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Enhancing heavy oil recovery is crucial to ensuring stable crude oil production. The development of stimulus-responsive Janus Pickering emulsifiers tailored for a reservoir environment has garnered significant attention in the field of reservoir production, emerging as a promising alternative to traditional surfactants. In this study, silica-based Janus nanosheets with temperature-responsive properties (OH-SiO 2 -PSBMA JNs) are synthesized using sol-gel process and atom transfer radical polymerization (ATRP) method. Experimental observations reveal that OH-SiO 2 -PSBMA JNs (0.1 wt %) can effectively reduce the interfacial tension (IFT) between heavy oil and water to 23.24 mN/m. Additionally, these nanosheets exhibited excellent emulsifying ability, forming stable emulsions with an average particle size of only 35 μm at a mass fraction of 1 wt % and demonstrating good salt tolerance (Salinity value: 1.51 × 10 4 mg/L). Furthermore, OH-SiO 2 -PSBMA JNs exhibited a temperature response that is well-suited for the reservoir environment, effectively stabilizing heavy oil emulsion at a high temperature (65 °C) and facilitating oil-water separation at a lower temperature (25 °C). These excellent properties of nanosheets contributed to obtaining an additional 13.2% recovery rate at low concentration (0.03 wt %). These results indicated that OH-SiO 2 -PSBMA JNs had the potential for enhanced oil recovery (EOR) application.

A nickel-catalyzed tandem alkoxylation/claisen rearrangement strategy for the oxy-difluoroallylation of ynamides has been developed. In this reaction, 3,3-difluoroallyl alcohol was used as a fluorine-containing building block to construct the C-CF 2 bond for the first time. This approach is recognized for its robust tolerance of functional groups, impressive yields, and excellent atomic efficiency, all achieved under mild reaction conditions. A series of β,β-difluoromethyleneamide derivatives were efficiently obtained through simple operations, and their practicality was confirmed through gram-scale synthesis and product derivatization.

Predicting protein-ligand binding affinity is a central issue in drug design. Various deep learning models have been published in recent years, where many of them rely on 3D protein-ligand complex structures as input and tend to focus on the single task of reproducing binding affinity. In this study, we have developed a graph neural network model called PLANET (Protein-Ligand Affinity prediction NETwork). This model takes the graph-represented 3D structure of the binding pocket on the target protein and the 2D chemical structure of the ligand molecule as input. It was trained through a multi-objective process with three related tasks, including deriving the protein-ligand binding affinity, protein-ligand contact map, and ligand distance matrix. Besides the protein-ligand complexes with known binding affinity data retrieved from the PDBbind database, a large number of non-binder decoys were also added to the training data for deriving the final model of PLANET. When tested on the CASF-2016 benchmark, PLANET exhibited a scoring power comparable to the best result yielded by other deep learning models as well as a reasonable ranking power and docking power. In virtual screening trials conducted on the DUD-E benchmark, PLANET's performance was notably better than several deep learning and machine learning models. As on the LIT-PCBA benchmark, PLANET achieved comparable accuracy as the conventional docking program Glide, but it only spent less than 1% of Glide's computation time to finish the same job because PLANET did not need exhaustive conformational sampling. Considering the decent accuracy and efficiency of PLANET in binding affinity prediction, it may become a useful tool for conducting large-scale virtual screening.

By carefully choosing the reaction conditions, we have developed the controllable FeCl 3 - or CuCl 2 -mediated dehydrazinative hydrogenation or chlorination of 3,3-difluoroallyl hydrazines to access α-CF 2 H or α-CF 2 Cl styrenes. The current reaction provides for the first time a facile method for the direct and selective synthesis of α-CF 2 H and α-CF 2 Cl styrenes starting from the same precursors, which is easy to scale up and displays a broad substrate scope and good functional group tolerance. Moreover, product derivatization experiments demonstrated that the resulting α-CF 2 Cl styrenes are practical and versatile building blocks for the diversified synthesis of fluorinated molecules.