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Activity coefficients of binary methanol alcohol mixtures from cluster weighting.

ABSTRACT: The hydrogen bond network of different small alcohols is investigated via cluster analysis. Methanol/alcohol mixtures are studied with increasing chain length and branching of the molecule. Those changes can play an important role in different fields, including solvent and metal extraction. The extended tight binding method GFN2-xTB allows the evaluation and geometry optimization of thousands of clusters built via a genetic algorithm. Interaction energies and geometries are evaluated and discussed for the neat systems. Thermodynamic properties, such as vaporization enthalpies and activity coefficients, are calculated with the binary quantum cluster equilibrium (bQCE) approach using our in-house code peacemaker 2.8. Combined distribution functions of the distances against the angles of the hydrogen bonds are evaluated for neat and mixed clusters and weighted by the equilibrium populations achieved from bQCE calculations.

SUBMITTER: Marchelli G

PROVIDER: S-EPMC7377217 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Activity coefficients of binary methanol alcohol mixtures from cluster weighting.

Marchelli Gwydyon G Ingenmey J J Kirchner B B

ChemistryOpen 20200723 7

The hydrogen bond network of different small alcohols is investigated via cluster analysis. Methanol/alcohol mixtures are studied with increasing chain length and branching of the molecule. Those changes can play an important role in different fields, including solvent and metal extraction. The extended tight binding method GFN2-xTB allows the evaluation and geometry optimization of thousands of clusters built via a genetic algorithm. Interaction energies and geometries are evaluated and discuss ...[more]

PMID: 32714740

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Project description:Background/aimsGeneralized estimating equations are commonly used to fit logistic regression models to clustered binary data from cluster randomized trials. A commonly used correlation structure assumes that the intracluster correlation coefficient does not vary by treatment arm or other covariates, but the consequences of this assumption are understudied. We aim to evaluate the effect of allowing variation of the intracluster correlation coefficient by treatment or other covariates on the efficiency of analysis and show how to account for such variation in sample size calculations.MethodsWe develop formulae for the asymptotic variance of the estimated difference in outcome between treatment arms obtained when the true exchangeable correlation structure depends on the treatment arm and the working correlation structure used in the generalized estimating equations analysis is: (i) correctly specified, (ii) independent, or (iii) exchangeable with no dependence on treatment arm. These formulae require a known distribution of cluster sizes; we also develop simplifications for the case when cluster sizes do not vary and approximations that can be used when the first two moments of the cluster size distribution are known. We then extend the results to settings with adjustment for a second binary cluster-level covariate. We provide formulae to calculate the required sample size for cluster randomized trials using these variances.ResultsWe show that the asymptotic variance of the estimated difference in outcome between treatment arms using these three working correlation structures is the same if all clusters have the same size, and this asymptotic variance is approximately the same when intracluster correlation coefficient values are small. We illustrate these results using data from a recent cluster randomized trial for infectious disease prevention in which the clusters are groups of households and modest in size (mean 9.6 individuals), with intracluster correlation coefficient values of 0.078 in the control arm and 0.057 in an intervention arm. In this application, we found a negligible difference between the variances calculated using structures (i) and (iii) and only a small increase (typically <5%) for the independent correlation structure (ii), and hence minimal effect on power or sample size requirements. The impact may be larger in other applications if there is greater variation in the ICC between treatment arms or with an additional covariate.ConclusionThe common approach of fitting generalized estimating equations with an exchangeable working correlation structure with a common intracluster correlation coefficient across arms likely does not substantially reduce the power or efficiency of the analysis in the setting of a large number of small or modest-sized clusters, even if the intracluster correlation coefficient varies by treatment arm. Our formulae, however, allow formal evaluation of this and may identify situations in which variation in intracluster correlation coefficient by treatment arm or another binary covariate may have a more substantial impact on power and hence sample size requirements.

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.

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Activity coefficients of binary methanol alcohol mixtures from cluster weighting.

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Activity coefficients of binary methanol alcohol mixtures from cluster weighting.

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