Selective C-H bond functionalization enables efficient access to valuable products from relatively simple precursors and thus remains a longstanding challenge in organic synthesis. This communication highlights the discovery of arylsulfonyl chlorides as readily available, inexpensive, and versatile reagents for C-H bond functionalization. A novel Pd-catalyzed synthesis of arylsulfones that features a rare C-H bond activation/functionalization to form a C-S bond is presented. By simple alterations of the reaction parameters, Pd-catalyzed C-Cl and C-C bond formation can also be achieved using arylsulfonyl chlorides as the oxidant.
By palladium catalysis, the C-H bond functionalization of O-phenylcarbamates with simple arenes has been achieved using sodium persulfate (Na(2)S(2)O(8)), an inexpensive, easy-to-handle, and environmentally friendly oxidant. This oxidative cross-coupling involves two aromatic C-H bonds undergoing concomitant oxidation to furnish a new biaryl C-C linkage. Excellent reaction efficiencies and regioselectivities were observed with a range of electron-rich, electron-neutral, and electron-deficient arenes; minimal homocoupling of either component was observed. When two reactive C-H bonds are present on the O-phenylcarbamate, selective diarylation can be achieved via quadruple C-H bond functionalization. This work represents a rare example of using O-carbamates as directing groups for catalytic C-H bond activation. Additionally, a palladacycle obtained from an O-phenylcarbamate was prepared and fully characterized. This trifluoroacetate-bridged bimetallic Pd complex exhibits clean conversion to the ortho-arylation product upon treatment with simple arenes. The addition of trifluoroacetic acid (TFA) was found to be critical for successful cyclopalladation of O-phenylcarbamates. We propose this oxidative arene cross-coupling occurs via two discrete C-H bond activations, namely cyclopalladation and electrophilic metalation, within a Pd(0/II) catalytic cycle.
An efficient enantioselective approach to form trans-γ-lactams in up to 99% yield, 93% ee and >20/1 dr using unactivated imines has been developed. The cyclohexyl-substituted azolium and the weak base sodium o-chlorobenzoate are most suitable for this transformation. Notably, the process involves cooperative catalysis by N-heterocyclic carbene and Brønsted acid.
Enantioselective trifluoromethylthiolation, especially of alkenes, is a challenging task. In this work, we have developed an efficient approach for enantioselective trifluoromethylthiolating lactonization by designing an indane-based bifunctional chiral sulfide catalyst and a shelf-stable electrophilic SCF3 reagent. The desired products were formed with diastereoselectivities of >99:1 and good to excellent enantioselectivities. The transformation represents the first enantioselective trifluoromethylthiolation of alkenes and the first enantioselective trifluoromethylthiolation that is enabled by a catalyst with a Lewis basic sulfur center.
Abstract:Precipitation is an important controlling parameter for land surface processes, and is crucial to ecological, environmental, and hydrological modeling. In this study, we propose a spatial downscaling approach based on precipitation-land surface characteristics. Land surface temperature features were introduced as new variables in addition to the Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM) to improve the spatial downscaling algorithm. Two machine learning algorithms, Random Forests (RF) and support vector machine (SVM), were implemented to downscale the yearly Tropical Rainfall Measuring Mission 3B43 V7 (TRMM 3B43 V7) precipitation data from 25 km to 1 km over the Tibetan Plateau area, and the downscaled results were validated on the basis of observations from meteorological stations and comparisons with previous downscaling algorithms. According to the validation results, the RF and SVM-based models produced higher accuracy than the exponential regression (ER) model and multiple linear regression (MLR) model. The downscaled results also had higher accuracy than the original TRMM 3B43 V7 dataset. Moreover, models including land surface temperature variables (LSTs) performed better than those without LSTs, indicating the significance of considering precipitation-land surface temperature when downscaling TRMM 3B43 V7 precipitation data. The RF model with only NDVI and DEM produced much worse accuracy than the SVM model with the same variables. This indicates that the Random Forests algorithm is more sensitive to LSTs than the SVM when downscaling yearly TRMM 3B43 V7 precipitation data over Tibetan Plateau. Moreover, the precipitation-LSTs relationship is more instantaneous, making it more likely to downscale precipitation at a monthly or weekly temporal scale.
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