Influence of the dispersion of Nanoclays on the cellular structure of foams based on polystyrene

In the present work blends of polystyrene (PS) with sepiolites have been produced using a melt extrusion process. The dispersion degree of the sepiolites in the PS has been analyzed by dynamic shear rheology and X-ray micro-computed tomography. Sepiolites treated with quaternary ammonium salts (O-QASEP) are better dispersed in the PS matrix than natural sepiolites (N-SEP) or sepiolites organo-modified with silane groups (O-SGSEP). A percolated network is obtained when using 6.0 wt% of O-QASEP, 8.0 wt% of N-SEP and 10.0 wt% of O-SGSEP. It has been shown that multiple extrusion processes have a negative effect on the polymer architecture. They produce a reduction in the length of the polymeric chains, and they do not lead to a better dispersion of the particles in the polymer matrix. Foams have been produced using a gas dissolution foaming process, where a strong effect of the dispersion degree on the cellular structure of the different foams was found. The effects on the cellular structure obtained by using different types of sepiolites, different contents of sepiolites and different extrusion conditions have been analyzed. The foams produced with the formulations containing O-QASEP present the lowest cell size and the most homogeneous cellular structures.     

The Berlekamp-Massey Algorithm revisited

We propose a slight modification of the Berlekamp-Massey Algorithm for obtaining the minimal polynomial of a given linearly recurrent sequence. Such a modification enables to explain it in a simpler way and to adapt it to lazy evaluation. partially supported by the Galois Theory and Explicit Methods in Arithmetic Project HPRN-CT-2000-00114 partially supported by the European Union funded project RAAG CT-2001-00271     

Fast in situ 3D nanoimaging: a new tool for dynamic characterization in materials science

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Multi-scale stacked sequential learning

Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring labels exhibit some kind of relationship. The paper main contribution is two-fold: first, we generalize the stacked sequential learning, highlighting the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions. We tested the method on two tasks: text lines classification and image pixel classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning as well as state-of-the-art conditional random fields.     

Effect of shrinkage-reducing admixtures on the properties of alkali-activated slag mortars and pastes

The effect of a shrinkage-reducing admixture (SRA) based on polypropylenglycol on the dimensional stability of waterglass-activated slag mortars was studied. The analysis also showed the effect of the admixture on pore structure of the mortars as well as on the mineralogical composition and microstructure of the alkali-activated slag pastes.The SRA reduced the shrinkage by up to 85 and 50% when the alkali-activated slag mortar specimens were cured at relative humidities of 99 and 50%, respectively. The mechanism primarily involved in shrinkage reduction is the decrease in the surface tension of pore water prompted by the admixture. The SRA also modified the pore structure - under both curing conditions - increasing the percentage of pores with diameters ranging from 1.0 to 0.1 μm. Capillary stress is much lower in these pores than in the smaller capillaries prevailing in mortars prepared without admixtures.Microstructurally, the SRA occasioned a slight increase in the proportion of Si units Q² in the CSH gel and a decrease in the percentage of Al replacing the Si in the gel structure. The admixture did not, however, modify the mineralogical composition of the pastes.Finally, the SRA admixture retarded the alkaline activation of the slag, more intensely at higher admixture dosages. While the admixture did not significantly alter the degree of reaction in pastes cured for 7 days at RH = 99%, the value of this parameter dropped by 7% in the presence of the admixture in pastes cured at 50% relative humidity.     

Bioactive compounds in wine: Resveratrol,hydroxytyrosol and melatonin: A review

Regular moderate wine consumption is often associated with reduced morbidity and mortality from a variety of chronic diseases in which inflammation is the root cause. This review is focused on three of the numerous bioactive compounds present in wine: resveratrol, hydroxytyrosol and melatonin. Resveratrol and hydroxytyrosol are polyphenols. Melatonin, recently described in wine, is an indoleamine. Their structures, concentrations in wine, bioavailability, pharmacokinetic and health promoting properties are reviewed. Resveratrol seems to be one of the most promising compounds due to its bioactivity, with wine being the main source of resveratrol in diet. Hydroxytyrosol, which its main source in diet is olive oil has been also found in both red and white wine in considerable amounts. Melatonin has been found in wine in low amounts. However, both high bioactivity and bioavailability have been attributed to it. They show antioxidant, cardioprotective, anticancer, antidiabetic, neuroprotective and antiaging activities. However, human studies are still in the initial stages and therefore further studies are needed.     

Mapping inland water carbon content with Landsat 8 data

Landsat 8 is the first Earth observation satellite with sufficient radiometric and spatial resolution to allow global mapping of lake CDOM and DOC (coloured dissolved organic matter and dissolved organic carbon, respectively) content. Landsat 8 is a multispectral sensor however, the number of potentially usable band ratios, or more sophisticated indices, is limited. In order to test the suitability of the ratio most commonly used in lake carbon content mapping, the green–red band ratio, we carried out fieldwork in Estonian and Brazilian lakes. Several atmospheric correction methods were also tested in order to use image data where the image-to-image variability due to illumination conditions would be minimal. None of the four atmospheric correction methods tested, produced reflectance spectra that matched well with in situ measured reflectance. Nevertheless, the green–red band ratio calculated from the reflectance data was in correlation with measured CDOM values. In situ data show that there is a strong correlation between CDOM and DOC concentrations in Estonian and Brazilian lakes. Thus, mapping the global CDOM and DOC content from Landsat 8 is plausible but more data from different parts of the world are needed before decisions can be made about the accuracy of such global estimation.