Computation of thermoacoustical and molecular properties of n-hexane, n-heptane, n-dodecane, cyclohexane and toluene at different temperatures

From the experimental values of density and sound speed of five pure organic liquids (n-hexane, n- heptane, n-dodecane, cyclohexane and toluene), radius (r), van der Waals constants (a and b), molecular dimension (d) and nonlinearity parameters B/A were computed at five different temperatures ranging from 283.15 K to 333.15 K. Four different methods (Hartmann, Ballou rule, Johnson et al and Tong-Dong method) were used to compute to the values of B/A. The experimental data of density and sound speed were taken from the study of Romani et al. All the calculated properties were found to vary with temperature.

A molecular signature for the metabolic syndrome by urine metabolomics

Background Metabolic syndrome (MetS) is a multimorbid long-term condition without consensual medical definition and a diagnostic based on compatible symptomatology. Here we have investigated the molecular signature of MetS in urine. Methods We used NMR-based metabolomics to investigate a European cohort including urine samples from 11,754 individuals (18–75 years old, 41% females), designed to populate all the intermediate conditions in MetS, from subjects without any risk factor up to individuals with developed MetS (4–5%, depending on the definition). A set of quantified metabolites were integrated from the urine spectra to obtain metabolic models (one for each definition), to discriminate between individuals with MetS. Results MetS progression produces a continuous and monotonic variation of the urine metabolome, characterized by up- or down-regulation of the pertinent metabolites (17 in total, including glucose, lipids, aromatic amino acids, salicyluric acid, maltitol, trimethylamine N-oxide, and p-cresol sulfate) with some of the metabolites associated to MetS for the first time. This metabolic signature, based solely on information extracted from the urine spectrum, adds a molecular dimension to MetS definition and it was used to generate models that can identify subjects with MetS (AUROC values between 0.83 and 0.87). This signature is particularly suitable to add meaning to the conditions that are in the interface between healthy subjects and MetS patients. Aging and non-alcoholic fatty liver disease are also risk factors that may enhance MetS probability, but they do not directly interfere with the metabolic discrimination of the syndrome. Conclusions Urine metabolomics, studied by NMR spectroscopy, unravelled a set of metabolites that concomitantly evolve with MetS progression, that were used to derive and validate a molecular definition of MetS and to discriminate the conditions that are in the interface between healthy individuals and the metabolic syndrome.

A molecular signature for the Metabolic Syndrome by urine metabolomics

BACKGROUND. Metabolic syndrome (MetS) is a multimorbid long-term condition without consensual medical definition and a diagnostic based on compatible symptomatology. Here we have investigated the molecular signature of MetS in urine.METHODS. We used NMR-based metabolomics to investigate a European cohort including urine samples from 11,754 individuals (18–75 years old, 41% females), designed to populate all the intermediate conditions in MetS, from subjects without any risk factor up to individuals with developed MetS (4–5%, depending on the definition). A set of quantified metabolites were integrated from the urine spectra to obtain metabolic models (one for each definition), to discriminate between individuals with MetS.RESULTS. MetS progression produces a continuous and monotonic variation of the urine metabolome, characterized by up- or down-regulation of the pertinent metabolites (19 in total, including glucose, lipids, aromatic amino acids, salicyluric acid, maltitol, trimethylamine N-oxide, and p-cresol sulfate) with some of the metabolites associated to MetS for the first time. This metabolic signature, based solely on information extracted from the urine spectrum, adds a molecular dimension to MetS definition and it was used to generate models that can identify subjects with MetS (AUROC values between 0.86 and 0.92). This signature is particularly suitable to add meaning to the conditions that are in the interface between healthy subjects and MetS patients. Aging and non-alcoholic fatty liver disease are also risk factors that may enhance MetS probability, but they do not directly interfere with the metabolic discrimination of the syndrome.CONCLUSIONS. Urine metabolomics, studied by NMR spectroscopy, unravelled a set of metabolites that concomitantly evolve with MetS progression, that were used to derive and validate a molecular definition of MetS and to discriminate the conditions that are in the interface between healthy individuals and the metabolic syndrome.

Relationship between biochars’ porosity and adsorption of three neutral herbicides from water

The porous biochars have exhibited good adsorption to many organic pollutants, but the relationship between biochars’ porosity and their adsorption capacity is not clear at the moment. In this work, six biochars were produced from different feedstocks and under different pyrolysis conditions, and used for adsorption of three neutral herbicides from water. The results demonstrated that the adsorption capacity was dominated by the mesopore (1.7–50 nm) area of biochars, instead of their total surface area, according to the analysis of surface area-normalized adsorption data with both Langmuir model and a mixed adsorption and partition model. The results implied the inaccessibility of most micropores in biochars to the organic molecules with nano-scale molecular dimension, and alkalis in feedstock and an oxygen-containing atmosphere in heat treatment for producing biochars would favor the development of mesopores.