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The original Leray’s problem concerns the well-posedness of weak solutions to the steady incompressible Navier-Stokes equations in a distorted pipe, which approach the Poiseuille flow subject to the no-slip boundary condition at spatial infinity. In this paper, the same problem with the Navier-slip boundary condition instead of the no-slip boundary condition, is addressed. Due to the complexity of the boundary condition, some new ideas, presented as follows, are introduced to handle the extra difficulties caused by boundary terms. First, the Poiseuille flow in the semi-infinite straight pipe with the Navier-slip boundary condition will be introduced, which will serve as the asymptotic profile of the solution to the generalized Leray’s problem at spatial infinity. Second, a solenoidal vector function defined in the whole pipe, satisfying the Navier-slip boundary condition, having the designated flux and equalling the Poiseuille flow at a large distance, will be carefully constructed. This plays an important role in reformulating our problem. Third, the energy estimates depend on a combined L2-estimate of the gradient and the stress tensor of the velocity.

Target detection is a critical task in interpreting hyperspectral remote sensing images. Small target (such as airplanes) detection is challenging, especially in large-scale complex scenes with high spectral variability of different land cover types. In this article, we propose a transfer learning-based, large-scale, robust hyperspectral target detector (TLH2TD) to improve the accuracy of hyperspectral target detection (HTD) in large-scale complex scenes. TLH2TD learns the spatial features of hyperspectral targets from high-resolution remote sensing images and achieves high-precision HTD with fused spatial–spectral features. It comprises three parts. First, the coupled target-background sample expansion (CTBSE) module is designed to expand the labeled hyperspectral target and background samples with sufficient high-resolution, labeled RGB images, and a few labeled hyperspectral samples. Second, the hard positive and negative example mining (HPNEM) module trains the hard positive and negative samples to enhance the discriminative ability of the network, addressing the problem of inadequate sample training in large-scale hyperspectral images (HSIs). Third, the spatial–spectral weighted subspace (SSWS) module is designed to fuse the spatial features extracted from the target detection network and the spectral features based on the Mahalanobis distance. The results show that: 1) the TLH2TD achieves average area under the curve (AUC) values of 0.96, 0.96, and 0.93 on small-, medium-, and large-sized HSIs, respectively, achieving the highest accuracy compared with other HTD algorithms; 2) the TLH2TD exhibits the highest detection time efficiency for medium- and large-sized HSIs; and 3) for large-sized HSIs, when most HTD algorithms fail, TLH2TD exhibits significantly higher accuracy and time efficiency than other methods. This is an important achievement in meeting the robust target detection tasks of large-scale hyperspectral remote sensing images.

Fast, sensitive, and low-cost high-performance detectors have gradually become an indispensable tool for people to keep healthy, and sensors are the key devices of detection equipment. In this work, a ZnO nanocrystal modified carbon fiber electrode was prepared using a hydrothermal method, and on this basis, a galvanostatic point deposition method was used to load copper nanoparticles to prepare a sensor electrode. Scanning electron microscopy and X-ray diffraction were used to comprehensively analyze composition, morphology, and environmental adaptability of the prepared electrodes. The DPV test was used to verify the enhanced effect of ZnO nanorods on neurotransmitter detection. The ZnO/CF showed an obvious electrical signal (0.22 V, 4 × 10−5A) in the detection of dopamine (DA) solution, and the Cu-NPs/ZnO/CF also showed excellent detection results in the glucose detection experiment., providing two excellent examples for the development of low-cost electrochemical sensors. The electrodes can specifically detect DA in the presence of ascorbic acid and uric acid, and the detection limit of the electrode for detecting DA is about 0.4 μM. In addition, the Cu-NPs/ZnO/CF electrode successfully realized the enzyme-free detection of glucose, and the detection limit could reach 0.5 μM.

Shale oil reservoirs are more complex and difficult to exploit than conventional and tight oil reservoirs because of low porosity, permeability, and rich organic matter. The presence of organic matter has great influence on the flow behaviors of oil in shale and the development methods for shale oil. In this work, the saturation and movability behaviors of oil in organic and inorganic matter of shale were investigated by experimental methods. First, vacuum-imbibition tests were carried out to evaluate the saturated oil amount at different states (in inorganic pores, organic pores, and adsorbed/dissolved in organic matter) in shale cores and to calculate the inorganic/organic saturation and inorganic/organic porosity. The results show that the imbibition volume of oil is 2.4–5.7 times as much as those of KCl solutions for the several shale cores. The excess of imbibition oil is adsorbed or dissolved into the organic matter and dispersed in the organic pores. The organic saturation of oil in the shale samples is from 58.9 to 82.6%, which is 1.4–4.7 times as much as that in inorganic matter. The inorganic porosity is from 1.9 to 4.4%, while the organic porosity is from 0.3 to 1.3%. Moreover, the oil movability under centrifugation in the organic and inorganic matter of shale was measured by nuclear magnetic resonance. The results show that the content of movable oil in inorganic pores is 20–80%, which is higher than that of organic matter with a value of lower than 10%. The total movable content of oil in shale is influenced greatly by the content of movable oil in organic matter. Therefore, how to displace the oil in organic matter is the main problem to be solved in developing shale oil.

Exploiting stretchable solar cells that can accommodate large strain and feature high cyclic mechanical endurance is challenging for wearable and skin-interfaced electronics application. In this work, we demonstrated such solar cells using the kirigami design. Experiments and mechanical simulations showed that the kirigami structure effectively imparted stretchability to perovskite solar cells (PSCs) through out-of-plane deformation, which significantly reduced the stress in devices. The kirigami-based PSCs with optimal geometric parameters exhibited high mechanical deformability, including stretchability (strain up to 200%), twistability (angle up to 450°), and bendability (radius down to 0.5 mm). More importantly, the kirigami PSCs revealed high mechanical endurance with almost unchanged performance even after 1000 repetitive stretching, twisting, and bending cycles. This kirigami design for stretchable PSCs presented here provides a promising strategy to achieve high deformability for solar cells as well as other optoelectronic devices.

CO2miscible displacement is an efficient method for enhanced shale-oil recovery. Understanding the movability of shale oil in different states of occurrence is important for CO2miscible displacement. Therefore, a nuclear magnetic resonance (NMR)-based CO2miscible displacement experiment is conducted on oil-saturated shale and sandstone. To describe the processes involved in the measurements, a mathematical model based on the Maxwell–Stefan equation is developed; it considers the heterogeneity of shale and the competitive adsorption and absorption of CO2–oil mixtures in organic matter. The results show that the porosity of immobile oil in the shale samples (4.0% and 4.3%) is lower than the porosity of free oil (8.0% and 10.2%). Compared with sandstone, a larger CO2-injection time yields a smaller recovery factor in the case of shale, and the recovery factor of immobile oil is much less than that of free oil in the shale. According to the model, the order of oil production in the laminated shale during CO2miscible displacement is as follows: free oil in the inclusions, free oil in organic matrix-clay, and adsorption and absorption of oil in organic matter. The oil production rate is associated with the heterogeneity of shale (R0) and the competitive adsorption and absorption of CO2-oil mixture in the organic matter (ω0, kads).

CO2cyclic injection is a promising method for enhanced shale oil recovery. However, the enhanced shale oil recovery mechanism is unclear, especially the adsorption and dissolution of CO2and oil in kerogen. Therefore, it is of great importance to study the adsorption and dissolution mechanisms of CO2and oil mixtures in shale. In this study, a new experimental apparatus was designed to test the change in the mole fractions of CO2and oil before and after adsorption and dissolution at equilibrium conditions. For simplicity, n-dodecane (n-C12) was used as the oil. The adsorption and dissolution amounts of CO2and n-C12were obtained using a mathematical method. Moreover, the adsorption and dissolution characteristics of the CO2and n-C12mixtures in shale and the effect of pressure on the adsorption and dissolution amounts were studied. Finally, the swelling factor of the shale, which was caused by the dissolution of the mixtures, was calculated from the experimental results. The results show that dissolved n-C12in shale could be replaced by CO2when the mole fraction of CO2in the free phase was larger than a threshold. The adsorption and dissolution amounts of CO2and n-C12increased with pressure. The lower pressure and larger mole fraction of CO2enabled a lower swelling factor of shale. This study provides a straightforward method to experimentally determine the adsorption and dissolution properties of shale, which can be used to evaluate enhanced shale oil recovery by CO2injection and the geological storage of CO2.