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Excess-entropy scaling in supercooled binary mixtures.

ABSTRACT: Transport coefficients, such as viscosity or diffusion coefficient, show significant dependence on density or temperature near the glass transition. Although several theories have been proposed for explaining this dynamical slowdown, the origin remains to date elusive. We apply here an excess-entropy scaling strategy using molecular dynamics computer simulations and find a quasiuniversal, almost composition-independent, relation for binary mixtures, extending eight orders of magnitude in viscosity or diffusion coefficient. Metallic alloys are also well captured by this relation. The excess-entropy scaling predicts a quasiuniversal breakdown of the Stokes-Einstein relation between viscosity and diffusion coefficient in the supercooled regime. Additionally, we find evidence that quasiuniversality extends beyond binary mixtures, and that the origin is difficult to explain using existing arguments for single-component quasiuniversality.

SUBMITTER: Bell IH

PROVIDER: S-EPMC7453028 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Excess-entropy scaling in supercooled binary mixtures.

Bell Ian H IH Dyre Jeppe C JC Ingebrigtsen Trond S TS

Nature communications 20200827 1

Transport coefficients, such as viscosity or diffusion coefficient, show significant dependence on density or temperature near the glass transition. Although several theories have been proposed for explaining this dynamical slowdown, the origin remains to date elusive. We apply here an excess-entropy scaling strategy using molecular dynamics computer simulations and find a quasiuniversal, almost composition-independent, relation for binary mixtures, extending eight orders of magnitude in viscosi ...[more]

PMID: 32855393

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Project description:The recent development of a custom cDNA microarray platform for one of thé standard organisms in aquatic toxicology, Daphnia magna, opened up new ways to mechanistic insights of toxicological responses. In this study, gene expression and (sub)organismal responses (Cellular Energy Allocation, growth) were assayed after short-term waterborne metal exposure. Microarray analysis of Ni-exposed daphnids revealed several affected functional gene classes, of which the largest ones were involved in different metabolic processes (mainly protein and chitin related processes), cuticula turnover, transport and signal transduction. Furthermore, genes involved in oxygen transport and heme metabolism (hemoglobin, δ-aminolevilunate synthase) were down-regulated. Applying a Partial Least Squares regression on nickel fingerprints and biochemical (sub)organismal parameters revealed a set of co-varying genes (hemoglobin, RNA terminal phosphate cyclase, a ribosomal protein and an “unknown” gene fragment). An inverse relationship was seen between the mRNA expression levels of different cuticula proteins and available energy reserves. In addition to the nickel exposure, daphnids were exposed to binary mixtures of nickel and cadmium or nickel and lead. Using multivariate analysis techniques, the mixture gene expression fingerprints (Ni2++Cd2+, Ni2++Pb2+) were compared to those of the single metal treatments (Ni2+, Cd2+, Pb2+). It was hypothesized that the molecular fingerprints of the mixtures would be additive combinations of the gene expression profiles of the individual compounds present in the mixture. However, our results clearly showed additionally affected pathways after mixture treatment (e.g. additional affected genes involved in carbohydrate catabolic processes and proteolysis), indicating interactive molecular responses which are not merely the additive sum of the individual metals. These findings, although indicative of the complex nature of mixture toxicity evaluation, underline the potential of a toxicogenomics approach in gaining more mechanistic information on the effects of single compounds and mixtures.

Dataset Information

Excess-entropy scaling in supercooled binary mixtures.

Publications

Excess-entropy scaling in supercooled binary mixtures.

Similar Datasets

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