An unprecedented direct C-H bond functionalization of unprotected phenols with α-aryl α-diazoacetates and diazooxindoles was developed. A tris(2,4-di-tert-butylphenyl) phosphite derived gold complex promoted the highly chemoselective and site-selective C-H bond functionalization of phenols and N-acylanilines with gold-carbene generated from the decomposition of diazo compounds, furnishing the corresponding products in moderate to excellent yields at rt. The salient features of this reaction include readily available starting materials, unprecedented C-H functionalization rather than X-H insertion, good substrate scope, mild conditions, high efficiency, and ease in further transformation. To the best of our knowledge, this is the first example of C-H functionalization of unprotected phenols with diazo compounds.
The polymer-bound Ming-Phos was easily
prepared by the highly efficient
immobilization of our recently developed Ming-Phos in polystyrene
by copolymerization in the presence of 5% DVB, which shows good performance
in the application of heterogeneously catalyzed asymmetric cycloaddition.
A pair of enantiomers of the product with opposite configurations
could be easily delivered in high yields with excellent enantioselectivity
by the application of two diastereomers of the heterogeneous catalyst.
This heterogeneous catalyst not only exhibits similar catalytic activity
and enantioselectivity to those of the homogeneous catalyst but also
could be easily recovered and recycled for up to eight cycles.
Biometric verification is generally considered a one-to-one matching task. In contrast, in this paper, we argue that the one-to-many competitive matching via sparse representation-based classification (SRC) can bring enhanced verification security and accuracy. SRC-based verification introduces non-target subjects to construct dynamic dictionary together with the client claimed and encodes the submitted feature. Owing to the sparsity constraint, a client can only be accepted when it defeats almost all non-target classes and wins a convincing sparsity-based matching score. This will make the verification more secure than those using one-to-one matching. However, intense competition may also lead to extremely inferior genuine scores when data degeneration occurs. Motivated by the latent benefits and concerns, we study SRC-based verification using two sparsity-based matching measures, three biometric modalities (i.e., face, palmprint, and ear) and their multimodal combinations based on both handcrafted and deep learning features. We finally approach a comprehensive study of SRC-based verification, including its methodology, characteristics, merits, challenges and the directions to resolve. Extensive experimental results demonstrate the superiority of SRC-based verification, especially when using multimodal fusion and advanced deep learning features. The concerns about its efficiency in large-scale user applications can be readily solved using a simple dictionary shrinkage strategy based on cluster analysis and random selection of non-target subjects.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.