Microstructures of poly(vinyl acetate) studied by nuclear magnetic resonance spectroscopy

Carbon-13 NMR spectroscopy was used to investigate the microstructures of poly(vinyl acetate) prepared by solution polymerization in benzene. A series of aromatic compounds was synthesized in order to model the structures formed via chain transfer to solvent. The peaks near 126.5 and 128.5 ppm in the spectra of the polymer samples were assigned to a 1-phenyl-(2n + 1)-multi-acetoxyalkane (where n = 1, 2, 3, etc.) microstructure. The concentration of that structure obtained from NMR spectra was correlated with the concentration calculated from reported kinetic data.;Chain transfer to benzene was shown to occur by addition of the macroradical to benzene, followed by rearomatization involving loss of a hydrogen atom. No evidence was obtained for a transfer mechanism involving hydrogen abstraction from benzene, and the copolymerization of benzene with vinyl acetate also was shown to be absent. The transfer mechanism actually established accounts for the unexpectedly large transfer constant of benzene in vinyl acetate polymerization. General mechanisms are proposed for the solution polymerization of vinyl acetate in aromatic solvents

XPS Characterization of Friedel-Crafts Cross-Linked Polystyrene

The combination of a difunctional alkylating agent, either hydroxymethylbenzyl chloride or α,α′-dichloroxylene with polystyrene or high-impact polystyrene together with a Friedel-Crafts catalyst, 2-ethylhexyldiphenylphosphate, and an amine to react with hydrogen chloride has been studied by X-ray photoelectron spectroscopy. The results confirm what had been suggested from previous investigations using thermogravimetric analysis; cross-linking of the polymer occurs as the temperature is raised and the alcohol-containing alkylating agent gives a greater amount of cross-linking than does the dichloro compound

Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning

It is well believed that video captioning is a fundamental but challenging task in both computer vision and artificial intelligence fields. The prevalent approach is to map an input video to a variable-length output sentence in a sequence to sequence manner via Recurrent Neural Network (RNN). Nevertheless, the training of RNN still suffers to some degree from vanishing/exploding gradient problem, making the optimization difficult. Moreover, the inherently recurrent dependency in RNN prevents parallelization within a sequence during training and therefore limits the computations. In this paper, we present a novel design --- Temporal Deformable Convolutional Encoder-Decoder Networks (dubbed as TDConvED) that fully employ convolutions in both encoder and decoder networks for video captioning. Technically, we exploit convolutional block structures that compute intermediate states of a fixed number of inputs and stack several blocks to capture long-term relationships. The structure in encoder is further equipped with temporal deformable convolution to enable free-form deformation of temporal sampling. Our model also capitalizes on temporal attention mechanism for sentence generation. Extensive experiments are conducted on both MSVD and MSR-VTT video captioning datasets, and superior results are reported when comparing to conventional RNN-based encoder-decoder techniques. More remarkably, TDConvED increases CIDEr-D performance from 58.8% to 67.2% on MSVD.Comment: AAAI 201

Circle Feature Graphormer: Can Circle Features Stimulate Graph Transformer?

In this paper, we introduce two local graph features for missing link prediction tasks on ogbl-citation2. We define the features as Circle Features, which are borrowed from the concept of circle of friends. We propose the detailed computing formulas for the above features. Firstly, we define the first circle feature as modified swing for common graph, which comes from bipartite graph. Secondly, we define the second circle feature as bridge, which indicates the importance of two nodes for different circle of friends. In addition, we firstly propose the above features as bias to enhance graph transformer neural network, such that graph self-attention mechanism can be improved. We implement a Circled Feature aware Graph transformer (CFG) model based on SIEG network, which utilizes a double tower structure to capture both global and local structure features. Experimental results show that CFG achieves the state-of-the-art performance on dataset ogbl-citation2.Comment: 3 pages, 2 figures, 1 table, 31 references, manuscript in preparatio

Potential advantage of multiple alkali metal doped KNbO3 single crystals

Potassium niobate crystal KNbO3 (KN) is a well-known crystal for lead free piezoelectric or nonlinear optical applications. The KN crystal has been studied in both single crystal form and in thin film form which has resulted in many review articles being published. In order to exceed the KN crystal, it is important to study KN phase forming and doping effects on the K site. This article summarizes the authors\u27 study towards a multiple alkali metal doped KN crystal and related single crystals briefly from the viewpoint of crystal growth