Structure and properties of clay nano‐biocomposites based on poly(lactic acid) plasticized with polyadipates

The use of nano‐biocomposites based on plasticized poly(lactic acid) (PLA) has been proposed as a way to improve the polymer ductility and to expand PLA applications window. Novative nano‐biocomposites were elaborated with PLA plasticized by polyadipates (15 wt%) with different molar masses (from 1500 to 2500 Da), with 2.1 wt% of an organo‐modified montmorillonite (O‐MMT). These materials showed enhanced ductility and barrier properties. The clay was swelled in liquid polyadipates prior to their blending with PLA to facilitate chains intercalation and nanofiller exfoliation during melt‐blending. In certain processing conditions, quite homogenous and exfoliated structures were obtained, as shown by X‐ray diffraction (XRD) and transmission electronic microscopy (TEM) results. Irrespective of the average molar mass of the polyadipate, the clay addition induced a reduction in around 25% in oxygen transmission rate (OTR) without an important detriment in tensile properties. Nano‐biocomposites prepared with higher molar masses polyadipates showed the highest thermal stability as well as the lowest OTR, resulting in very promising and novative materials for different applications such as soft packaging. Copyright © 2010 John Wiley & Sons, Ltd.     

Enhanced imaging of magnetic structures in micropatterned arrays of Co dots and antidots

A specific technique of numerical treatment of atomic force microscopy (AFM) and magnetic force microscopy (MFM) signal has been developed to enhance the quality of raw images, in order both to improve their contrast and to gain better insight on the sample topography and on the local arrangement of the magnetisation vector. Basically, the technique consists in computing the optimum conformal transformation that allows one to superimpose two AFM images of the same area, acquired performing subsequent scans whose fast scan axis were mutually perpendicular, and applying the inverse transform to the second image. After MFM image superposition, the two datasets were either summed or subtracted, in order to improve the magnetic contrast. Computations have been done in a Matlab^® workspace with the help of Image Processing Toolbox 4.2. Improved MFM images obtained on both dots and antidots thin evaporated Co arrays in the demagnetised state (after performing alternate field demagnetisation parallel and perpendicular to the array plane) have been interpreted. Samples consisting of large-size patterns (1×1 mm) of circular dots/antidots with square/hexagonal lattices and minimum diameters of 1 μm were prepared by optical lithography. The magnetic film thickness was chosen depending on resist thickness, and varied between 25 and 150 nm, with a fixed ratio 1:4 between metal/resist film thickness. MFM was exploited to obtain images of either intra-dot or inter-antidot magnetic structures.     

Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning

Machine learning and pattern recognition techniques are being increasingly employed in functional magnetic resonance imaging (fMRI) data analysis. By taking into account the full spatial pattern of brain activity measured simultaneously at many locations, these methods allow detecting subtle, non-strictly localized effects that may remain invisible to the conventional analysis with univariate statistical methods. In typical fMRI applications, pattern recognition algorithms "learn" a functional relationship between brain response patterns and a perceptual, cognitive or behavioral state of a subject expressed in terms of a label, which may assume discrete (classification) or continuous (regression) values. This learned functional relationship is then used to predict the unseen labels from a new data set ("brain reading"). In this article, we describe the mathematical foundations of machine learning applications in fMRI. We focus on two methods, support vector machines and relevance vector machines, which are respectively suited for the classification and regression of fMRI patterns. Furthermore, by means of several examples and applications, we illustrate and discuss the methodological challenges of using machine learning algorithms in the context of fMRI data analysis.     

Zinc oxide nanostructures grown by pulsed laser deposition

We report the use of PLD to grow different ZnO nanostructures. Very different film morphologies have been observed using different laser wavelengths to ablate the target. The influence of substrate temperature and oxygen background pressure on the film morphology has been investigated too. Smooth and rough films, hexagonal pyramids and columns have been obtained by using a KrF excimer laser (248 nm) for the target ablation, while hexagonal hierarchical structures and pencils have been obtained by using ArF (193 nm). Photoluminescence and X-ray diffraction measurements revealed the good quality of the samples, in particular of those deposited using the ArF laser beam.     

Product decompositions of depolarizing Mueller matrices with negative determinants

We show that the product decomposition of a depolarizing Mueller matrix (S.-Y. Lu, R.A. Chipman, J. Opt. Soc. Am. A 13 (1996) 1106) as well as the recently proposed reverse decomposition (R. Ossikovski, A. De Martino, Opt. Lett. 32 (2007) 689) need to be extended in order to account for Mueller matrices with negative determinants. The necessity of such an extension of the formalism is illustrated on experimentally determined Mueller matrices. The procedure of the modified decomposition formalism is explicitly described.     

Multimodal imaging: an evaluation of univariate and multivariate methods for simultaneous EEG/fMRI

The combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) has been proposed as a tool to study brain dynamics with both high temporal and high spatial resolution. Multimodal imaging techniques rely on the assumption of a common neuronal source for the different recorded signals. In order to maximally exploit the combination of these techniques, one needs to understand the coupling (i.e., the relation) between electroencephalographic (EEG) and fMRI blood oxygen level-dependent (BOLD) signals.