Enhancing the Separation Performance of Glassy PPO with the Addition of a Molecular Sieve (ZIF-8): Gas Transport at Various Temperatures.

In this study, we prepared and characterized composite films formed by amorphous poly(2,6-dimethyl-1,4-phenylene oxide) (PPO) and particles of the size-selective Zeolitic Imidazolate Framework 8 (ZIF-8). The aim was to increase the permselectivity properties of pure PPO using readily available materials to enable the possibility to scale-up the technology developed in this work. The preparation protocol established allowed robust membranes with filler loadings as high as 45 wt% to be obtained. The thermal, morphological, and structural properties of the membranes were analyzed via DSC, SEM, TGA, and densitometry. The gas permeability and diffusivity of He, CO2, CH4, and N2 were measured at 35, 50, and 65 °C. The inclusion of ZIF-8 led to a remarkable increase of the gas permeability for all gases, and to a significant decrease of the activation energy of diffusion and permeation. The permeability increased up to +800% at 45 wt% of filler, reaching values of 621 Barrer for He and 449 for CO2 at 35 °C. The ideal size selectivity of the PPO membrane also increased, albeit to a lower extent, and the maximum was reached at a filler loading of 35 wt% (1.5 for He/CO2, 18 for CO2/N2, 17 for CO2/CH4, 27 for He/N2, and 24 for He/CH4). The density of the composite materials followed an additive behavior based on the pure values of PPO and ZIF-8, which indicates good adhesion between the two phases. The permeability and He/CO2 selectivity increased with temperature, which indicates that applications at higher temperatures than those inspected should be encouraged.

The urinary 1 H-NMR metabolomics profile of an italian autistic children population and their unaffected siblings.

Autism spectrum disorders (ASD) make a dishomogeneous group of psychiatric diseases having either genetic and environmental components, including changes of the microbiota. The rate of diagnosis, based on a series of psychological tests and observed behavior, dramatically increased in the past few decades. Currently, no biological markers are available and the pathogenesis is not defined. The purpose of this study was to evaluate the potential use of 1 H-NMR metabolomics to analyze the global biochemical signature of ASD patients (n = 21) and controls (n = 21), these being siblings of autistic patients. A multivariate model has been used to extrapolate the variables of importance. The discriminating urinary metabolites were identified; in particular, significantly increased levels of hippurate, glycine, creatine, tryptophan, and d-threitol and decreased concentrations of glutamate, creatinine, lactate, valine, betaine, and taurine were observed in ASD patients. Based on the identified discriminant metabolites, the attention was focused on two possible mechanisms that could be involved in ASD: oxidative stress conditions and gut microflora modifications. In conclusion, nuclear magnetic resonance-based metabolomics analysis of the urine seems to have the potential for the identification of a metabolic fingerprint of ASD phenotypes and appears to be suitable for further investigation of the disease mechanisms. Autism Res 2017. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. Autism Res 2017, 10: 1058-1066. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

Clinical metabolomics and nutrition: the new frontier in neonatology and pediatrics.

In the pediatric clinic, nutritional research is focusing more and more on preventing the development of long-term diseases as well as supporting the repair processes important in the therapy of already fully developed diseases. Most children who are hospitalized or affected by chronic diseases could benefit from specific and careful attention to nutrition. Indeed, the state of nutrition modulates all body functions, including the different metabolic processes which, all together, have a profound effect on the development of the health and future of all individuals. Inappropriate food, even in the first periods of life, can accelerate the development of chronic metabolic diseases, especially in the pediatric age. To gain further insights into metabolic cycles and how they are connected with diet and health, nutrition and metabolomics interact to develop and apply modern technologies for metabolic assessment. In particular, nutritionists are evaluating the metabolomic approach to establish the single nutritional phenotypes, that is, the way in which diet interacts with individuals' metabolisms. This strategy offers the possibility of providing a complete definition of the individual's nutritional and health status, predict the risk of disease, and create metabolomic databases supporting the development of "personalized nutrition," in which diet is attuned to the nutritional needs of individual patients.

Prediction of physical-chemical properties of crude oils by 1H NMR analysis of neat samples and chemometrics.

In this work, we report the feasibility study to predict the properties of neat crude oil samples from 300-MHz NMR spectral data and partial least squares (PLS) regression models. The study was carried out on 64 crude oil samples obtained from 28 different extraction fields and aims at developing a rapid and reliable method for characterizing the crude oil in a fast and cost-effective way. The main properties generally employed for evaluating crudes' quality and behavior during refining were measured and used for calibration and testing of the PLS models. Among these, the UOP characterization factor K (K(UOP)) used to classify crude oils in terms of composition, density (D), total acidity number (TAN), sulfur content (S), and true boiling point (TBP) distillation yields were investigated. Test set validation with an independent set of data was used to evaluate model performance on the basis of standard error of prediction (SEP) statistics. Model performances are particularly good for K(UOP) factor, TAN, and TPB distillation yields, whose standard error of calibration and SEP values match the analytical method precision, while the results obtained for D and S are less accurate but still useful for predictions. Furthermore, a strategy that reduces spectral data preprocessing and sample preparation procedures has been adopted. The models developed with such an ample crude oil set demonstrate that this methodology can be applied with success to modern refining process requirements.