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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Hyperspectral imaging offers non-invasive precision diagnosis of glomerular diseases, minimizing risks from invasive procedures. • Proposed CVT model has the best accuracy in glomerular diseases classification by integrating spatial and spectral features. • This study used a small-sample dataset for pixel-level classification research addressing the challenges of clinical sample collection. In renal disease research, precise glomerular disease diagnosis is crucial for treatment and prognosis. Currently reliant on invasive biopsies, this method bears risks and pathologist-dependent variability, yielding inconsistent results. There is a pressing need for innovative diagnostic tools that enhance traditional methods, streamline processes, and ensure accurate and consistent disease detection. In this study, we present an innovative Convolutional Neural Networks-Vision Transformer (CVT) model leveraging Transformer technology to refine glomerular disease diagnosis by fusing spectral and spatial data, surpassing traditional diagnostic limitations. Using interval sampling, preprocessing, and wavelength optimization, we also introduced the Gramian Angular Field (GAF) method for a unified representation of spectral and spatial characteristics. We captured hyperspectral images ranging from 385.18 nm to 1009.47 nm and employed various methods to extract sample features. Initial models based solely on spectral features achieved a accuracy of 85.24 %. However, the CVT model significantly outperformed these, achieving an average accuracy of 94 %. This demonstrates the model's superior capability in utilizing sample data and learning joint feature representations. The CVT model not only breaks through the limitations of existing diagnostic techniques but also showcases the vast potential of non-invasive, high-precision diagnostic technology in supporting the classification and prognosis of complex glomerular diseases. This innovative approach could significantly impact future diagnostic strategies in renal disease research. This study introduces a transformative hyperspectral image classification model leveraging a Transformer to significantly improve glomerular disease diagnosis accuracy by synergizing spectral and spatial data, surpassing conventional methods. Through a rigorous comparative analysis, it was determined that while spectral features alone reached a peak accuracy of 85.24 %, the novel Convolutional Neural Network-Transformer (CVT) model's integration of spatial-spectral features via the Gramian Angular Field (GAF) method markedly enhanced diagnostic precision, achieving an average accuracy of 94 %. This methodological innovation not only overcomes traditional diagnostic limitations but also underscores the potential of non-invasive, high-precision technologies in advancing the classification and prognosis of complex renal diseases, setting a new benchmark in the field. [ABSTRACT FROM AUTHOR]