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The global oil and gas exploration targets are gradually moving towards a new field of oil and gas accumulation with nanopore throats, ranging from millimeter scale to micro-nano pore throats. The development method of tight oil reservoirs is different from that of conventional oil reservoirs, and the development efficiency is constrained. Therefore, it is necessary to construct a nanoscale fluid with strong diffusion and dispersion and improve its permeability, suction, and displacement capabilities. Under the background of CCUS, carbon dioxide flooding is a better way to develop tight reservoirs. However, in order to solve the problem of gas channeling, this paper developed a carbon dioxide-responsive graphene point type surfactant, which has a good gas–liquid synergistic effect. At the same time, graphene nanomaterials are carbon-based and create no environmental damage in oil reservoirs. In this study, graphene quantum dots (GQDs) were prepared using the hydrothermal method, and functional graphene quantum dots (F-GQDs) responsive to carbon dioxide stimulation were synthesized by covalent grafting of amidine functional groups. By characterizing its structure and physical and chemical properties, and by conducting imbibition simulation experiments, its imbibition and drainage ability in nanopore throats is elucidated. Infrared spectrum measurement shows that after functional modification, the quantum dots exhibited new characteristic peaks at 1600 cm−1 to 1300 cm−1, considering the N-H plane-stretching characteristic peak. The fluorescence spectra showed that the fluorescence intensity of F-GQDs was increased after functional modification, which indicated that F-GQDs were successfully synthesized. Through measurements of interfacial activity and adhesion work calculations, the oil–water interfacial tension can achieve ultra-low values within the range of 10−2 to 10−3 mN/m. Oil sand cleaning experiments and indoor simulations of spontaneous imbibition in tight cores demonstrate that F-GQDs exhibit effective oil-washing capabilities and a strong response to carbon dioxide. When combined with carbon dioxide, the system enhances both the rate and efficiency of oil washing. Imbibition recovery can reach more than 50%. The research results provide a certain theoretical basis and data reference for the efficient development of tight reservoirs. [ABSTRACT FROM AUTHOR]

Systematic investigation of tumor-infiltrating immune (TII) cells is important to the development of immunotherapies, and the clinical response prediction in cancers. There exists complex transcriptional regulation within TII cells, and different immune cell types display specific regulation patterns. To dissect transcriptional regulation in TII cells, we first integrated the gene expression profiles from single-cell datasets, and proposed a computational pipeline to identify TII cell type-specific transcription factor (TF) mediated activity immune modules (TF-AIMs). Our analysis revealed key TFs, such as BACH2 and NFKB1 play important roles in B and NK cells, respectively. We also found some of these TF-AIMs may contribute to tumor pathogenesis. Based on TII cell type-specific TF-AIMs, we identified eight CD8+ T cell subtypes. In particular, we found the PD1 + CD8+ T cell subset and its specific TF-AIMs associated with immunotherapy response. Furthermore, the TII cell type-specific TF-AIMs displayed the potential to be used as predictive markers for immunotherapy response of cancer patients. At the pan-cancer level, we also identified and characterized six molecular subtypes across 9680 samples based on the activation status of TII cell type-specific TF-AIMs. Finally, we constructed a user-friendly web interface CellTF-AIMs (http://bio-bigdata.hrbmu.edu.cn/CellTF-AIMs/) for exploring transcriptional regulatory pattern in various TII cell types. Our study provides valuable implications and a rich resource for understanding the mechanisms involved in cancer microenvironment and immunotherapy. [ABSTRACT FROM AUTHOR]

As an important means to enhance oil recovery, ternary composite flooding (ASP flooding for short) technology has achieved remarkable results in Daqing Oilfield. Alkalis, surfactants and polymers are mixed in specific proportions and injected into the reservoir to give full play to the synergistic effect of each component, which can effectively enhance the fluidity of crude oil and greatly improve the oil recovery. At present, the technology for further improving oil recovery after ternary composite flooding is not mature and belongs to the stage of technical exploration. The presence of alkaline substances significantly alters the reservoir's physical properties and causes considerable corrosion to the equipment used in its development. This is detrimental to both the environment and production. Therefore, it is necessary to develop green displacement control agents. In the reservoir environment post-ASP flooding, 2-(methylamino)ethyl methacrylate and glycidyl methacrylate were chosen as monomers to synthesize a polymer responsive to alkali, and then grafted with cellulose nanocrystals to form microspheres of alkali-resistant swelling hydrogel. Cellulose nanocrystals (CNCs) modified with functional groups and other materials were utilized to fabricate hydrogel microspheres. The product's structure was characterized and validated using Fourier transform infrared spectroscopy and X-ray diffraction. The infrared spectrum revealed characteristic absorption peaks of CNCs at 1165 cm−1, 1577 cm−1, 1746 cm−1, and 3342 cm−1. The diffraction spectrum corroborated the findings of the infrared analysis, indicating that the functional modification occurred on the CNC surface. After evaluating the swelling and erosion resistance of the hydrogel microspheres under various alkaline conditions, the optimal particle size for compatibility with the target reservoir was determined to be 6 μm. The potential of cellulose-based gel microspheres to enhance oil recovery was assessed through the evaluation of Zeta potential and laboratory physical simulations of oil displacement. The study revealed that the absolute value of the Zeta potential for gel microspheres exceeds 30 in an alkaline environment with pH values ranging from 7 to 14, exhibiting a phenomenon where stronger alkalinity correlates with a greater absolute value of Zeta potential. The dispersion stability spans from good to excellent. The laboratory oil displacement simulation experiment was conducted using a cellulose-based gel microsphere system following weak alkali ASP flooding within the pH value range from 7 to 10. The experimental interventions yielded recovery rates of 2.98%, 3.20%, 3.31%, and 3.38%, respectively. The study indicates that cellulose-based gel microspheres exhibit good adaptability in alkaline reservoirs. This research offers a theoretical foundation and experimental approaches to enhance oil recovery techniques post-ASP flooding. [ABSTRACT FROM AUTHOR]

Automated rubber tapping not only frees the workers from heavy physical labor and harsh working conditions, but also reduces the dependence on the workers' skills and greatly increases tapping efficiency. The key technologies for tapping robots are the independent acquisition of operational information and servo control of the tapping position in unstructured environments. In this study, taking rubber tree in rubber plantations as object, incorporating robot kinematics, machine vision technology and multi-sensor feedback control technology, a modular prototype of a rubber tapping robot was developed. The rubber tapping robot was mainly composed of an orbital mobile platform, a multi-joint robotic arm, a binocular stereo vision system and an end-effector. The binocular stereo vision and structured light system were used to obtain the structural parameters of the rubber trunk and secant. A six-joint tandem robotic arm was used for the planning and realization of complex rubber tapping trajectories. An multi-sensor fusion end-effector was developed to complete the identification of the starting point, the measurement of cut compensation and the tapping operation. To address the technical difficulties in rubber tapping operations, such as complex and variable environment, superimposed interaction of operational information, similar target background features and sub-millimeter operational accuracy requirements, the spatial mathematical model of the rubber tapping trajectory was established to plan the robot's movement path for fast approaching and moving away from the operation space. The results of the field tests conducted at a natural rubber plantation in Hainan province showed that the accuracy in bark consumption was about 0.28 mm and the accuracy in cutting depth was about 0.49 mm when the rubber tapping robot cut 1 mm thick bark. Compared to manual operations, the continuity of the chips and the flatness of the rubber output surface were improved significantly. This research could provide a positive reference and development direction for exploring automated rubber tapping operations.

To detect the coal-carrying rate in gangue, a new method based on three-dimensional (3D) image features and gray wolf optimization-support vector machine (GWO-SVM) was proposed. First, region segmentation was performed for each sample image according to the particles' shapes automatically, and each region was identified as coal or gangue. For each region, 4 geometry features and 11 density features were extracted, respectively. In each sample image, the geometry features of the coal region were added by category, while its density features were averaged by category, and the gangue region performed the same operation. The initial feature set, i.e. a two-dimensional (2D) image feature set was obtained by dividing the gangue feature value by the coal feature value. Binocular images were then gathered to extract the third-dimensional (3D) feature, that is, the height feature of the particles. The final feature set consisted of the 2D image features and the third-dimensional feature. Extreme Gradient Boosting was applied to calculate the importance ranking of all the features, and the top nine features were selected as the best features. Then, the coal-carrying rate in gangue was modeled using the SVM optimized by gray wolf optimization. Finally, based on 16 complete features, 8 optimal two-dimensional features and 9 optimal three-dimensional features, three models were established respectively. The results showed that the proposed 3D feature model performed the best with an average relative error of 5.78%. [ABSTRACT FROM AUTHOR]