The ability to programmatically assemble colloidal micro/nanostructures into highly ordered superstructures is of great importance in both fundamental studies and practical applications. In addition to the sophisticated manipulation of the short-range and long-range interactions imposed on the colloidal building blocks, the intrinsic shape elements including face, edge, corner, concave, convex, and curvature also play very important roles in solving the "jigsaw puzzle" of the superstructures. Here, the recent progress in the development of colloidal assembly strategies for the formation of complex superstructures is reviewed, with a primary focus on the unique effects of the morphology of the building blocks to the assembly processes and the final structures. Overall, this Review aims to shed light on the fundamental understanding of the colloidal behaviors of complex micro/nanostructures and promote the continued development of effective strategies for the creation of functional materials with complex compositions and morphologies.
Enantioselective hydrogenation of quinoline derivatives catalyzed by phosphine-free chiral cationic Cp*Ir(OTf)(CF 3TsDPEN) complex (CF 3TsDPEN = N-(p-trifluoromethylbenzenesulfonyl)-1,2-diphenylethylene-diamine) afforded the 1,2,3,4-tetrahydroquinoline derivatives in up to 99% ee. The reaction could be carried out with a substrate-to-catalyst molar ratio as high as 1000 in undegassed methanol and with no need for inert gas protection.
When nanocrystals are made to undergo chemical transformations, there are often accompanying large mechanical deformations and changes to overall particle morphology. These effects can constrain development of multistep synthetic methods through loss of well-defined particle morphology and functionality. Here, we demonstrate a surface protection strategy for solution phase chemical conversion of colloidal nanostructures that allows for preservation of overall particle morphology despite large volume changes. Specifically, via stabilization with strong coordinating capping ligands, we demonstrate the effectiveness of this method by transforming β-FeOOH nanorods into magnetic FeO nanorods, which are known to be difficult to produce directly. The surface-protected conversion strategy is believed to represent a general self-templating method for nanocrystal synthesis, as confirmed by applying it to the chemical conversion of nanostructures of other morphologies (spheres, rods, cubes, and plates) and compositions (hydroxides, oxides, and metal organic frameworks).
Fingerprint liveness detection has gradually been regarded as a primary countermeasure for protecting the fingerprint recognition systems from spoof presentation attacks. The convolutional neural networks (CNNs) have shown impressive performance and great potential in advancing the state-of-the-art of fingerprint liveness detection. However, most existing CNNs-based fingerprint liveness methods have a few shortcomings: 1) the CNN structure used on natural images does not achieve good performance on fingerprint liveness detection, which neglects the inevitable differences between natural images and fingerprint images; or 2) a relative shallow architecture (typically several layers) has not paid attention to the capability of deep network for spoof fingerprint detection. Motivated by the compelling classification accuracy and desirable convergence behaviors of the deep residual network, this paper proposes a new CNN-based fingerprint liveness detection framework to discriminate between live fingerprints and fake ones. The proposed framework is a lightweight yet powerful network structure, called Slim-ResCNN, which consists of the stack of series of improved residual blocks. The improved residual blocks are specifically designed for fingerprint liveness detection without overfitting and less processing time. The proposed approach significantly improves the performance of fingerprint liveness detection on LivDet2013 and LivDet2015 datasets. Additionally, the Slim-ResCNN wins the first prize in the Fingerprint Liveness Detection Competition 2017, with an overall accuracy of 95.25%.
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