1.25 times higher than that of the undoped TiO₂ under identical operating conditions in plasma, which was almost impossible to occur even at 850 °C on the MnTi catalyst in the absence of plasma. Moreover, the reaction activity of the catalyst was fully recovered by plasma regeneration at 300 °C and maintained its stability in for at least 30 consecutive cyclic redox tests. This work presents a new opportunity for efficient methane conversion to produce C₂₊ at low temperatures by plasma assistance.

Background: The incidence rate of renal disease is high, which can cause end-stage renal disease. Ultrasound is a commonly used imaging method, including conventional ultrasound, color ultrasound, elastography, etc. Machine learning is a potential method which has been widely used in clinical practices. Objective: To compare the diagnostic performance of different ultrasonic image measurement parameters for kidney diseases, and to compare different machine learning methods with the human- reading method. Methods: Ninety-four patients with pathologically diagnosed renal diseases and 109 normal controls were included in this study. The patients were examined by conventional ultrasound, color ultrasound and shear wave elasticity, respectively. Ultrasonic data were analyzed by Support vector machine (SVM), random forest (RF), K-nearest neighbor (KNN) and artificial neural network (ANN), respectively, and compared with the human-reading method. Results: Only ultrasound elastography data have a diagnostic value for renal diseases. The accuracy of SVM, RF, KNN and ANN methods is 80.98%, 80.32%, 78.03% and 79.67%, respectively, while the accuracy of human-reading is 78.33%. In the data of machine learning ultrasound elastography, the elastic hardness parameters of the renal cortex are most important. Conclusion: Ultrasound elastography is of the highest diagnostic value in machine learning for nephropathy, the diagnostic efficiency of the machine learning method is slightly higher than that of the human-reading method, and the diagnostic ability of the SVM method is higher than other methods.