Mechanical and thermal behaviour of isotactic polypropylene reinforced with inorganic fullerene-like WS2 nanoparticles: Effect of filler loading and temperature

The thermal and mechanical behaviour of isotactic polypropylene (iPP) nanocomposites reinforced with different loadings of inorganic fullerene-like tungsten disulfide (IF-WS2) nanoparticles was investigated. The IF-WS2 noticeably enhanced the polymer stiffness and strength, ascribed to their uniform dispersion, the formation of a large nanoparticle?matrix interface combined with a nucleating effect on iPP crystallization. Their reinforcement effect was more pronounced at high temperatures. However, a drop in ductility and toughness was found at higher IF-WS2 concentrations. The tensile behaviour of the nanocomposites was extremely sensitive to the strain rate and temperature, and their yield strength was properly described by the Eyring s equation. The activation energy increased while the activation volume decreased with increasing nanoparticle loading, indicating a reduction in polymer chain motion. The nanoparticles improved the thermomechanical properties of iPP: raised the glass transition and heat deflection temperatures while decreased the coefficient of thermal expansion. The nanocomposites also displayed superior flame retardancy with longer ignition time and reduced peak heat release rate. Further, a gradual rise in thermal conductivity was found with increasing IF-WS2 loading both in the glassy and rubbery states. The results presented herein highlight the benefits and high potential of using IF-nanoparticles for enhancing the thermomechanical properties of thermoplastic polymers compared to other nanoscale fillers.

Study of the effect of four warm mix asphalt additives on bitumen modified with 15% crumb rubber

Due to a growing concern over global warming, the bituminous mixture industry is making a constant effort to diminish its emissions by reducing manufacturing and installation temperatures without compromising the mechanical properties of the bituminous mixtures. The use of mixtures with tyre rubber has demonstrated that these mixtures can be economical and ecological and that they improve the behaviour of the pavements. However, bituminous mixtures with a high rubber content present one major drawback: they require higher mixing and installation temperatures due to the elevated viscosity caused by the high rubber content and thus they produce larger amounts of greenhouse gas emissions than conventional bituminous mixtures. This article presents a study of the effect of four viscosity-reducing additives (Sasobit®, Asphaltan A®, Asphaltan B® and Licomont BS 100®) on a bitumen modified with 15% rubber. The results of this study indicate that these additives successfully reduce viscosity, increase the softening temperature and reduce penetration. However, they do not have a clear effect on the test for elastic recovery and ductility at 25 °C.

Preparation and characterization of polyester resin-layered silicate containing reactive groups

Polymer nanocomposites, specifically nanoclay-reinforced polymers, have attracted great interest as matrix materials for high temperature composite applications. Nanocomposites require relatively low dispersant loads to achieve significant property enhancements. These enhancements are mainly a consequence of the interfacial effects that result from dispersing the silicate nanolayers in the polymer matrix and the high in-plane strength, stiffness and aspect ratio of the lamellar nanoparticles. The montmorillonite (MMT) clay, modified with organic onium ions with long alkyl chains as Cloisites, has been widely used to obtain nanocomposites. The presence of reactive groups in organic onium ions can form chemical bonds with the polymer matrix which favours a very high exfoliation degree of the clay platelets in the nanocomposite (1,2)

Influence of different organoclays on the curing, morphology, and dynamic mechanical properties of an epoxy adhesive

The thermal, mechanical, and adhesive properties of nanoclay-modified adhesives were investigated. Two organically modified montmorillonites: Cloisite 93A (C93A) and Nanomer I.30E (I.30E) were used as reinforcement of an epoxy adhesive. C93A and I.30E are modified with tertiary and primary alkyl ammonium cations, respectively. The aim was to study the influence of the organoclays on the curing, and on the mechanical and adhesive properties of the nanocomposites. A specific goal was to compare their behavior with that of Cloisite30B/epoxy and Cloisite15A/ epoxy nanocomposites that we have previously studied. Both C30B and C15A are modified with quaternary alkyl ammonium cations. Differential scanning calorimetry results showed that the clays accelerate the curing reaction, an effect that is related to the chemical structure of the ammonium cations. The three Cloisite/nanocomposites showed intercalated clay structures,the interlayer distance was independent of the clay content. The I.30E/epoxy nanocomposites presented exfoliated structure due to the catalytic effect of the organic modifier. Clay-epoxy nanocompo-sites showed lower glass transition temperature (Tg) and higher values of storage modulus than neat epoxy thermoset, with no significant differences between exfoliated or intercalated nanocom-posites. The shear strength of aluminum joints using clay/epoxy adhesives was lower than with the neat epoxy adhesive. The wáter aging was less damaging for joints with I.30E/epoxy adhesive.

Unsaturated polyester-poly(epsilon-caprolactone) hybrid nanocomposites: Thermal-mechanical properties

This paper reports on the thermal behavior and mechanical properties of nanocomposites based on unsaturated polyester resin (UP) modified with poly(ɛ-caprolactone) (PCL) and reinforced with an organically modified clay (cloisite 30B). To optimize the dispersion of 30B and the mixing of PCL in the UP resin, two different methods were employed to prepare crosslinked UP–PCL-30B hybrid nanocomposites. Besides, two samples of poly(ɛ-caprolactone) of different molecular weight (PCL2: Mn = 2.103g.mol−1 and PCL50: Mn = 5.104g.mol−1) were used at several concentrations (4, 6, 10 wt%). The 30B concentration was 4 wt% in all the nanocomposites. The morphology of the samples was studied by scanning electron microscopy (SEM). The analysis of X-ray patterns reveals that intercalated structures have been found for all ternary nanocomposites, independently of the molecular weight, PCL concentration and the preparation method selected. A slight rise of the glass transition temperature, Tg, is observed in UP/PCL/4%30B ternary nanocomposites regarding to neat UP. The analysis of the tensile properties of the ternary (hybrid) systems indicates that UP/4%PCL2/4%30B nanocomposite improves the tensile strength and elongation at break respect to the neat UP while the Young modulus remains constant

Automated detection and classification of brain injury lesions on structural magnetic resonance imaging

El daño cerebral adquirido (DCA) es un problema social y sanitario grave, de magnitud creciente y de una gran complejidad diagnóstica y terapéutica. Su elevada incidencia, junto con el aumento de la supervivencia de los pacientes, una vez superada la fase aguda, lo convierten también en un problema de alta prevalencia. En concreto, según la Organización Mundial de la Salud (OMS) el DCA estará entre las 10 causas más comunes de discapacidad en el año 2020. La neurorrehabilitación permite mejorar el déficit tanto cognitivo como funcional y aumentar la autonomía de las personas con DCA. Con la incorporación de nuevas soluciones tecnológicas al proceso de neurorrehabilitación se pretende alcanzar un nuevo paradigma donde se puedan diseñar tratamientos que sean intensivos, personalizados, monitorizados y basados en la evidencia. Ya que son estas cuatro características las que aseguran que los tratamientos son eficaces. A diferencia de la mayor parte de las disciplinas médicas, no existen asociaciones de síntomas y signos de la alteración cognitiva que faciliten la orientación terapéutica. Actualmente, los tratamientos de neurorrehabilitación se diseñan en base a los resultados obtenidos en una batería de evaluación neuropsicológica que evalúa el nivel de afectación de cada una de las funciones cognitivas (memoria, atención, funciones ejecutivas, etc.). La línea de investigación en la que se enmarca este trabajo de investigación pretende diseñar y desarrollar un perfil cognitivo basado no sólo en el resultado obtenido en esa batería de test, sino también en información teórica que engloba tanto estructuras anatómicas como relaciones funcionales e información anatómica obtenida de los estudios de imagen. De esta forma, el perfil cognitivo utilizado para diseñar los tratamientos integra información personalizada y basada en la evidencia. Las técnicas de neuroimagen representan una herramienta fundamental en la identificación de lesiones para la generación de estos perfiles cognitivos. La aproximación clásica utilizada en la identificación de lesiones consiste en delinear manualmente regiones anatómicas cerebrales. Esta aproximación presenta diversos problemas relacionados con inconsistencias de criterio entre distintos clínicos, reproducibilidad y tiempo. Por tanto, la automatización de este procedimiento es fundamental para asegurar una extracción objetiva de información. La delineación automática de regiones anatómicas se realiza mediante el registro tanto contra atlas como contra otros estudios de imagen de distintos sujetos. Sin embargo, los cambios patológicos asociados al DCA están siempre asociados a anormalidades de intensidad y/o cambios en la localización de las estructuras. Este hecho provoca que los algoritmos de registro tradicionales basados en intensidad no funcionen correctamente y requieran la intervención del clínico para seleccionar ciertos puntos (que en esta tesis hemos denominado puntos singulares). Además estos algoritmos tampoco permiten que se produzcan deformaciones grandes deslocalizadas. Hecho que también puede ocurrir ante la presencia de lesiones provocadas por un accidente cerebrovascular (ACV) o un traumatismo craneoencefálico (TCE). Esta tesis se centra en el diseño, desarrollo e implementación de una metodología para la detección automática de estructuras lesionadas que integra algoritmos cuyo objetivo principal es generar resultados que puedan ser reproducibles y objetivos. Esta metodología se divide en cuatro etapas: pre-procesado, identificación de puntos singulares, registro y detección de lesiones. Los trabajos y resultados alcanzados en esta tesis son los siguientes: Pre-procesado. En esta primera etapa el objetivo es homogeneizar todos los datos de entrada con el objetivo de poder extraer conclusiones válidas de los resultados obtenidos. Esta etapa, por tanto, tiene un gran impacto en los resultados finales. Se compone de tres operaciones: eliminación del cráneo, normalización en intensidad y normalización espacial. Identificación de puntos singulares. El objetivo de esta etapa es automatizar la identificación de puntos anatómicos (puntos singulares). Esta etapa equivale a la identificación manual de puntos anatómicos por parte del clínico, permitiendo: identificar un mayor número de puntos lo que se traduce en mayor información; eliminar el factor asociado a la variabilidad inter-sujeto, por tanto, los resultados son reproducibles y objetivos; y elimina el tiempo invertido en el marcado manual de puntos. Este trabajo de investigación propone un algoritmo de identificación de puntos singulares (descriptor) basado en una solución multi-detector y que contiene información multi-paramétrica: espacial y asociada a la intensidad. Este algoritmo ha sido contrastado con otros algoritmos similares encontrados en el estado del arte. Registro. En esta etapa se pretenden poner en concordancia espacial dos estudios de imagen de sujetos/pacientes distintos. El algoritmo propuesto en este trabajo de investigación está basado en descriptores y su principal objetivo es el cálculo de un campo vectorial que permita introducir deformaciones deslocalizadas en la imagen (en distintas regiones de la imagen) y tan grandes como indique el vector de deformación asociado. El algoritmo propuesto ha sido comparado con otros algoritmos de registro utilizados en aplicaciones de neuroimagen que se utilizan con estudios de sujetos control. Los resultados obtenidos son prometedores y representan un nuevo contexto para la identificación automática de estructuras. Identificación de lesiones. En esta última etapa se identifican aquellas estructuras cuyas características asociadas a la localización espacial y al área o volumen han sido modificadas con respecto a una situación de normalidad. Para ello se realiza un estudio estadístico del atlas que se vaya a utilizar y se establecen los parámetros estadísticos de normalidad asociados a la localización y al área. En función de las estructuras delineadas en el atlas, se podrán identificar más o menos estructuras anatómicas, siendo nuestra metodología independiente del atlas seleccionado. En general, esta tesis doctoral corrobora las hipótesis de investigación postuladas relativas a la identificación automática de lesiones utilizando estudios de imagen médica estructural, concretamente estudios de resonancia magnética. Basándose en estos cimientos, se han abrir nuevos campos de investigación que contribuyan a la mejora en la detección de lesiones. ABSTRACT Brain injury constitutes a serious social and health problem of increasing magnitude and of great diagnostic and therapeutic complexity. Its high incidence and survival rate, after the initial critical phases, makes it a prevalent problem that needs to be addressed. In particular, according to the World Health Organization (WHO), brain injury will be among the 10 most common causes of disability by 2020. Neurorehabilitation improves both cognitive and functional deficits and increases the autonomy of brain injury patients. The incorporation of new technologies to the neurorehabilitation tries to reach a new paradigm focused on designing intensive, personalized, monitored and evidence-based treatments. Since these four characteristics ensure the effectivity of treatments. Contrary to most medical disciplines, it is not possible to link symptoms and cognitive disorder syndromes, to assist the therapist. Currently, neurorehabilitation treatments are planned considering the results obtained from a neuropsychological assessment battery, which evaluates the functional impairment of each cognitive function (memory, attention, executive functions, etc.). The research line, on which this PhD falls under, aims to design and develop a cognitive profile based not only on the results obtained in the assessment battery, but also on theoretical information that includes both anatomical structures and functional relationships and anatomical information obtained from medical imaging studies, such as magnetic resonance. Therefore, the cognitive profile used to design these treatments integrates information personalized and evidence-based. Neuroimaging techniques represent an essential tool to identify lesions and generate this type of cognitive dysfunctional profiles. Manual delineation of brain anatomical regions is the classical approach to identify brain anatomical regions. Manual approaches present several problems related to inconsistencies across different clinicians, time and repeatability. Automated delineation is done by registering brains to one another or to a template. However, when imaging studies contain lesions, there are several intensity abnormalities and location alterations that reduce the performance of most of the registration algorithms based on intensity parameters. Thus, specialists may have to manually interact with imaging studies to select landmarks (called singular points in this PhD) or identify regions of interest. These two solutions have the same inconvenient than manual approaches, mentioned before. Moreover, these registration algorithms do not allow large and distributed deformations. This type of deformations may also appear when a stroke or a traumatic brain injury (TBI) occur. This PhD is focused on the design, development and implementation of a new methodology to automatically identify lesions in anatomical structures. This methodology integrates algorithms whose main objective is to generate objective and reproducible results. It is divided into four stages: pre-processing, singular points identification, registration and lesion detection. Pre-processing stage. In this first stage, the aim is to standardize all input data in order to be able to draw valid conclusions from the results. Therefore, this stage has a direct impact on the final results. It consists of three steps: skull-stripping, spatial and intensity normalization. Singular points identification. This stage aims to automatize the identification of anatomical points (singular points). It involves the manual identification of anatomical points by the clinician. This automatic identification allows to identify a greater number of points which results in more information; to remove the factor associated to inter-subject variability and thus, the results are reproducible and objective; and to eliminate the time spent on manual marking. This PhD proposed an algorithm to automatically identify singular points (descriptor) based on a multi-detector approach. This algorithm contains multi-parametric (spatial and intensity) information. This algorithm has been compared with other similar algorithms found on the state of the art. Registration. The goal of this stage is to put in spatial correspondence two imaging studies of different subjects/patients. The algorithm proposed in this PhD is based on descriptors. Its main objective is to compute a vector field to introduce distributed deformations (changes in different imaging regions), as large as the deformation vector indicates. The proposed algorithm has been compared with other registration algorithms used on different neuroimaging applications which are used with control subjects. The obtained results are promising and they represent a new context for the automatic identification of anatomical structures. Lesion identification. This final stage aims to identify those anatomical structures whose characteristics associated to spatial location and area or volume has been modified with respect to a normal state. A statistical study of the atlas to be used is performed to establish which are the statistical parameters associated to the normal state. The anatomical structures that may be identified depend on the selected anatomical structures identified on the atlas. The proposed methodology is independent from the selected atlas. Overall, this PhD corroborates the investigated research hypotheses regarding the automatic identification of lesions based on structural medical imaging studies (resonance magnetic studies). Based on these foundations, new research fields to improve the automatic identification of lesions in brain injury can be proposed.