Styrene radical polymerization was carried out in the presence of a polymerizable dithioester, benzyl 4-vinyldithiobenzoate, which possesses a dithioester group and a polymerizable double bond. Branched polystyrene was formed during the polymerization, as indicated by multimodal GPC curves of the products. The branched polystyrene contains a dithiobenzoate C(dS)S moiety at each branch point and thus can be analyzed by cleavage with amine. After cleavage, the GPC profiles became narrow. The molecular weight of the cleaved product increased linearly with monomer conversion, illustrating a living fashion of the polymerization. Solution property obtained by simultaneous online measurements of viscosity and light scattering indicates that the viscosity of the branched product decreased remarkably as compared to the linear polystyrene of equivalent molecular weight. The copolymerization behavior of styrene and benzyl 4-vinyldithiobenzoate was investigated by FT-IR monitoring during the polymerization. The results show that the latter was incorporated homogeneously into polystyrene chain. Therefore, branched polystyrene was synthesized with controlled architecture in the light of the length and narrow distribution of primary chains as well as the degree and the distribution of branching along the polymer chain.

The field of machine learning is witnessing its golden era as deep learning slowly becomes the leader in this domain. Deep learning uses multiple layers to represent the abstractions of data to build computational models. Some key enabler deep learning algorithms such as generative adversarial networks, convolutional neural networks, and model transfers have completely changed our perception of information processing. However, there exists an aperture of understanding behind this tremendously fast-paced domain, because it was never previously represented from a multiscope perspective. The lack of core understanding renders these powerful methods as black-box machines that inhibit development at a fundamental level. Moreover, deep learning has repeatedly been perceived as a silver bullet to all stumbling blocks in machine learning, which is far from the truth. This article presents a comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications. It was also undertaken to review the issues faced in deep learning such as unsupervised learning, black-box models, and online learning and to illustrate how these challenges can be transformed into prolific future research avenues.

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Magnetic field alignment of rod-coil block copolymers is shown to proceed through coupling to the diamagnetic moment of individual rod blocks. Block copolymer self-assembly then leads to alignment of the interfaces perpendicular to the field lines and long range order on a 10 nm lengthscale. This is in contrast to previously demonstrated alignment techniques, which couple to the block copolymer interfaces rather than individual molecules. Furthermore, alignment occurs without direct physical contact to samples millimeters in size.