3 resultados para Gaussian functions

em Biblioteca Digital da Produção Intelectual da Universidade de São Paulo


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A new method for analysis of scattering data from lamellar bilayer systems is presented. The method employs a form-free description of the cross-section structure of the bilayer and the fit is performed directly to the scattering data, introducing also a structure factor when required. The cross-section structure (electron density profile in the case of X-ray scattering) is described by a set of Gaussian functions and the technique is termed Gaussian deconvolution. The coefficients of the Gaussians are optimized using a constrained least-squares routine that induces smoothness of the electron density profile. The optimization is coupled with the point-of-inflection method for determining the optimal weight of the smoothness. With the new approach, it is possible to optimize simultaneously the form factor, structure factor and several other parameters in the model. The applicability of this method is demonstrated by using it in a study of a multilamellar system composed of lecithin bilayers, where the form factor and structure factor are obtained simultaneously, and the obtained results provided new insight into this very well known system.

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The relative amounts of amorphous and crystalline ?- and a-phases in polyamide-6 nanocomposites, estimated from the deconvolution of X-ray diffraction peaks using Gaussian functions, correlates with their mechanical, thermomechanical, and barrier properties. The incorporation of organoclay platelets (Cloisite 15A and 30B) induced the crystallization of the polymer in the ? form at expense of the amorphous phase, such that 12 wt % of Cloisite is enough to enhance the mechanical and the thermomechanical properties. However, higher nanofiller loads were necessary to achieve good barrier effects, because this property is mainly dependent on the tortuous path permeation mechanism of the gas molecules through the nanocomposite films. (C) 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012

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Spatial linear models have been applied in numerous fields such as agriculture, geoscience and environmental sciences, among many others. Spatial dependence structure modelling, using a geostatistical approach, is an indispensable tool to estimate the parameters that define this structure. However, this estimation may be greatly affected by the presence of atypical observations in the sampled data. The purpose of this paper is to use diagnostic techniques to assess the sensitivity of the maximum-likelihood estimators, covariance functions and linear predictor to small perturbations in the data and/or the spatial linear model assumptions. The methodology is illustrated with two real data sets. The results allowed us to conclude that the presence of atypical values in the sample data have a strong influence on thematic maps, changing the spatial dependence structure.