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By combining tandem asymmetric gold catalysis and subsequent stereoconvergent hydrolysis of enol ester in a one-pot process, hydroxylated propargylic esters are converted into chiral β-oxygenated ketones with mostly good enantiomeric ratios and in largely good to excellent yields. The product chiral center is formed via stereoselective cyclization of a hydroxylated allenyl ester intermediate, which is enabled by asymmetric gold-ligand cooperation.

By employing a chiral bifunctional phosphine ligand, a gold(I)-catalyzed efficient and highly enantioselective dearomatization of phenols is achieved via versatile metal-ligand cooperation. The reaction is proven to be remarkably general in scope, permitting substitutions at all four remaining benzene positions, accommodating electron-withdrawing groups including strongly deactivating nitro, and allowing carbon-based groups of varying steric bulk including tert-butyl at the alkyne terminus. Moreover, besides N-(o-hydroxyphenyl)alkynamides, the corresponding ynoates and ynones are all suitable substrates. Spirocyclohexadienone-pyrrol-2-ones, spirocyclohexadienone-butenolides, and spirocyclohexadenone-cyclopentenones are formed in yields up to 99 % and with ee up to 99 %.

The pore structure has a significant impact on the capability of coal seams to adsorb methane. But there is currently limited research on the features of adsorption pore structure in high-rank coal and its influence on methane adsorption capability. Taking the high-rank coal samples from Nuodong Coal Mine of Guizhou Xing'an Coal Industry Co., Ltd. as the research object, low-temperature N2 adsorption and low-temperature CO2 adsorption experiments are conducted. Combined with fractal theory, this paper studies the pore structure features of high-rank coal adsorption pores. Through high-pressure isothermal methane adsorption experiments, the influence of coal reservoir properties, pore structure features, and fractal dimension on methane adsorption capability is analyzed. The results show the following points. ① The pore morphology of high-rank coal reservoirs is relatively simple, mostly consisting of parallel plate pores and narrow slit pores with open ends. Micro pores dominate the pore structure of coal, with pore volume and pore specific surface area accounting for more than 98%, providing space for gas enrichment. ② The method calculates the comprehensive fractal dimension of high-rank coal pores based on the proportion of pore volume in different aperture segments, with micropore fractal dimension dominating the comprehensive fractal dimension. The pore structure of coal samples has obvious fractal features and strong heterogeneity of pores. ③ The Langmuir model can describe the adsorption behavior of high-rank coal. The physical properties, pore structure, and fractal dimension of coal reservoirs have a significant impact on methane adsorption capability. Langmuir volume is linearly positively correlated with maximum vitrinite reflectance, vitrinite content, ash content, and moisture content. It is linearly negatively correlated with inertinite content. The Langmuir volume is linearly positively correlated with the pore specific surface area and pore volume of the adsorption pores. The Langmuir volume is weakly linearly correlated with the fractal dimension. The research results can provide theoretical guidance for the exploration and development of high-rank coalbed methane and the prevention and control of coal mine methane disasters in southwestern Guizhou.

This work implements a catalytic SN2 glycosylation by employing an amide-functionalized 1-naphthoate platform as a latent glycosyl leaving group. Upon gold-catalyzed activation, the amide group enables the SN2 process by directing the attack of the glycosyl acceptor via H-bonding interaction, which results in stereoinversion at the anomeric center. Unique in this approach is that the amide group also enables a novel safeguarding mechanism by trapping oxocarbenium intermediates and, hence, minimizing stereorandom SN1 processes. The strategy is applicable to the synthesis of a broad range of glycosides with high to excellent levels of stereoinversion from anomerically pure/enriched glycosyl donors. These reactions are generally high-yielding, and their applications in the synthesis of challenging 1,2-cis-linkage-rich oligosaccharides are demonstrated.

An oxidative strategy is reported to access α-oxo BMIDA gold carbenes directly from BMIDA-terminated alkynes. Besides offering expedient access to seldom studied boryl metal carbenes, these BMIDA gold carbene species undergo facile insertions into methyl, methylene, methine, and benzylic C-H bonds in the absence of the Thorpe-Ingold effect. They also undergo efficient OH insertion, cyclopropanation, and F-C alkylations. This chemistry provides rapid access to structurally diverse α-BMIDA ketones, which are scarcely documented. In combination with DFT studies, the role of BMIDA is established to be an electron-donating group that attenuates the high electrophilicity of the gold carbene center.

The conventional reservoir automatic history matching method requires multiple computationally time-consuming reservoir numerical simulations. Deep learning agent models can perform alternative reservoir numerical simulation calculations with approximate accuracy and greater computational efficiency. In the reservoir automatic history matching method based on the deep learning agent model， the reservoir uncertainty parameters adjusted by the reservoir automatic history matching method are usually used as the input parameters of the deep learning agent model. Existing deep learning agent models are often single input-output neural network model architectures. They do not consider that reservoir automatic history matching methods require the adjustment of multiple reservoir uncertainty parameters. Multiple deep learning agent models need to be trained to predict water saturation field distribution and pressure field distribution in reservoirs. For solving this problem， a reservoir automatic history matching method based on a dual input-output convolutional neural network agent model was proposed to simultaneously predict the water saturation field distribution and pressure field distribution in a reservoir by using a dual input-output convolutional neural network， with the reservoir permeability field distribution and phase permeability parameters as input. The production was calculated with the help of the Peaceman equation. It was coupled to the ensemble smoother with multiple data assimilation （ES-MDA） methods to invert the reservoir permeability field distribution and phase permeability parameters to achieve a more efficient reservoir automatic history matching solution. The results of the study show that the prediction accuracy of the reservoir water saturation field distribution and pressure field distribution is above 93% at the specified time step based on the dual input-output convolutional neural network agent model. Compared with the traditional reservoir automatic history matching method， the proposed reservoir automatic history matching method based on a dual input-output convolutional neural network agent model avoids the time-consuming computation of multiple calls to the reservoir numerical simulator and improves the efficiency of the matching.

The stereoselective construction of 1,2-cis furanosidic linkage is synthetically challenging. A strategy that applies to all furanose types remains elusive. In this work, a solution is developed based on gold catalysis and the deployment of the directing-group-on-leaving-group strategy, where a basic oxazole group in the gold-activated leaving group facilitates the stereoinvertive attack by glycosyl acceptors. In addition to exhibiting good to excellent 1,2-cis selectivities, these furanosylation reactions are high-yielding and mostly complete in 30 min to 2 h. A broad range of 1,2-cis-furanosides is prepared. Although some are uncommon, the ease of access enabled by this approach presents new opportunities to study their applications in medicine and materials research.