Solubility of caffeic acid in CO2 + ethanol: experimental and predicted data using Cubic Plus Association Equation of State

This work evaluated the solubility of caffeic acid (CA) in mixtures of supercritical carbon dioxide (scCO2) and ethanol at different temperatures (313, 323 and 333 K), pressures (20, 30 and 40 MPa) and concentrations of ethanol (2.2, 5.4 and 10.2 mol%). The Soave-Redlich-Kwong and Cubic Plus Association (CPA) equations of state were used to correlate the binary mixture data and to predict the ternary system data. CA solubility in ethanol is approximately 106 times higher than its solubility in pure scCO2. By using 10.2 mol% ethanol in scCO2, CA solubility increased 30,000 times at 313 K and 20 MPa. Both models provided reasonable descriptions of the experimental data for the binary systems. However, CPA-EoS can better describe the strong interactions between acid molecules and ethanol, and can predict that the addition of small amounts of ethanol to scCO2 provides a large increase in CA solubility.publishe

Niche distribution and influence of environmental parameters in marine microbial communities: a systematic review

Contains fulltext : 153506.pdf (publisher's version ) (Open Access)Associations between microorganisms occur extensively throughout Earth's oceans. Understanding how microbial communities are assembled and how the presence or absence of species is related to that of others are central goals of microbial ecology. Here, we investigate co-occurrence associations between marine prokaryotes by combining 180 new and publicly available metagenomic datasets from different oceans in a large-scale meta-analysis. A co-occurrence network was created by calculating correlation scores between the abundances of microorganisms in metagenomes. A total of 1,906 correlations amongst 297 organisms were detected, segregating them into 11 major groups that occupy distinct ecological niches. Additionally, by analyzing the oceanographic parameters measured for a selected number of sampling sites, we characterized the influence of environmental variables over each of these 11 groups. Clustering organisms into groups of taxa that have similar ecology, allowed the detection of several significant correlations that could not be observed for the taxa individually