Reaction-Induced Phase Separation and Structure Formation in Polymer Blends

The peculiarities of reaction-induced phase separation and the structure formation in semi- and full interpenetrating polymer networks and in the blends of linear polymers formed in situ are analyzed. It is shown that for most of these systems phase separation proceeds viathe spinodal decomposition mechanism resulting in the formation of interconnected spatially periodic structures. The possible ways for the structure regulation of the composites produced are considered.     

Influence of noise and delay on reaction-diffusion recurrent neural networks

In this paper, the influence of the noise and delay upon the stability property of reaction-diffusion recurrent neural networks （RNNs） with the time-varying delay is discussed. The new and easily verifiable conditions to guarantee the mean value exponential stability of an equilibrium solution are derived. The rate of exponential convergence can be estimated by means of a simple computation based on these criteria.     

REINFORCEMENT OF POLYDIMETHYLSILOXANE NETWORKS BY NANOCALCIUM CARBONATE

Although a number of investigations have been devoted to the analysis of silica or carbon black filled elastomer networks, little work has been done on the reinforcement of CaCO3 filled elastomer network. In this work, the reinforcement of polydimethylsiloxane (PDMS) network by using CaCO3 nano-particles was investigated. We have found a simultaneous increase of tensile strength, modulus and elongation with the increase in nano-CaCO3 content, which suggests that nano-CaCO3 particles can indeed be used as a reinforcing agent, just like silica or carbon black. Interestingly, the tensile strength,modulus and elongation were seen to leave off for the first time when the content of nano-CaCO3 particles reaches to 80%.PDMS also showed an enhanced elastic modulus and storage modulus with the increase in nano-CaCO3 content, particularly for samples with high nano-CaCO3 content. SEM was used to investigate the dispersion of the filler in PDMS matrix. A better dispersion was found for samples with high nano-CaCO3 content. A great increase of viscosity was found for samples with higher filler content, which is considered to be the reason for the good dispersion thus the reinforcement, because high viscosity will be helpful for breaking the agglomerates of fillers into small size particles under effect of shear. Our work provides a new way for the reinforcement of elastomer by using an adequate amount of nano-CaCO3 particles instead of as mall quantity of silica, which is not only economically cheap but also very effective.     

Weave States in Loop Quantum Gravity

Weaves are eigenstates of geometrical operators in nonperturbative quantum gravity, which approximate flat space (or other smooth geometries) at large scales. We describe two such states, which diagonalize the area as well as the volume operators. The existence of such states shows that some earlier worries about the difficulty of realizing kinematical states with non-vanishing volume can be overcome. We also show that the Q operator used in earlier work for extracting geometrical information from quantum states does not capture more information than the area and volume operators.     

Filler effect on formation and properties of reinforced interpenetrating polymer networks

The formation processes of unfilled and filled interpenetrating polymer networks (IPNs) and some of their physico-mechanical properties have been investigated. The formation kinetics and constituent network curing rates determine the rate and degree of microphase separation. This in turn determines the boundary layer composition and structure. Introduction of filler into the IPN during formation affects greatly the crosslinking reaction and the microphase segregation of homopolymers. It has been shown that the degree of phase segregation in filled IPNs differs from that in unfilled ones. All the fillers were found to shorten the time of internal stress appearance and to increase its value for IPNs with predominantly high-modulus component content. Some filled IPNs were shown to have greater thermodynamic stability than unfilled ones.     

Predicting the Capacity of RC Beams Strengthened in Shear with Side-Bonded FRP Reinforcements Using Artificial Neural Networks

The application of artificial neural network (ANN) to predict the shear capacity of reinforced concrete (RC) beams retrofitted in shear by means of side-bonded fiber-reinforced polymer (FRP) is investigated in this paper. An extensive literature review has been carried out. In addition, ten shear deficient RC beams with different carbon fiber-reinforced polymer (CFRP) configurations were tested and added as data to the collected data. It was aimed to build an efficient and practical ANN model with parameters which can easily be obtained without any calculation and/or experimental investigation. The results are compared with the design guideline equations that emerge as predictions of the FRP contribution using the trained neural networks: these are in good agreement with the experimental results and better than those calculated from the theoretical guideline equations. Based on ANN results, a parametric study has been carried out to study the importance of different influencing parameters on the FRP contribution. Thereafter, a new simple expression is proposed for determining the contribution of externally bonded side-bonded FRP. Accordingly, the suggested design formula is capable of predicting the experimental FRP satisfactorily so that it can be admitted as an alternative to the existing guideline equations within the range of parameters covered in the study.     

MOLECULAR DESIGN OF NEW KINDS OF AUXETIC POLYMERS AND NETWORKS

Three new kinds of molecular networks are designed and predicted to exhibit negative Poisson ratios. Molecular mechanics calculations on these networks show that the magnitude of Poisson ratios depends on the relative flexibility of beam and arm structures. Several new kinds of auxetic polymers, whose successful synthesis should be easier than that of the corresponding auxetic networks, are then proposed. It is found that the kabob-like polymers with auxegens lying vertically on the main chain can acquire auxeticity while those with auxegens lying horizontally on the main chain cannot. Besides, a half kabob-like or pseudo-ladder polymer with auxegens linked at the intersection of the beam and the arm does show auxeticity when adopting constrictive conformers. It is, however, worthwhile noting that the origins of auxeticity still await and strongly deserve further experimental and theoretical investigations.