1,121 research outputs found
Dynamically Slow Processes in Supercooled Water Confined Between Hydrophobic Plates
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
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
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
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
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
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
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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