Elaboració de membranes de fibra buida modificades amb nanopartícules metàl·liques per aplicacions catalítiques

Investigación producida a partir de una estancia en la Université Paul Sabatier, Toulouse III - CNRS, entre 2007 y 2009. Durante los últimos años la investigación centrada en nuevos materiales de tamaño nanoscòpico (nanopartículas, quantum dots, nanotubos de carbono,...) ha experimentado un crecimiento considerable debido a las especiales propiedades de los "nanoobjetos" con respecto a magnetismo, catálisis, conductividad eléctrica, etc ... Sin embargo, hoy en día todavía existen pocas aplicaciones de las nanopartículas en temas medioambientales. Uno de los motivos de esta situación es la posible toxicidad de los nanoobjetos, pero existe también una dificultad tecnológica dado que las nanopartículas tienden a agregarse y es muy difícil manipularlas sin que pierdan sus propiedades especiales. Así, aunque la preparación de materiales catalíticos nanoestructurados es muy interesante, es necesario definir nuevas estrategias para prepararlos. Este proyecto de investigación tiene como objetivo principal la preparación de nuevas membranas catalíticas con nanopartículas metálicas en el interior para aplicaciones de tratamiento de agua. La innovación principal de este proyecto consiste en que las nanopartículas no son introducidas en la matriz polimérica una vez preformadas sino que se hacen crecer en el interior de la matriz polimérica mediante una síntesis intermatricial. El único requisito es que la matriz polimérica contenga grupos funcionales capaces de interaccionar con los precursores de las nanopartículas. Una vez finalizado el proyecto se puede afirmar que se han logrado parte de los objetivos planteados inicialmente. Concreamente ha quedado demostrado que se pueden sintetizar nanopartículas metálicas de metales nobles (platino y paladio) en membranas de fibra hueca de micro- y ultrafiltración siguiendo dos metodologías diferentes: modificación fotoquímica de polímeros y deposición de multicapas de polielectrolitos. Los nuevos materiales son efectivos en la catálisis de reducción de un compuesto modelo (4-nitrofenol con borohidruro de sodio) y, en general, los resultados han sido satisfactorios. Sin embargo, se ha puesto de manifiesto que el uso de un reactivo que genera hidrógeno gas en contacto con la solución acuosa dificulta enormemente la implementación de la reacción catalítica al ser el medio de la membrana una matriz porosa. Así, como conclusión principal se puede decir que se han encontrado las limitaciones de esta aproximación y se sugieren dos posibilidades de continuidad: la utilización de las membranas sintetizadas en contactores gas-líquido o bien el estudio y optimización del sistema de membrana en configuración de membranas planas, un objetivo más asequible dada su menor complejidad. Esta investigación se ha realizado en el seno del “Laboratoire de Génie Chimique” de Toulouse y del Departamento de Química de la Michigan State University y ha sido posible gracias a un proyecto financiado por la “Agence National pour la Recherce” y al programa PERMEANT entre el CNRS y la NSF.

A Monte Carlo-Based Fiber Tracking Algorithm using Diffusion Tensor MRI

Diffusion tensor magnetic resonance imaging, which measures directional information of water diffusion in the brain, has emerged as a powerful tool for human brain studies. In this paper, we introduce a new Monte Carlo-based fiber tracking approach to estimate brain connectivity. One of the main characteristics of this approach is that all parameters of the algorithm are automatically determined at each point using the entropy of the eigenvalues of the diffusion tensor. Experimental results show the good performance of the proposed approach

Universality class of fiber bundles with strong heterogeneities

We study the effect of strong heterogeneities on the fracture of disordered materials using a fiber bundle model. The bundle is composed of two subsets of fibers, i.e. a fraction 0 ≤ α ≤ 1 of fibers is unbreakable, while the remaining 1 - α fraction is characterized by a distribution of breaking thresholds. Assuming global load sharing, we show analytically that there exists a critical fraction of the components αc which separates two qualitatively diferent regimes of the system: below αc the burst size distribution is a power law with the usual exponent Ƭ= 5/2, while above αc the exponent switches to a lower value Ƭ = 9/4 and a cutoff function occurs with a diverging characteristic size. Analyzing the macroscopic response of the system we demonstrate that the transition is conditioned to disorder distributions where the constitutive curve has a single maximum and an inflexion point defining a novel universality class of breakdown phenomena

Avalanche dynamics of fiber bundle models

We present a detailed analytical and numerical study of the avalanche distributions of the continuous damage fiber bundle model CDFBM . Linearly elastic fibers undergo a series of partial failure events which give rise to a gradual degradation of their stiffness. We show that the model reproduces a wide range of mechanical behaviors. We find that macroscopic hardening and plastic responses are characterized by avalanche distributions, which exhibit an algebraic decay with exponents between 5/2 and 2 different from those observed in mean-field fiber bundle models. We also derive analytically the phase diagram of a family of CDFBM which covers a large variety of potential avalanche size distributions. Our results provide a unified view of the statistics of breaking avalanches in fiber bundle models

Impairment of mossy fiber long-term potentiation and associative learning in pituitary adenylate cyclase activating polypeptide type I receptor-deficient Mice

The pituitary adenylate cyclase activating polypeptide (PACAP) type I receptor (PAC1) is a G-protein-coupled receptor binding the strongly conserved neuropeptide PACAP with 1000-fold higher affinity than the related peptide vasoactive intestinal peptide. PAC1-mediated signaling has been implicated in neuronal differentiation and synaptic plasticity. To gain further insight into the biological significance of PAC1-mediated signaling in vivo, we generated two different mutant mouse strains, harboring either a complete or a forebrain-specific inactivation of PAC1. Mutants from both strains show a deficit in contextual fear conditioning, a hippocampus-dependent associative learning paradigm. In sharp contrast, amygdala-dependent cued fear conditioning remains intact. Interestingly, no deficits in other hippocampus-dependent tasks modeling declarative learning such as the Morris water maze or the social transmission of food preference are observed. At the cellular level, the deficit in hippocampus-dependent associative learning is accompanied by an impairment of mossy fiber long-term potentiation (LTP). Because the hippocampal expression of PAC1 is restricted to mossy fiber terminals, we conclude that presynaptic PAC1-mediated signaling at the mossy fiber synapse is involved in both LTP and hippocampus-dependent associative learning.

Prediction of subcutaneous fatty acid composition from ham of CLA fed boars and gilts using a NIRS fiber optic probe

The aim of this work was the use of NIR technology by direct application of a fiber optic probe on back fat to analyze the fatty acid composition of CLA fed boars and gilts. 265 animals were fed 3 different diets and the fatty acid profile of back fat from Gluteus medius was analyzed using gas chromatography and FT-NIR. Spectra were acquired using a Bruker Optics Matrix-F duplex spectrometer equipped with a fiber optic probe (IN-268-2). Oleic and stearic fatty acids were predicted accurately; myristic, vaccenic and linoleic fatty acids were predicted with lower accuracy, while palmitic and α-linolenic fatty acids were poorly predicted. The relative percentage of fatty acids and NIR spectra showed differences in fatty acid composition of back fat from pigs fed CLA which increased the relative percentage of SFA and PUFA while MUFA decreased. Results suggest that a NIR fiber optic probe can be used to predict total saturated and unsaturated fatty acid composition, as well as the percentage of stearic and oleic. NIR showed potential as a rapid and easily implemented method to discriminate carcasses from animals fed different diets.

Hollow sphere as a detector of gravitational radiation

The most important features of the proposed spherical gravitational wave detectors are closely linked with their symmetry. Hollow spheres share this property with solid ones, considered in the literature so far, and constitute an interesting alternative for the realization of an omnidirectional gravitational wave detector. In this paper we address the problem of how a hollow elastic sphere interacts with an incoming gravitational wave and find an analytical solution for its normal mode spectrum and response, as well as for its energy absorption cross sections. It appears that this shape can be designed having relatively low resonance frequencies (~ 200 Hz) yet keeping a large cross section, so its frequency range overlaps with the projected large interferometers. We also apply the obtained results to discuss the performance of a hollow sphere as a detector for a variety of gravitational wave signals.

Detection of scalar gravitational waves with a hollow spherical antenna

We study the response and cross sections for the absorption of GW energy generated in a Jordan-Brans-Dicke theory by a resonant mass detector shaped as a hollow sphere. As a source of the GW we take a binary system in the Newtonian approximation. For masses of the stars of the order of the solar mass, the emitted GW sweeps a range of frequencies which include the first resonant mode of the detector.

Brittle to ductile transition in a fiber bundle with strong heterogeneity

We analyze the failure process of a two-component system with widely different fracture strength in the framework of a fiber bundle model with localized load sharing. A fraction 0≤α≤1 of the bundle is strong and it is represented by unbreakable fibers, while fibers of the weak component have randomly distributed failure strength. Computer simulations revealed that there exists a critical composition αc which separates two qualitatively different behaviors: Below the critical point, the failure of the bundle is brittle, characterized by an abrupt damage growth within the breakable part of the system. Above αc, however, the macroscopic response becomes ductile, providing stability during the entire breaking process. The transition occurs at an astonishingly low fraction of strong fibers which can have importance for applications. We show that in the ductile phase, the size distribution of breaking bursts has a power law functional form with an exponent μ=2 followed by an exponential cutoff. In the brittle phase, the power law also prevails but with a higher exponent μ=92. The transition between the two phases shows analogies to continuous phase transitions. Analyzing the microstructure of the damage, it was found that at the beginning of the fracture process cracks nucleate randomly, while later on growth and coalescence of cracks dominate, which give rise to power law distributed crack sizes.