Abstract: Polyester-grafted polyhedral oligomeric silsesquioxane (POSS) nanohybrids selectively produced by ring-opening polymerization of ε-caprolactone and L,L-lactide (A.-L. Goffin, E. Duquesne, S. Moins, M. Alexandre, Ph. Dubois, Eur. Polym. Journal, 2007, 43, 4103) were studied as masterbatches by melt-blending within their corresponding commercial polymeric matrices, i.e., poly(ε-caprolactone) (PCL) and poly(L,L-lactide) (PLA). For the sake of comparison, neat POSS nanoparticles were also dispersed in PCL and PLA. The objective was to prepare aliphatic polyester-based nanocomposites with enhanced crystallization behavior, and therefore, enhanced thermo-mechanical properties. Wide-angle X-ray scattering and transmission electron microscopy attested for the dispersion of individualized POSS nanoparticles in the resulting nanocomposite materials only when the polyester-grafted POSS nanohybrid was used as a masterbatch. The large impact of such finely dispersed (grafted) nanoparticles on the crystallization behavior for the corresponding polyester matrices was noticed, as evidenced by differential scanning calorimetry analysis. Indeed, well-dispersed POSS nanoparticles acted as efficient nucleating sites, significantly increasing the crystallinity degree of both PCL and PLA matrices. As a result, a positive impact on thermo-mechanical properties was highlighted by dynamic mechanical thermal analysis.

Abstract: Quintuple perovskites Sm2Ba3Fe5-xCoxO15-delta = 0.5, 1.0 and 1.5) have been prepared by glycerin-nitrate tech- nique in air. The phase purity was confirmed by XRD. Partial substitution of Co for Fe decreases the oxygen content and thus the mean oxidation state of 3d-metals. It also slightly decreases the thermal expansion coefficient of oxides. Positive value of the Seebeck coefficient confirmed p-type conductivity, though the thermopower decreases as the Co content increases. The temperature dependence of electrical conductivity reveals a maximum at 550-750 degrees C.

Abstract: Multiplex detection of emerging pollutants is essential to improve quality control of water treatment plants, which requires portable systems capable of real-time monitoring. In this paper we describe a flexible, dual electrochemical sensing device that detects nonylphenol and paroxetine in tap water samples. The platform contains two voltammetric sensors, with different working electrodes that were either pretreated or functionalized. Each working electrode was judiciously tailored to cover the concentration range of interest for nonylphenol and paroxetine, and square wave voltammetry was used for detection. An electrochemical pretreatment with sulfuric acid on the printed electrode enabled a selective detection of nonylphenol in 1.0-10 x 10(-6) mol L-1 range with a limit of detection of 8.0 x 10(-7) mol L-1. Paroxetine was detected in the same range with a limit of detection of 6.7 x 10(-7) mol L-1 using the printed electrode coated with a layer of carbon spherical shells. Simultaneous detection of the two analytes was achieved in tap water samples within 1 min, with no fouling and no interference effects. The long-term monitoring capability of the dual sensor was demonstrated in phosphate buffer for 45 days. This performance is statistically equivalent to that of high-performance liquid chromatography (HPLC) for water analysis. The dual-sensor platform is generic and may be extended to other water pollutants and clinical biomarkers in real-time monitoring of the environment and health conditions.

Abstract: The time-dependent Ginzburg-Landau formalism for (d + s)-wave superconductors and their representation using auxiliary fields is investigated. By using the link variable method, we then develop suitable discretization of these equations. Numerical simulations are carried out for a mesoscopic superconductor in a homogeneous perpendicular magnetic field which revealed peculiar vortex states. (C) 2014 AIP Publishing LLC.

Abstract: We report an innovative method to explore the optimal experimental settings to detect light atoms from scanning transmission electron microscopy (STEM) images. Since light elements play a key role in many technologically important materials, such as lithium-battery devices or hydrogen storage applications, much effort has been made to optimize the STEM technique in order to detect light elements. Therefore, classical performance criteria, such as contrast or signal-to-noise ratio, are often discussed hereby aiming at improvements of the direct visual interpretability. However, when images are interpreted quantitatively, one needs an alternative criterion, which we derive based on statistical detection theory. Using realistic simulations of technologically important materials, we demonstrate the benefits of the proposed method and compare the results with existing approaches.