SearcharxivSearch

arXiv · 1112.2055

Noble gases in high-pressure silicate liquids: A computer simulation study

Abstract

The test particle method has been used in conjunction with molecular dynamics simulations to evaluate the solubility of noble gases in silicate melts of various compositions. At low pressure the calculated solubility constants (the inverse of the Henry's constant) are in excellent agreement with data of the literature. In particular it is found that the solubility constant (i) decreases when the size of the noble gas increases, (ii) decreases from silica-rich to silica-poor composition of the melt, and (iii) is positively correlated with the temperature. Moreover it is shown that the solubility is governed primarily by the entropic cost of cavity formation for inserting the noble gas into the melt and secondarily by its solvation energy. Interestingly, the behaviour of these two contributions differ from each other as the entropic cost of cavity formation increases strongly with the size of the solute atom to insert whereas large atoms are better solvated than small ones. With all melt composition investigated here (silica, rhyolite, MORB and olivine), the calculated solubility curves exhibit the same qualitative behaviour with pressure; a steep rise culminating in a broad maximum followed by a gradual decrease of the solubility at higher pressure. At variance with LHDAC experiments (Chamorro et al. (1996, 1998) and Bouhifd et al. (2006, 2008)) where a Ar solubility drop is observed at about 50 kbar in silica and molten olivine and in the pressure range ~100-160 kbar with other melt composition, we do not find such a sudden change of the solubility.

Explore related subjects

Keep this discovery

BibTeXRIS

B. Guillot, N. Sator. 2011-12-09. Noble gases in high-pressure silicate liquids: A computer simulation study. https://doi.org/10.1016/j.gca.2011.11.040

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Universal sampling of spin systems across quenched disorder

Statistical physics extracts macroscopic laws by averaging over the many microscopic degrees of freedom of a system. Disordered systems demand a second and far harder average, one over the quenched randomness itself. The classic analytical routes, the replica and cavity methods, become uncontrolled outside mean-field or tree-like limits, and conventional numerical algorithms like parallel tempering require expensive, independent equilibration for every disorder realization. In this work, we introduce a universal neural variational framework that amortizes inference across the disorder ensemble, eliminating both the need for per-instance Markov chain equilibration and the cost of retraining instance-specific variational ansatzes. Built on an encoder-decoder Transformer architecture, after training once, it produces an explicit approximation to the Boltzmann distribution given previously unseen disorder realizations without further optimization. We validate this framework on 2D Edwards-Anderson models, and apply it to the random-bond Ising model, successfully capturing the Binder cumulant crossings near the Nishimori multicritical point. These results shift the object of variational inference from the single instance to the disorder ensemble, opening a route to frustrated many-body systems where instance-by-instance computation is prohibitive.

cond-mat.stat-mech

Information-Theoretic Characterization of Macroscopic Chaos Emerging from the Chemical Master Equation

Open chemical reaction networks exhibit stochastic concentration dynamics at finite system sizes, whereas their macroscopic limit is governed by deterministic rate equations that can display chaos. In this Letter, we show theoretically that a rate of information loss constructed from two-time mutual information recovers the Kolmogorov-Sinai entropy in the deterministic limit. We verify this result through numerical simulations of a Markov jump process for a three-species system involving seven reactions.

cond-mat.stat-mech

Orientational order on non-orientable domains

We study the statistical properties of passive and active many-body systems with orientational degrees of freedom on non-orientable domains. By rephrasing topological constraints as non-local symmetry relations on an orientable double-cover, we show that non-orientability eliminates global rotational soft modes without acting like an external field. In a passive XY model, this results in topological caging, where orientational fluctuations that exhibit conventional diffusive behavior on a torus saturate on a Klein bottle to a finite value that we compute exactly in the thermodynamic limit. In models of active self-propelled particles with orientational degrees of freedom, topological caging persists despite continuously changing interaction neighborhoods. In an active Ising spin model, non-orientability enforces the coexistence of ordered anti-parallel domains with vanishing global polar order, a state that is absent on orientable domains.

cond-mat.stat-mech