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Natural gas is a stable and flexible low-carbon fossil energy. It has comparative advantages on both supply and consumption sides. It can be used as a transitional energy for constructing a clean, low-carbon, safe, and efficient new energy system and for achieving carbon peak and carbon neutrality. Currently, China’s energy transition has many scheme designs. Huge differences exist in the development prospects of natural gas. In this study, we analyze the relationship, challenges, and principles of carbon peak and carbon neutrality. Based on these analyses, we study the opportunities for natural gas development in China, explore the comparative advantages of natural gas in key areas such as power generation, transportation, city gas, and industry, and summarize the constraints in terms of terminal prices, infrastructure, pricing mechanism, and development speed. We also propose the development goals and key tasks of China’s natural gas. Finally, five recommendations are proposed for China’s natural gas development under the carbon goals. The first is to strengthen top-level coordination and leadership to clarify the development positioning and ideas of natural gas.The second is to maximize the comparative advantages to promote natural gas consumption. The third is to rationally use domestic and foreign resources to ensure sufficient and reliable supply. The fourth is to promote infrastructure construction to improve the supply efficiency. The fifth is to strengthen the policies and mechanisms for land, finance, taxation. more...

Control over the two-dimensional electron gas (2DEG) in AlGaN/GaN heterostructures is crucial for their practical applications in current semiconducting devices. However, the oxide surface structures inducing 2DEG are still ambiguous because oxide-stoichiometry (OS) matching structures possess occupied surface donor states at 1.0–1.8 eV below the conduction band minimum of bulk but are usually not available in energy than electron counting (EC) rule structures. In this work, a global optimization algorithm was introduced to explore the possible oxidation structures on GaN (0001) and AlN (0001) surfaces; the method was demonstrated to be available due to the fact that the reported oxidized structures were reproduced at each stoichiometry. Interestingly, the two similar oxide structures with close energy were found in each oxide-bilayer, which can be used to clarify the experimental observations of disordered surface oxide layers below 550 °C. Additionally, new stable oxidation structures with low surface energy were proposed. Interestingly, the new OS matching structures are proposed with remarkably lower energy than EC rule structures under cation-rich and oxygen-poor conditions, which is caused by the large formation enthalpy of Al2O3 and Ga2O3. Further electronic structure calculations demonstrate that the new OS structures possess highest occupied states above the half of the gap and are the origin of 2DEG in AlGaN/GaN heterostructures. [ABSTRACT FROM AUTHOR] more...

The use of a single-atom FeN4 catalyst on defective graphene (Fe-NC) has recently emerged as an effective method for the synthesis of amino acids. Herein, we investigated the mechanism of alanine formation on FeN4-doped graphene using comprehensive density functional theory (DFT) computations. The alanine formation reaction begins with the activation of NO molecules on the surface, followed by their reaction with hydrogen atoms provided in the system. The computational results show that NO molecules can be effectively activated on Fe-NC, facilitating the subsequent alanine formation at a relatively lower potential. The potential-limiting step in alanine production involves either the formation of HNO* or HNOH* intermediates on Fe-NG, as the free energy changes (ΔG) in these two elementary steps are nearly equivalent. Notably, the formation of HNO* exhibits a higher activation energy (Ea) compared to HNOH* formation. This study provides valuable insights into the C–N coupling reaction and the mechanism of amino acid synthesis on single-atom catalysts. [ABSTRACT FROM AUTHOR] more...

The hydraulic fracturing process is a prominent example of fracture network evolution under stress. However, the interactions between hydraulic fractures and natural fracture networks, along with the connectivity evolution of the resultant fracture networks, require more research. This research incorporates discrete fracture networks to characterize subsurface structures and employs the Discrete Element ‐ Lattice Boltzmann Method to simulate the hydraulic fracturing process. The dynamic evolution of subsurface structures, including the initiation of hydraulic fractures and their interaction with natural fractures, is systematically investigated. Results indicate that natural fractures significantly impact fracture initiation, propagation, and connectivity evolution, which in turn affects fluid production. Fracture strength is key for the interaction, and hydraulic fractures tend to propagate along weak natural fractures with low resistance. Fracture strength variability determines the final fracture networks, with low‐strength fractures breaking due to the altered in‐situ stress and forming local clusters. High injection rates and fluid viscosity result in a large pressure buildup and exaggerate the influential region. A multi‐cluster system is thus formed during the hydraulic fracturing process, and its connectivity can be well quantified with a novel connectivity metric. In low‐permeable reservoirs, fracture clusters connected to production wells can contribute instantly, while local clusters may contribute to production from a long‐term perspective. Injection rate, fluid viscosity, fracture orientation, and clustering effects have consistent positive correlations with the total connectivity and production. Heterogeneity has a weak positive correlation with fluid production, while a moderate negative correlation with total connectivity. Plain Language Summary: Hydraulic fracturing is a widely employed technique in developing unconventional reservoirs. Ubiquitous natural fractures in the deep subsurface significantly influence the hydraulic fracturing outcomes; however, the interactions between hydraulic and natural fractures remain incompletely understood. In this work, we optimize the numerical scheme to incorporate natural fractures and explore their impact on hydraulic fracturing. We found that natural fractures significantly impact fracture initiation, propagation, and connectivity evolution, which in turn affects fluid production. Fracture strength is key for the interaction, and hydraulic fractures tend to propagate along weak natural fractures with low resistance. Fracture strength variability can significantly impact the final fracture networks. Low‐strength fractures tend to break due to the altered in‐situ stress, forming local clusters. High injection rates and fluid viscosity result in a large pressure buildup and exaggerate the influential region. Furthermore, we propose a connectivity metric to quantify the connectivity evolution of multi‐cluster fracture networks formed during the hydraulic fracturing process. Therefore, the quantitative analysis of sensitivity for influential factors on connectivity and production is available and discussed. These findings are essential for us to better understand the subsurface structures and their dynamic evolution under the hydraulic fracturing process. Key Points: Natural fractures significantly impact fracture initiation, propagation, and connectivity evolution during hydraulic fracturingLow‐strength fractures break due to altered in‐situ stress, resulting in the final fracture networks forming a multi‐cluster systemHeterogeneity is crucial in forming final fracture networks, and high injection rates and viscosity exacerbate their influence areas [ABSTRACT FROM AUTHOR] more...

Simulating vehicle trajectories at intersections is one of the challenging tasks in traffic simulation. Existing methods are often ineffective due to the complexity and diversity of lane topologies at intersections, as well as the numerous interactions affecting vehicle motion. To address this issue, we propose a deep learning based vehicle trajectory simulation method. First, we employ a vectorized representation to uniformly extract features from traffic elements such as pedestrians, vehicles, and lanes. By fusing all factors that influence vehicle motion, this representation makes our method suitable for a variety of intersections. Second, we propose a deep learning model, which has an attention network to dynamically extract features from the surrounding environment of the vehicles. To address the issue of vehicles continuously entering and exiting the simulation scene, we employ an asynchronous recurrent neural network for the extraction of temporal features. Comparative evaluations against existing rule‐based and deep learning‐based methods demonstrate our model's superior simulation accuracy. Furthermore, experimental validation on public datasets demonstrates that our model can simulate vehicle trajectories among the urban intersections with different topologies including those not present in the training dataset. [ABSTRACT FROM AUTHOR] more...