ABSTRACT
Transition-metal catalyzed allylic substitution reactions of alkenes are among the most efficient methods for synthesizing diene compounds, driven by the inherent preference for an inner-sphere mechanism. Here, we present a demonstration of an outer-sphere mechanism in Rh-catalyzed allylic substitution reaction of simple alkenes using gem-difluorinated cyclopropanes as allyl surrogates. This unconventional mechanism offers an opportunity for the fluorine recycling of gem-difluorinated cyclopropanes via C - F bond cleavage/reformation, ultimately delivering allylic carbofluorination products. The developed method tolerates a wide range of simple alkenes, providing access to secondary, tertiary fluorides and gem-difluorides with 100% atom economy. DFT calculations reveal that the C - C bond formation goes through an unusual outer-sphere nucleophilic substitution of the alkenes to the allyl-Rh species instead of migration insertion, and the generated carbon cation then forms the C - F bond with tetrafluoroborate as a fluoride shuttle.
ABSTRACT
Cell imaging technology is undoubtedly a powerful tool for studying single-cell heterogeneity due to its non-invasive and visual advantages. It covers microscope hardware, software, and image analysis techniques, which are hindered by low throughput owing to abundant hands-on time and expertise. Herein, a cellular nucleus image-based smarter microscope system for single-cell analysis is reported to achieve high-throughput analysis and high-content detection of cells. By combining the hardware of an automatic fluorescence microscope and multi-object recognition/acquisition software, we have achieved more advanced process automation with the assistance of Robotic Process Automation (RPA), which realizes a high-throughput collection of single-cell images. Automated acquisition of single-cell images has benefits beyond ease and throughout and can lead to uniform standard and higher quality images. We further constructed a single-cell image database-based convolutional neural network (Efficient Convolutional Neural Network, E-CNN) exceeding 20618 single-cell nucleus images. Computational analysis of large and complex data sets enhances the content and efficiency of single-cell analysis with the assistance of Artificial Intelligence (AI), which breaks through the super-resolution microscope's hardware limitation, such as specialized light sources with specific wavelengths, advanced optical components, and high-performance graphics cards. Our system can identify single-cell nucleus images that cannot be artificially distinguished with an accuracy of 95.3%. Overall, we build an ordinary microscope into a high-throughput analysis and high-content smarter microscope system, making it a candidate tool for Imaging cytology.
