Understanding gas transport mechanisms in shale gas reservoir: Pore network modelling approach

This report summarizes the recent findings on gas transport mechanisms in shale gas reservoir by pore network modelling. Multi-scale pore network model was developed to accurately characterize the shale pore structure. The pore network single component gas transport model was established considering the gas slippage and real gas property. The gas transport mechanisms in shale pore systems were elaborated on this basis. A multicomponent hydrocarbon pore network transport model was further proposed considering the influences of capillary pressure and fluid occurrence on fugacity balance. The hydrocarbon composition and pore structure influences on condensate gas transport were analyzed. These results provide valuable insights on gas transport mechanisms in shale gas reservoir.Cited as: Song, W., Yao, J., Zhang, K., Yang, Y., Sun, H. Understanding gas transport mechanisms in shale gas reservoir: Pore network modelling approach. Advances in Geo-Energy Research, 2022, 6(4): 359-360. https://doi.org/10.46690/ager.2022.04.1

Evidence for quasi-one-dimensional charge density wave in CuTe by angle-resolved photoemission spectroscopy

We report the electronic structure of CuTe with a high charge density wave (CDW) transition temperature Tc = 335 K by angle-resolved photoemission spectroscopy (ARPES). An anisotropic charge density wave gap with a maximum value of 190 meV is observed in the quasi-one-dimensional band formed by Te px orbitals. The CDW gap can be filled by increasing temperature or electron doping through in situ potassium deposition. Combining the experimental results with calculated electron scattering susceptibility and phonon dispersion, we suggest that both Fermi surface nesting and electron-phonon coupling play important roles in the emergence of the CDW

Specific detection and deletion of the sigma-1 receptor widely expressed in neurons and glial cells in vivo

The chaperon protein sigma-1 receptor (S1R) has been discovered over 40 years ago. Recent pharmacological studies using S1R exogenous ligands demonstrated a promising therapeutical potential of targeting the S1R in several neurological disorders. Although intensive in vitro studies have revealed S1Rs are mainly residing at the membrane of the endoplasmic reticulum (ER), the cell-specific in vivo expression pattern of S1Rs is still unclear, mainly because of the lack of a reliable detection method which also prevented a comprehensive functional analysis. Here, first, we identified a highly specific antibody using S1R knockout (KO) mice and established an immunohistochemical protocol involving a 1% sodium dodecyl sulphate (SDS) antigen retrieval step. Second, we characterized the S1R expression in the mouse brain and can demonstrate that the S1R is widely expressed: in principal neurons, interneurons and all glial cell types. In addition, unlike reported in previous studies, we showed that the S1R expression in astrocytes is not colocalized with the astrocytic cytoskeleton protein GFAP. Thus, our results raise concerns over previously reported S1R properties. Finally, we generated a Credependent S1R conditional KO mouse (S1R flox) to study cell-type-specific functions of the S1R. As a proof of concept, we successfully ablated S1R expressions in neurons or microglia employing neuronal and microglial Cre-expressing mice, respectively. In summary, we provide powerful tools to cell-specifically detect, delete and functionally characterize S1R in vivo

Selection of Cluster Heads for Wireless Sensor Network in Ubiquitous Power Internet of Things

This paper designs a selection algorithm of cluster heads (CHs) in wireless sensor network (WSN) under the ubiquitous power Internet of Things (UPIoT), aiming to solve the network failure caused by premature death of WSN sensors and overcome the imbalance in energy consumption of sensors. The setting of the cluster head node helps to reduce the energy consumption of the nodes in the network, so the choice of cluster head is very important. The author firstly explains the low energy adaptive clustering hierarchy (LEACH) and the distance and energy based advanced LEACH (DEAL) protocol. Compared with the LEACH, the DEAL considers the remaining nodal energy and the sensor-sink distance. On this basis, the selectivity function-based CH selection (SF-CHs) algorithm was put forward to select CHs and optimize the clustering. Specifically, the choice of CHs was optimized by a selectivity function, which was established based on the remaining energy, number of neighbors, motion velocity and transmission environment of sensors. Meanwhile, a clustering function was constructed to optimize the clustering, eliminating extremely large or small clusters.Finally, the simulation proves that the DEAL protocol is more conducive to prolonging the life cycle of the sensor network. The SF-CHs algorithm can reduce the residual energy variance of nodes in the network, and the network failure time is later, which provides a way to improve the stability of the network and reduce energy loss

Energy Optimization for WSN in Ubiquitous Power Internet of Things

This paper attempts to solve the problems of uneven energy consumption and premature death of nodes in the traditional routing algorithm of rechargeable wireless sensor network in the ubiquitous power Internet of things. Under the application environment of the UPIoT, a multipath routing algorithm and an opportunistic routing algorithm were put forward to optimize the network energy and ensure the success of information transmission. Inspired by the electromagnetic propagation theory, the author constructed a charging model for a single node in the wireless sensor network (WSN). On this basis, the network energy optimization problem was transformed into the network lifecycle problem, considering the energy consumption of wireless sensor nodes. Meanwhile, the traffic of each link was computed through linear programming to guide the distribution of data traffic in the network. Finally, an energy optimization algorithm was proposed based on opportunistic routing, in a more realistic low power mode. The experimental results show that the two proposed algorithms achieved better energy efficiency, network lifecycle and network reliability than the shortest path routing (SPR) and the expected duty-cycled wakeups minimal routing (EDC). The research findings provide a reference for the data transmission of UPIoT nodes