Complete mapping of the morphologies of some linear and graft fluorinated co-oligomers in an aprotic solvent by dissipative particle dynamics.
ABSTRACT The mesoscopic morphologies of linear and graft fluorinated block copolymers of ABCBA and C:BA types, respectively, have been investigated by using dissipative particle dynamics method. Self-assembly in a selective solvent has been examined by the introduction of dimethylformamide as the choice of solvent. By comparing the solubility parameters calculated using atomistic simulations, fluorine-containing segments are found to be immiscible both with other segments of the polymer and with the solvent. Morphologies of the pure linear and graft copolymers were lamellar and cylindrical, respectively. Interfacial tension versus concentration curves have been used to explain the self-assembly behavior of copolymers in solution, as well as to predict the kinetic mechanisms responsible for this behavior.
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ABSTRACT: The "asymmetric brownian ratchet model" is a variation of Feynman's ratchet and pawl system proposed. In this model, a system consisting of a motor and a rail has two binding states. One is the random brownian state, and the other is the asymmetric potential state. When the system is alternatively switched between these states, the motor can be driven in one direction. This model is believed to explain nanomotor behavior in biological systems. The feasibility of the model has been demonstrated using electrical and magnetic forces; however, switching of these forces is unlikely to be found in biological systems. In this paper, we propose an original mechanism of transition between states by bubble formation in a nanosized channel surrounded by hydrophobic atoms. This amounts to a nanoscale motor system using bubble propulsion. The motor system consists of a hydrophobic motor and a rail on which hydrophobic patterns are printed. Potential asymmetry can be produced by using a left-right asymmetric pattern shape. Hydrophobic interactions are believed to play an important role in the binding of biomolecules and molecular recognition. The bubble formation is controlled by changing the width of the channel by an atomic distance (∼0.1 nm). Therefore, the motor is potentially more efficient than systems controlled by other forces, in which a much larger change in the motor position is necessary. We have simulated the bubble-powered motor using dissipative particle dynamics and found behavior in good agreement with that of motor proteins. Energy efficiency is as high as 60%.ACS Nano 10/2010; 4(10):5905-13. · 10.77 Impact Factor
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ABSTRACT: In recent years, there is aroused interest in expressing complex systems as networks of interacting nodes. Using descriptors from graph theory, it has been possible to classify many diverse systems derived from social and physical sciences alike. In particular, folded proteins as examples of self-assembled complex molecules have also been investigated intensely using these tools. However, we need to develop additional measures to classify different systems, in order to dissect the underlying hierarchy. In this study, a general analytical relation for the dependence of nearest neighbor degree correlations on degree is derived. Dependence of local clustering on degree is shown to be the sole determining factor of assortative versus disassortative mixing in networks. The characteristics of networks constructed from spatial atomic/molecular systems exemplified by self-organized residue networks built from folded protein structures and block copolymers, atomic clusters and well-compressed polymeric melts are studied. Distributions of statistical properties of the networks are presented. For these densely-packed systems, assortative mixing in the network construction is found to apply, and conditions are derived for a simple linear dependence. Our analyses (i) reveal patterns that are common to close-packed clusters of atoms/molecules, (ii) identify the type of surface effects prominent in different close-packed systems, and (iii) associate fingerprints that may be used to classify networks with varying types of correlations.PLoS ONE 01/2010; 5(12):e15551. · 4.09 Impact Factor