Rheological and tribological properties of poly(phenylene sulphide) and poly(ether ether ketone) composites incorporating carbon nanotubes and inorganic nanoparticles: a comparative study

The rheological and tribological properties of single-walled carbon nanotube (SWCNT)-reinforced poly(phenylene sulphide) (PPS) and poly(ether ether ketone) (PEEK) nanocomposites prepared via melt-extrusion were investigated. The effectiveness of employing a dual-nanofiller strategy combining polyetherimide (PEI)-wrapped SWCNTs with inorganic fullerene-like tungsten disulfide (IF-WS2) nanoparticles for property enhancement of the resulting hybrid composites was evaluated. Viscoelastic measurements revealed that the complex viscosity ?, storage modulus G?, and loss modulus G? increased with SWCNT content. In the low-frequency region, G? and G? became almost independent of frequency at higher SWCNT loadings, suggesting a transition from liquid-like to solid-like behavior. The incorporation of increasing IF-WS2 contents led to a progressive drop in ? and G? due to a lubricant effect. PEEK nanocomposites showed lower percolation threshold than those based on PPS, ascribed to an improved SWCNT dispersion due to the higher affinity between PEI and PEEK. The SWCNTs significantly lowered the wear rate but only slightly reduced the coefficient of friction. Composites with both nanofillers exhibited improved wear behavior, attributed to the outstanding tribological properties of these nanoparticles and a synergistic reinforcement effect. The combination of SWCNTs with IF-WS2 is a promising route for improving the tribological and rheological performance of thermoplastic nanocomposites.

Palaeoenvironmental reconstruction of Las Conchas Mire (Asturias, N. Spain) from the ketone and fatty acid record= reconstrucción paleoambiental de la Turbera de las Conchas (Asturias, N. España)a partir del registro de cetonas y ácidos grasos

Se han estudiado los biomarcadores, principalmente cetonas y ácidos, preservados en el registro de 3.2 m de la Turbera de Las Conchas. Las cetonas reflejan cierta actividad bacteriana desde 94 cm hasta la base del registro, Los ácidos grasos reflejan una buena preservación de la materia orgánica, salvo en los 20 cm superiores en los que hay indicios de oxidación microbiana de alcanos .The biomarkers, mainly ketones and fally aclds, preserved In 3.2 m deep Las Conchas Mire have been studied, Kelones reflect certain bacterial activity from 94 cm to the bottom of the record. Falty aclds indlcate a good preservation of the organlc matter, wlth the exception of the uppermost 20 cm In whlch mlcroblal oxldation of alkanes are likely to occur

Opportunities and challenges in the use of inorganic fullerene-like nanoparticles to produce advanced polymer nanocomposites

Polymer/inorganic nanoparticle nanocomposites have garnered considerable academic and industrial interest over recent decades in the development of advanced materials for a wide range of applications. In this respect, the dispersion of so-called inorganic fullerene-like (IF) nanoparticles, e.g., tungsten disulfide (IF-WS2) or molybdenum disulfide (IF-MoS2), into polymeric matrices is emerging as a new strategy. The surprising properties of these layered metal dichalcogenides such as high impact resistance and superior tribological behavior, attributed to their nanoscale size and hollow quasi-spherical shape, open up a wide variety of opportunities for applications of these inorganic compounds. The present work presents a detailed overview on research in the area of IF-based polymer nanocomposites, with special emphasis on the use of IF-WS2 nanoparticles as environmentally friendly reinforcing fillers. The incorporation of IF particles has been shown to be efficient for improving thermal, mechanical and tribological properties of various thermoplastic polymers, such as polypropylene, nylon-6, poly(phenylene sulfide), poly(ether ether ketone), where nanocomposites were fabricated by simple melt-processing routes without the need for modifiers or surfactants. This new family of nanocomposites exhibits similar or enhanced performance when compared with nanocomposites that incorporate carbon nanotubes, carbon nanofibers or nanoclays, but are substantially more cost-effective, efficient and environmentally satisfactory. Most recently, innovative approaches have been described that exploit synergistic effects to produce new materials with enhanced properties, including the combined use of micro- and nanoparticles such as IF-WS2/nucleating agent or IF-WS2/carbon fiber, as well as dual nanoparticle systems such as SWCNT/IF-WS2 where each nanoparticle has different characteristics. The structure–property relationships of these nanocomposites are discussed and potential applications proposed ranging from medicine to the aerospace, automotive and electronics industries.

Hierarchical and hybrid polymer nanocomposites based on carbon nanotubes and inorganic fullerene type nanoparticles

The influence of singlewalled carbon nanotubes (SWCNT) and inorganic fullerenelike tungsten disulfide nanoparticles (IFWS2) on the morphology and thermal, mechanical and electrical performance of multifunctional fibrereinforced polymer composites has been investigated. Significant improvements were observed in stiffness, strength and toughness in poly (ether ether ketone) (PEEK) / (SWCNT) / glass fibre (GF) laminates when a compatibilizer was used for wrapping the CNTs. Hybrid poly(phenylene sulphide) (PPS)/IFWS2/ carbon fibre (CF) reinforced polymer composites showed improved mechanical and tribological properties attributed to a synergetic effect between the IF nanoparticles and CF.

Origen y distribución de los pesticidas organoclorados (OCPs) en sedimentos actuales de la Laguna de El Hito (Cuenca, España Central)

Se llevó a cabo el estudio del estado ambiental actual de la Laguna de El Hito basado en el análisis y cuantificación de 24 pesticidas organoclorados (OCPs): hexaclorobenceno, hexaclorociclohexanos, diclorodifenil-tricloroetano sus homólogos y metabolitos (DDTs), y ciclodienos (aldrín, dieldrín, endrín, endrín aldehido, endrín cetona, ?-clordano, ?-clordano, endosulfán I, endosulfán II, endosulfán sulfato, heptacloro, heptacloro epóxido B y metoxicloro). Algunos compuestos superaron los Niveles Genéricos de Referencia (NGR) para la salud humana y los ecosistemas del R.D.09/2005. Se determinó su origen a partir de diversos índices interpretándose tanto aplicaciones históricas como más recientes. Se obtuvieron los mapas de distribución de los índices y de las concentraciones de OCPs.Environmental evaluation of the current state of El Hito Lake referred to 24 organochlorine pesticides (OCPs) was carried out: hexachlorobenzene, hexachlorocyclohexanes, dichlorodiphenyltrichloroethane their homologs and metabolites (DDTs), and cyclodienes (aldrin, dieldrin, endrin, endrin aldehyde, endrin ketone, α- chlordane, γ- chlordane, endosulfan I, endosulfan II, endosulfan sulphate, heptachlor, heptachlor epóxide B and metoxichlor). Some of these compounds showed concentrations above the Soil Screening Levels (SSLs) for human health and ecosystems as is established in R.D.09/2005. Different indexes were used to determine their origin. Both historical and recent applications sources were interpreted. Distribution maps for concentrations and indexes were plotted

Extracción de conocimientos de la diabetes tipo 1 utilizando la metodología de "Data Mining"

La diabetes mellitus es un trastorno en la metabolización de los carbohidratos, caracterizado por la nula o insuficiente segregación de insulina (hormona producida por el páncreas), como resultado del mal funcionamiento de la parte endocrina del páncreas, o de una creciente resistencia del organismo a esta hormona. Esto implica, que tras el proceso digestivo, los alimentos que ingerimos se transforman en otros compuestos químicos más pequeños mediante los tejidos exocrinos. La ausencia o poca efectividad de esta hormona polipéptida, no permite metabolizar los carbohidratos ingeridos provocando dos consecuencias: Aumento de la concentración de glucosa en sangre, ya que las células no pueden metabolizarla; consumo de ácidos grasos mediante el hígado, liberando cuerpos cetónicos para aportar la energía a las células. Esta situación expone al enfermo crónico, a una concentración de glucosa en sangre muy elevada, denominado hiperglucemia, la cual puede producir a medio o largo múltiples problemas médicos: oftalmológicos, renales, cardiovasculares, cerebrovasculares, neurológicos… La diabetes representa un gran problema de salud pública y es la enfermedad más común en los países desarrollados por varios factores como la obesidad, la vida sedentaria, que facilitan la aparición de esta enfermedad. Mediante el presente proyecto trabajaremos con los datos de experimentación clínica de pacientes con diabetes de tipo 1, enfermedad autoinmune en la que son destruidas las células beta del páncreas (productoras de insulina) resultando necesaria la administración de insulina exógena. Dicho esto, el paciente con diabetes tipo 1 deberá seguir un tratamiento con insulina administrada por la vía subcutánea, adaptado a sus necesidades metabólicas y a sus hábitos de vida. Para abordar esta situación de regulación del control metabólico del enfermo, mediante una terapia de insulina, no serviremos del proyecto “Páncreas Endocrino Artificial” (PEA), el cual consta de una bomba de infusión de insulina, un sensor continuo de glucosa, y un algoritmo de control en lazo cerrado. El objetivo principal del PEA es aportar al paciente precisión, eficacia y seguridad en cuanto a la normalización del control glucémico y reducción del riesgo de hipoglucemias. El PEA se instala mediante vía subcutánea, por lo que, el retardo introducido por la acción de la insulina, el retardo de la medida de glucosa, así como los errores introducidos por los sensores continuos de glucosa cuando, se descalibran dificultando el empleo de un algoritmo de control. Llegados a este punto debemos modelar la glucosa del paciente mediante sistemas predictivos. Un modelo, es todo aquel elemento que nos permita predecir el comportamiento de un sistema mediante la introducción de variables de entrada. De este modo lo que conseguimos, es una predicción de los estados futuros en los que se puede encontrar la glucosa del paciente, sirviéndonos de variables de entrada de insulina, ingesta y glucosa ya conocidas, por ser las sucedidas con anterioridad en el tiempo. Cuando empleamos el predictor de glucosa, utilizando parámetros obtenidos en tiempo real, el controlador es capaz de indicar el nivel futuro de la glucosa para la toma de decisones del controlador CL. Los predictores que se están empleando actualmente en el PEA no están funcionando correctamente por la cantidad de información y variables que debe de manejar. Data Mining, también referenciado como Descubrimiento del Conocimiento en Bases de Datos (Knowledge Discovery in Databases o KDD), ha sido definida como el proceso de extracción no trivial de información implícita, previamente desconocida y potencialmente útil. Todo ello, sirviéndonos las siguientes fases del proceso de extracción del conocimiento: selección de datos, pre-procesado, transformación, minería de datos, interpretación de los resultados, evaluación y obtención del conocimiento. Con todo este proceso buscamos generar un único modelo insulina glucosa que se ajuste de forma individual a cada paciente y sea capaz, al mismo tiempo, de predecir los estados futuros glucosa con cálculos en tiempo real, a través de unos parámetros introducidos. Este trabajo busca extraer la información contenida en una base de datos de pacientes diabéticos tipo 1 obtenidos a partir de la experimentación clínica. Para ello emplearemos técnicas de Data Mining. Para la consecución del objetivo implícito a este proyecto hemos procedido a implementar una interfaz gráfica que nos guía a través del proceso del KDD (con información gráfica y estadística) de cada punto del proceso. En lo que respecta a la parte de la minería de datos, nos hemos servido de la denominada herramienta de WEKA, en la que a través de Java controlamos todas sus funciones, para implementarlas por medio del programa creado. Otorgando finalmente, una mayor potencialidad al proyecto con la posibilidad de implementar el servicio de los dispositivos Android por la potencial capacidad de portar el código. Mediante estos dispositivos y lo expuesto en el proyecto se podrían implementar o incluso crear nuevas aplicaciones novedosas y muy útiles para este campo. Como conclusión del proyecto, y tras un exhaustivo análisis de los resultados obtenidos, podemos apreciar como logramos obtener el modelo insulina-glucosa de cada paciente. ABSTRACT. The diabetes mellitus is a metabolic disorder, characterized by the low or none insulin production (a hormone produced by the pancreas), as a result of the malfunctioning of the endocrine pancreas part or by an increasing resistance of the organism to this hormone. This implies that, after the digestive process, the food we consume is transformed into smaller chemical compounds, through the exocrine tissues. The absence or limited effectiveness of this polypeptide hormone, does not allow to metabolize the ingested carbohydrates provoking two consequences: Increase of the glucose concentration in blood, as the cells are unable to metabolize it; fatty acid intake through the liver, releasing ketone bodies to provide energy to the cells. This situation exposes the chronic patient to high blood glucose levels, named hyperglycemia, which may cause in the medium or long term multiple medical problems: ophthalmological, renal, cardiovascular, cerebrum-vascular, neurological … The diabetes represents a great public health problem and is the most common disease in the developed countries, by several factors such as the obesity or sedentary life, which facilitate the appearance of this disease. Through this project we will work with clinical experimentation data of patients with diabetes of type 1, autoimmune disease in which beta cells of the pancreas (producers of insulin) are destroyed resulting necessary the exogenous insulin administration. That said, the patient with diabetes type 1 will have to follow a treatment with insulin, administered by the subcutaneous route, adapted to his metabolic needs and to his life habits. To deal with this situation of metabolic control regulation of the patient, through an insulin therapy, we shall be using the “Endocrine Artificial Pancreas " (PEA), which consists of a bomb of insulin infusion, a constant glucose sensor, and a control algorithm in closed bow. The principal aim of the PEA is providing the patient precision, efficiency and safety regarding the normalization of the glycemic control and hypoglycemia risk reduction". The PEA establishes through subcutaneous route, consequently, the delay introduced by the insulin action, the delay of the glucose measure, as well as the mistakes introduced by the constant glucose sensors when, decalibrate, impede the employment of an algorithm of control. At this stage we must shape the patient glucose levels through predictive systems. A model is all that element or set of elements which will allow us to predict the behavior of a system by introducing input variables. Thus what we obtain, is a prediction of the future stages in which it is possible to find the patient glucose level, being served of input insulin, ingestion and glucose variables already known, for being the ones happened previously in the time. When we use the glucose predictor, using obtained real time parameters, the controller is capable of indicating the future level of the glucose for the decision capture CL controller. The predictors that are being used nowadays in the PEA are not working correctly for the amount of information and variables that it need to handle. Data Mining, also indexed as Knowledge Discovery in Databases or KDD, has been defined as the not trivial extraction process of implicit information, previously unknown and potentially useful. All this, using the following phases of the knowledge extraction process: selection of information, pre- processing, transformation, data mining, results interpretation, evaluation and knowledge acquisition. With all this process we seek to generate the unique insulin glucose model that adjusts individually and in a personalized way for each patient form and being capable, at the same time, of predicting the future conditions with real time calculations, across few input parameters. This project of end of grade seeks to extract the information contained in a database of type 1 diabetics patients, obtained from clinical experimentation. For it, we will use technologies of Data Mining. For the attainment of the aim implicit to this project we have proceeded to implement a graphical interface that will guide us across the process of the KDD (with graphical and statistical information) of every point of the process. Regarding the data mining part, we have been served by a tool called WEKA's tool called, in which across Java, we control all of its functions to implement them by means of the created program. Finally granting a higher potential to the project with the possibility of implementing the service for Android devices, porting the code. Through these devices and what has been exposed in the project they might help or even create new and very useful applications for this field. As a conclusion of the project, and after an exhaustive analysis of the obtained results, we can show how we achieve to obtain the insulin–glucose model for each patient.