1,096 research outputs found

    Dynamically Slow Processes in Supercooled Water Confined Between Hydrophobic Plates

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    We study the dynamics of water confined between hydrophobic flat surfaces at low temperature. At different pressures, we observe different behaviors that we understand in terms of the hydrogen bonds dynamics. At high pressure, the formation of the open structure of the hydrogen bond network is inhibited and the surfaces can be rapidly dehydrated by decreasing the temperature. At lower pressure the rapid ordering of the hydrogen bonds generates heterogeneities that are responsible for strong non-exponential behavior of the correlation function, but with no strong increase of the correlation time. At very low pressures, the gradual formation of the hydrogen bond network is responsible for the large increase of the correlation time and, eventually, the dynamical arrest of the system and of the dehydration process.Comment: 14 pages, 3 figure

    Invaded Cluster Dynamics for Frustrated Models

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    The Invaded Cluster (IC) dynamics introduced by Machta et al. [Phys. Rev. Lett. 75 2792 (1995)] is extended to the fully frustrated Ising model on a square lattice. The properties of the dynamics which exhibits numerical evidence of self-organized criticality are studied. The fluctuations in the IC dynamics are shown to be intrinsic of the algorithm and the fluctuation-dissipation theorem is no more valid. The relaxation time is found very short and does not present critical size dependence.Comment: notes and refernences added, some minor changes in text and fig.3,5,7 16 pages, Latex, 8 EPS figures, submitted to Phys. Rev.

    Hydrogen-Bonded Liquids: Effects of Correlations of Orientational Degrees of Freedom

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    We improve a lattice model of water introduced by Sastry, Debenedetti, Sciortino, and Stanley to give insight on experimental thermodynamic anomalies in supercooled phase, taking into account the correlations between intra-molecular orientational degrees of freedom. The original Sastry et al. model including energetic, entropic and volumic effect of the orientation-dependent hydrogen bonds (HBs), captures qualitatively the experimental water behavior, but it ignores the geometrical correlation between HBs. Our mean-field calculation shows that adding these correlations gives a more water-like phase diagram than previously shown, with the appearance of a solid phase and first-order liquid-solid and gas-solid phase transitions. Further investigation is necessary to be able to use this model to characterize the thermodynamic properties of the supercooled region.Comment: 7 pages latex, 3 figures EP

    Effect of hydrogen bond cooperativity on the behavior of water

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    Four scenarios have been proposed for the low--temperature phase behavior of liquid water, each predicting different thermodynamics. The physical mechanism which leads to each is debated. Moreover, it is still unclear which of the scenarios best describes water, as there is no definitive experimental test. Here we address both open issues within the framework of a microscopic cell model by performing a study combining mean field calculations and Monte Carlo simulations. We show that a common physical mechanism underlies each of the four scenarios, and that two key physical quantities determine which of the four scenarios describes water: (i) the strength of the directional component of the hydrogen bond and (ii) the strength of the cooperative component of the hydrogen bond. The four scenarios may be mapped in the space of these two quantities. We argue that our conclusions are model-independent. Using estimates from experimental data for H bond properties the model predicts that the low-temperature phase diagram of water exhibits a liquid--liquid critical point at positive pressure.Comment: 18 pages, 3 figure

    Cluster Monte Carlo and numerical mean field analysis for the water liquid--liquid phase transition

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    By the Wolff's cluster Monte Carlo simulations and numerical minimization within a mean field approach, we study the low temperature phase diagram of water, adopting a cell model that reproduces the known properties of water in its fluid phases. Both methods allows us to study the water thermodynamic behavior at temperatures where other numerical approaches --both Monte Carlo and molecular dynamics-- are seriously hampered by the large increase of the correlation times. The cluster algorithm also allows us to emphasize that the liquid--liquid phase transition corresponds to the percolation transition of tetrahedrally ordered water molecules.Comment: 6 pages, 3 figure

    More than one dynamic crossover in protein hydration water

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    Studies of liquid water in its supercooled region have led to many insights into the structure and behavior of water. While bulk water freezes at its homogeneous nucleation temperature of approximately 235 K, for protein hydration water, the binding of water molecules to the protein avoids crystallization. Here we study the dynamics of the hydrogen bond (HB) network of a percolating layer of water molecules, comparing measurements of a hydrated globular protein with the results of a coarse-grained model that has been shown to successfully reproduce the properties of hydration water. With dielectric spectroscopy we measure the temperature dependence of the relaxation time of protons charge fluctuations. These fluctuations are associated to the dynamics of the HB network of water molecules adsorbed on the protein surface. With Monte Carlo (MC) simulations and mean--field (MF) calculations we study the dynamics and thermodynamics of the model. In both experimental and model analyses we find two dynamic crossovers: (i) one at about 252 K, and (ii) one at about 181 K. The agreement of the experiments with the model allows us to relate the two crossovers to the presence of two specific heat maxima at ambient pressure. The first is due to fluctuations in the HB formation, and the second, at lower temperature, is due to the cooperative reordering of the HB network

    Risk assessment of atmospheric emissions using machine learning

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    Supervised and unsupervised machine learning algorithms are used to perform statistical and logical analysis of several transport and dispersion model runs which simulate emissions from a fixed source under different atmospheric conditions. <br><br> First, a clustering algorithm is used to automatically group the results of different transport and dispersion simulations according to specific cloud characteristics. Then, a symbolic classification algorithm is employed to find complex non-linear relationships between the meteorological input conditions and each cluster of clouds. The patterns discovered are provided in the form of probabilistic measures of contamination, thus suitable for result interpretation and dissemination. <br><br> The learned patterns can be used for quick assessment of the areas at risk and of the fate of potentially hazardous contaminants released in the atmosphere
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