ABSTRACT:Contact angles of water droplet on regenerated cellulose films as an index of wettability were positively correlated with the orientation of (1-10) crystal planes and crystallinity. Because hydroxyl groups of cellulose are located at the equatorial positions of glucopyranose rings, corresponding to the surface of (1-10) crystal planes, the hydrophilicity of the (1-10) surface is expected to be very high. It is natural, therefore, that higher planar orientation of (1-10) planes and crystallinity lead to higher density of hydroxyl groups on the surface of regenerated cellulose films resulting in higher wettability. In contrast, hydrogen atoms are located at the axial positions of the glucopyranose rings, corresponding to the surface of (110) planes. Thus, the (110) surface is expected to be hydrophobic, and the surface energy obtained by computer simulations was far lower than that of the (1-10) surface. This suggests that cellulose with complementary properties, i.e., hydrophobicity, may be created by structural controls such as reversing the planar orientation from (1-

The molecular mechanism of the strain–stress behavior of the ABA triblock copolymer is studied by combining self-consistent field (SCF) calculation and molecular dynamics (MD) simulation. First, the equilibrium structure was obtained by the SCF calculation. The bridge fraction φbridge was found to be about 0.4, 0.6, and 0.8 for lamellar, cylindrical, and spherical phases, respectively. From the segment distribution calculated by the SCF, the equilibrium chain configuration was generated by the method reported previously [Aoyagi et al., Comput. Phys. Comm. 145, 267 (2002)]. The loading and unloading behavior was then studied by the MD simulation. The loading curve shows a strain-softening, and then a yielding at a strain of about 350%, where the breakup of microdomains takes place. The strain–stress curve in the second elongation-compression cycle is different from that of the first cycle. Such hysteresis effect is seen also for small elongation where the domain breakup does not take place.

Coarse-grained molecular dynamics simulation of a bead–spring polymer model has been conducted for polymer melt confined between two solid walls. The wall effect was studied by changing the distance between the walls and the wall–polymer interaction. It was observed that the polymers near the walls are compressed towards the walls: the component of the radius of gyration tensor perpendicular to the wall surfaces decreases in a layer near the walls. The thickness of this surface layer, estimated from the analysis of the static polymer structure, is about 1.0–1.5 times the radius of gyration Rg in the bulk, and is independent of the distance between the walls and the wall–polymer interaction. The relaxation time of the polymers, obtained from the autocorrelation of normal modes, increases with increasing the strength of the wall–polymer interaction and with decreasing the distance between the walls. These wall effects are observed at a distance much larger than Rg. This result is in agreement with the recent dielectric measurements of cis-polyisoprene confined between mica surfaces reported by Cho, Watanabe, and Granick [J. Chem. Phys. 110, 9688 (1999)]. The thickness of the surface layer was also estimated by the position dependence of the static and dynamic properties, and was found to agree with that estimated by the viscoelastic measurements.

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Elucidation of mesoscopic structures of molecular systems is of considerable scientific and technological interest for the development and optimization of advanced materials. Molecular dynamics simulations are a promising means of revealing macroscopic physical properties of materials from a microscopic viewpoint, but analysis of the resulting complex mesoscopic structures from microscopic information is a non-trivial and challenging task. In this study, a Machine Learning-aided Local Structure Analyzer (ML-LSA) is developed to classify the complex local mesoscopic structures of molecules that have not only simple atomistic group units but also rigid anisotropic functional groups such as mesogens. The proposed ML-LSA is applied to classifying the local structures of liquid crystal polymer (LCP) systems, which are of considerable scientific and technological interest because of their potential for sensors and soft actuators. A machine learning (ML) model is constructed from small, and thus computationally less costly, monodomain LCP trajectories. The ML model can distinguish nematic- and smectic-like monodomain structures with high accuracy. The ML-LSA is applied to large, complex quenched LCP structures, and the complex local structures are successfully classified as either nematic- or smectic-like. Furthermore, the results of the ML-LSA suggest the best order parameter for distinguishing the two mesogenic structures. Our ML model enables automatic and systematic analysis of the mesogenic structures without prior knowledge, and thus can overcome the difficulty of manually determining the specific order parameter required for the classification of complex structures.

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Aliphatic poly(carbonate) glycols (PC-glycols) with different methylene unit lengths were synthesized from