ABSTRACT
The thermal conductivity, specific heat, and specific volume of the orientational glass former 1,1,2-trichloro-1,2,2-trifluoroethane (CCl2F-CClF2, F-113) have been measured under equilibrium pressure within the low-temperature range, showing thermodynamic anomalies at ca. 120, 72, and 20 K. The results are discussed together with those pertaining to the structurally related 1,1,2,2-tetrachloro-1,2-difluoroethane (CCl2F-CCl2F, F-112), which also shows anomalies at 130, 90, and 60 K. The rich phase behavior of these compounds can be accounted for by the interplay between several of their degrees of freedom. The arrest of the degrees of freedom corresponding to the internal molecular rotation, responsible for the existence of two energetically distinct isomers, and the overall molecular orientation, source of the characteristic orientational disorder of plastic phases, can explain the anomalies at higher and intermediate temperatures, respectively. The soft-potential model has been used as the framework to describe the thermal properties at low temperatures. We show that the low-temperature anomaly of the compounds corresponds to a secondary relaxation, which can be associated with the appearance of Umklapp processes, i.e., anharmonic phonon-phonon scattering, that dominate thermal transport in that temperature range.
ABSTRACT
The determination of the molecular ordering in a liquid is still a controversial subject. There is no general consensus either on the methods to obtain reliable liquid structures or on the way to analyze them. Regardless of the method, it is very important to have a realistic molecular structure available that allows simulations to faithfully reproduce the sample features, and that minimizes the computing time in structure refinements. However, attention is not always paid to this point and molecular models coming from general force-fields are frequently used to undertake many of the analyses. We propose in this work to use a Bayesian scheme to fit the experimental data and produce reliable molecular models that can be used as the starting point of any simulation or refinement. The algorithm behind the proposed method is based on a Markov chain Monte Carlo procedure, as many other refinement programs such as reverse Monte Carlo or empirical potential structure refinement.
ABSTRACT
Trans-1,2-dichloroethene (HClC=CClH) has several structural and dynamic anomalies between its low- and high-density liquid, previously found through neutron scattering experiments. To explain the microscopic origin of the differences found in those experiments, a series of molecular dynamics simulations were performed. The analysis of molecular short-range order shows that the number of molecules in the first neighbor shell is 12 for the high-density liquid and 11 for the low-density one. It also shows that the angular position of the center of mass of the first neighbor is roughly the same although the molecular orientation is not. In both liquids the first neighbor and its reference molecule arrange mainly in two configurations, each being the most probable in one of the liquids. First neighbors in the configuration that predominates in the high-density liquid tend to locate themselves closer to the reference molecule, an evidence that they are more strongly bonded. This arrangement facilitates a better packing of the rest of molecules in the first neighbor shell so that on average an additional molecule can be included, and is proposed to be the key in the explanation of all the observed anomalies in the characteristics of both liquids.
Subject(s)
Dichloroethylenes/chemistry , Molecular Dynamics Simulation , Water/chemistry , Hydrogen Bonding , Models, Molecular , ThermodynamicsABSTRACT
Fitting a data set with a parametrized model can be seen geometrically as finding the global minimum of the χ(2) hypersurface, depending on a set of parameters {P(i)}. This is usually done using the Levenberg-Marquardt algorithm. The main drawback of this algorithm is that despite its fast convergence, it can get stuck if the parameters are not initialized close to the final solution. We propose a modification of the Metropolis algorithm introducing a parameter step tuning that optimizes the sampling of parameter space. The ability of the parameter tuning algorithm together with simulated annealing to find the global χ(2) hypersurface minimum, jumping across χ(2){P(i)} barriers when necessary, is demonstrated with synthetic functions and with real data.
