Scaling properties of coexistence curves for polymer solutions in terms of different order parameters

1989 ◽  
Vol 50 (12) ◽  
pp. 1573-1579 ◽  
Author(s):  
Yu. B. Mel'nichenko ◽  
V.V. Klepko
2007 ◽  
Vol 11 (08) ◽  
pp. 577-585 ◽  
Author(s):  
Refael Minnes ◽  
Hana Weitman ◽  
Benjamin Ehrenberg

In this study we investigated, spectroscopically, the binding of hematoporphyrin (HP) to non-charged lipid vesicles as a function of temperature and the molecular structure of the phospholipid. The temperature dependence of partitioning was employed to evaluate the thermodynamic parameters of the process. We studied the binding of HP to liposomes composed of different phospholipids: natural lecithin and three chemically defined phosphatidylcholines: dimiristoyl-phosphatidylcholine (DMPC), 1-palmitoyl-2-myristoyl-phosphatidylcholine (PMPC) and 1-stearoyl-2-myristoyl-phosphatidylcholine (SMPC), at different temperatures. The last three lipids differ only in the length of the fatty acid on 1 position of the glycerol backbone. Consequently, they have different phase transition temperatures and different order parameters. For SMPC, PMPC and DMPC, we checked the effect of temperatures above and below the phase transition while for lecithin, whose phase transition temperature is well below 0 °C, only temperatures above the phase transition could be tested. A very distinct effect of the phase transition on the binding constant was observed. Below this temperature a dramatic decrease in the binding was observed as the temperature was increased. Above the phase transition, the effect of temperature declined and the changes were minor compared to the changes observed when the bilayers undergo the solid-gel phase transition. Differences in HP binding to the various bilayers were attributed to the differences in the order parameters of DMPC, PMPC, SMPC and lecithin bilayers.


Author(s):  
Shuai Liang ◽  
Kyle Wm. Hall ◽  
Aatto Laaksonen ◽  
Zhengcai Zhang ◽  
Peter G. Kusalik

Crystallization in liquids is critical to a range of important processes occurring in physics, chemistry and life sciences. In this article, we review our efforts towards understanding the crystallization mechanisms, where we focus on theoretical modelling and molecular simulations applied to ice and gas hydrate systems. We discuss the order parameters used to characterize molecular ordering processes and how different order parameters offer different perspectives of the underlying mechanisms of crystallization. With extensive simulations of water and gas hydrate systems, we have revealed unexpected defective structures and demonstrated their important roles in crystallization processes. Nucleation of gas hydrates can in most cases be characterized to take place in a two-step mechanism where the nucleation occurs via intermediate metastable precursors, which gradually reorganizes to a stable crystalline phase. We have examined the potential energy landscapes explored by systems during nucleation, and have shown that these landscapes are rugged and funnel-shaped. These insights provide a new framework for understanding nucleation phenomena that has not been addressed in classical nucleation theory. This article is part of the theme issue ‘The physics and chemistry of ice: scaffolding across scales, from the viability of life to the formation of planets’.


2009 ◽  
Vol 33 (6_2) ◽  
pp. 447-450 ◽  
Author(s):  
Y. Shiratsuchi ◽  
M. Yamashita ◽  
R. Nakatani ◽  
H. Nomura ◽  
M. Yamamoto

2007 ◽  
Vol 44 (02) ◽  
pp. 393-408 ◽  
Author(s):  
Allan Sly

Multifractional Brownian motion is a Gaussian process which has changing scaling properties generated by varying the local Hölder exponent. We show that multifractional Brownian motion is very sensitive to changes in the selected Hölder exponent and has extreme changes in magnitude. We suggest an alternative stochastic process, called integrated fractional white noise, which retains the important local properties but avoids the undesirable oscillations in magnitude. We also show how the Hölder exponent can be estimated locally from discrete data in this model.


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