Spray coating of oxide and chalcogenide semiconductor layers for TFT application

This work documents the deposition and optimization of semiconductor thin films using chemical spray coating technique (CSC) for application on thin-film transistors (TFTs), with a low-cost, simple method. CSC setup was implemented and explored for industrial application, within Holst Centre, an R&D center in the Netherlands. As zinc oxide had already been studied within the organization, it was used as a standard material in the initial experiments, obtaining typical mobility values of 0.14 cm2/(V.s) for unpatterned TFTs. Then, oxide X layer characteristics were compared for films deposited with CSC at 40°C and spin-coating. The mobility of the spin-coated TFTs was 103 cm2/(V.s) higher, presumably due to the lack of uniformity of spray-coated film at such low temperatures. Lastly, tin sulfide, a relatively unexplored material, was deposited by CSC in order to obtain functional TFTs and explore the device’s potential for working as a phototransistor. Despite the low mobilities of the devices, a sensitive photodetector was made, showing drain current variation of nearly one order of magnitude under yellow light. CSC technique’s simplicity and versatility was confirmed, as three different semiconductors were successfully implemented into functional devices.

Development and validation of gold nanoprobes for human SNP detection towards commercial application

Conventional molecular techniques for detection and characterization of relevant nucleic acid (i.e. DNA) sequences are, nowadays, cumbersome, expensive and with reduced portability. The main objective of this dissertation consisted in the optimization and validation of a fast and low-cost colorimetric nanodiagnostic methodology for the detection of single nucleotide polymorphisms (SNPs). This was done considering SNPs associated to obesity of commercial interest for STAB VIDA, and subsequent evaluation of other clinically relevant targets. Also, integration of this methodology into a microfluidic platform envisaging portability and application on points-of-care (POC) was achieved. To warrant success in pursuing these objectives, the experimental work was divided in four sections: i) genetic association of SNPs to obesity in the Portuguese population; ii) optimization and validation of the non-cross-linking approach for complete genotype characterization of these SNPs; iii) incorporation into a microfluidic platform; and iv) translation to other relevant commercial targets. FTO dbSNP rs#:9939609 carriers had higher body mass index (BMI), total body fat mass, waist perimeter and 2.5 times higher risk to obesity. AuNPs functionalized with thiolated oligonucleotides (Au-nanoprobes) were used via the non-cross-linking to validate a diagnostics approach against the gold standard technique - Sanger Sequencing - with high levels of sensitivity (87.50%) and specificity (91.67%). A proof-of-concept POC microfluidic device was assembled towards incorporation of the molecular detection strategy. In conclusion a successful framework was developed and validated for the detection of SNPs with commercial interest for STAB VIDA, towards future translation into a POC device.

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.

Application of near surface mounted (NSM) strengthening technique to traditional timber frame walls

Timber frame buildings are well known as an efficient seismic resistant structure popular all over the world not only due to their seismic performance, but also to their low cost and the strength they offer. These constructions still exist today and it is important to be able to preserve them, so a better knowledge on their behaviour is sought. Furthermore, historic technologies could be used even in modern constructions to build seismic resistant buildings using more natural materials with lesser costs. A great rehabilitation effort is being carried out on this type of buildings, as their neglect has led to decay or their change in use and alterations to the structure has led to the need to retrofit such buildings; only recently studies on their behaviour have become available and only a few of them address the issue of possible strengthening techniques for this kind of walls. In this scope, an innovative retrofitting technique (near surface mounted steel flat bars) is proposed and validated on traditional timber frame walls based on an extensive experimental program. The results of the static cyclic tests on distinct wall typologies retrofitted with the NSM technique are herein presented and discussed in detail. The main features on deformation, lateral stiffness, lateral resistance and seismic performance indexes are analysed

Assessing the application of polypropylene multifilament yarns in brain phantoms

The currently available clinical imaging methods do not provide highly detailed information about location and severity of axonal injury or the expected recovery time of patients with traumatic brain injury [1]. High-Definition Fiber Tractography (HDFT) is a novel imaging modality that allows visualizing and quantifying, directly, the degree of axons damage, predicting functional deficits due to traumatic axonal injury and loss of cortical projections. This imaging modality is based on diffusion technology [2]. The inexistence of a phantom able to mimic properly the human brain hinders the possibility of testing, calibrating and validating these medical imaging techniques. Most research done in this area fails in key points, such as the size limit reproduced of the brain fibers and the quick and easy reproducibility of phantoms [3]. For that reason, it is necessary to develop similar structures matching the micron scale of axon tubes. Flexible textiles can play an important role since they allow producing controlled packing densities and crossing structures that match closely the human crossing patterns of the brain. To build a brain phantom, several parameters must be taken into account in what concerns to the materials selection, like hydrophobicity, density and fiber diameter, since these factors influence directly the values of fractional anisotropy. Fiber cross-section shape is other important parameter. Earlier studies showed that synthetic fibrous materials are a good choice for building a brain phantom [4]. The present work is integrated in a broader project that aims to develop a brain phantom made by fibrous materials to validate and calibrate HDFT. Due to the similarity between thousands of hollow multifilaments in a fibrous arrangement, like a yarn, and the axons, low twist polypropylene multifilament yarns were selected for this development. In this sense, extruded hollow filaments were analysed in scanning electron microscope to characterize their main dimensions and shape. In order to approximate the dimensional scale to human axons, five types of polypropylene yarns with different linear density (denier) were used, aiming to understand the effect of linear density on the filament inner and outer areas. Moreover, in order to achieve the required dimensions, the polypropylene filaments cross-section was diminished in a drawing stage of a filament extrusion line. Subsequently, tensile tests were performed to characterize the mechanical behaviour of hollow filaments and to evaluate the differences between stretched and non-stretched filaments. In general, an increase of the linear density causes the increase in the size of the filament cross section. With the increase of structure orientation of filaments, induced by stretching, breaking tenacity increases and elongation at break decreases. The production of hollow fibers, with the required characteristics, is one of the key steps to create a brain phantom that properly mimics the human brain that may be used for the validation and calibration of HDFT, an imaging approach that is expected to contribute significantly to the areas of brain related research.

Desarrollo de sistemas nano/micro-particulados con aplicación en la fotodegradación selectiva de contaminantes orgánicos en solución. Development of nano / micro-particles systems with application to selective photodegradation of organic pollutants in solution.

IDENTIFICACIÓN DEL PROBLEMA DE ESTUDIO. Las sustancias orgánicas solubles en agua no biodegradables tales como ciertos herbicidas, colorantes industriales y metabolitos de fármacos de uso masivo son una de las principales fuentes de contaminación en aguas subterráneas de zonas agrícolas y en efluentes industriales y domésticos. Las reacciones fotocatalizadas por irradiación UV-visible y sensitizadores orgánicos e inorgánicos son uno de los métodos más económicos y convenientes para la descomposición de contaminantes en subproductos inocuos y/o biodegradables. En muchas aplicaciones es deseable un alto grado de especificidad, efectividad y velocidad de degradación de un dado agente contaminante que se encuentra presente en una mezcla compleja de sustancias orgánicas en solución. En particular son altamente deseables sistemas nano/micro -particulados que formen suspensiones acuosas estables debido a que estas permiten una fácil aplicación y una eficaz acción descontaminante en grandes volúmenes de fluidos. HIPÓTESIS Y PLANTEO DE LOS OBJETIVOS. El objetivo general de este proyecto es desarrollar sistemas nano/micro particulados formados por polímeros de impresión molecular (PIMs) y foto-sensibilizadores (FS). Un PIMs es un polímero especialmente sintetizado para que sea capaz de reconocer específicamente un analito (molécula plantilla) determinado. La actividad de unión específica de los PIMs en conjunto con la capacidad fotocatalizadora de los sensibilizadores pueden ser usadas para lograr la fotodescomposición específica de moléculas “plantilla” (en este caso un dado contaminante) en soluciones conteniendo mezclas complejas de sustancias orgánicas. MATERIALES Y MÉTODOS A UTILIZAR. Se utilizaran técnicas de polimerización en mini-emulsión para sintetizar los sistemas nano/micro PIM-FS para buscar la degradación de ciertos compuestos de interés. Para caracterizar eficiencias, mecanismos y especificidad de foto-degradación en dichos sistemas se utilizan diversas técnicas espectroscópicas (estacionarias y resueltas en el tiempo) y de cromatografía (HPLC y GC). Así mismo, para medir directamente distribuciones de afinidades de unión y eficiencia de foto-degradación se utilizaran técnicas de fluorescencia de molécula/partícula individual. Estas determinaciones permitirán obtener resultados importantes al momento de analizar los factores que afectan la eficiencia de foto-degradación (nano/micro escala), tales como cantidad y ubicación de foto- sensibilizadores en las matrices poliméricas y eficiencia de unión de la plantilla y los productos de degradación al PIM. RESULTADOS ESPERADOS. Los estudios propuestos apuntan a un mejor entendimiento de procesos foto-iniciados en entornos nano/micro-particulados para aplicar dichos conocimientos al diseño de sistemas optimizados para la foto-destrucción selectiva de contaminantes acuosos de relevancia social; tales como herbicidas, residuos industriales, metabolitos de fármacos de uso masivo, etc. IMPORTANCIA DEL PROYECTO. Los sistemas nano/micro-particulados PIM-FS que se propone desarrollar en este proyecto se presentan como candidatos ideales para tratamientos específicos de efluentes industriales y domésticos en los cuales se desea lograr la degradación selectiva de compuestos orgánicos. Los conocimientos adquiridos serán indispensables para construir una plataforma versátil de sistemas foto-catalíticos específicos para la degradación de diversos contaminantes orgánicos de interés social. En lo referente a la formación de recursos humanos, el proyecto propuesto contribuirá en forma directa a la formación de 3 estudiantes de postgrado y 2 estudiantes de grado. En las capacidades institucionales se contribuirá al acondicionamiento del Laboratorio para Microscopía Óptica Avanzada (LMOA) en el Dpto. de Química de la UNRC y al montaje de un sistema de microscopio de fluorescencia que permitirá la aplicación de técnicas avanzadas de espectroscopia de fluorescencia de molecula individual. Water-soluble organic molecules such as certain non-biodegradable herbicides, industrial dyes and metabolites of widespread use drugs are a major source of pollution in groundwater from agricultural areas and in industrial and domestic effluents. Photo-catalytic reactions by UV-visible irradiation and organic sensitizers are one of the most economical and convenient methods for the decomposition of pollutants into harmless byproducts. In many applications it is highly desirable a high degree of specificity, effectiveness and speed of degradation of specific pollutants present in a complex mixture. In particular nano/micro-particles systems that form stable aqueous suspensions are highly desirable because they allow for easy application and effective decontamination of large volumes of fluids. Herein we propose the development of nano/micro particles composed by molecularly imprinted polymers (MIP) and photo-sensitizers (PS). The specific binding of MIP and the photo-catalytic ability of the sensitizers are used to achieve the photo-decomposition of specific "template" molecules in complex mixtures. Mini-emulsion polymerization techniques will be used to synthesize nano/micro MIP-FS systems. Spectroscopy (steady-state and time resolved) and chromatography (GC and HPLC) will be used to characterize efficiency, mechanisms and specificity of photo-degradation in these systems. In addition single molecule/particle fluorescence spectroscopy techniques will be used to directly measure distributions of binding affinities and photo-degradation efficiency in individual particles. The proposed studies point to a more detailed understanding of the factors affecting the photo-degradation efficiency in nano/micro-particles and to apply that knowledge in the design of optimized systems for photo-selective destruction of socially relevant aqueous pollutants.

The use of forensic polycyclic aromatic hydrocarbon signatures and Compound Ratio Analysis Techniques (CORAT) for the source characterisation of petrogenic/pyrogenic environmental releases.

This study utilised recent developments in forensic aromatic hydrocarbon fingerprint analysis to characterise and identify specific biogenic, pyrogenic and petrogenic contamination. The fingerprinting and data interpretation techniques discussed include the recognition of: The distribution patterns of hydrocarbons (alkylated naphthalene, phenanthrene, dibenzothiophene, fluorene, chrysene and phenol isomers), • Analysis of “source-specific marker” compounds (individual saturated hydrocarbons, including n-alkanes (n-C5 through 0-C40) • Selected benzene, toluene, ethylbenzene and xylene isomers (BTEX), • The recalcitrant isoprenoids; pristane and phytane and • The determination of diagnostic ratios of specific petroleum / non-petroleum constituents, and the application of various statistical and numerical analysis tools. An unknown sample from the Irish Environmental Protection Agency (EPA) for origin characterisation was subjected to analysis by gas chromatography utilising both flame ionisation and mass spectral detection techniques in comparison to known reference materials. The percentage of the individual Polycyclic Aromatic Hydrocarbons (PAIIs) and biomarker concentrations in the unknown sample were normalised to the sum of the analytes and the results were compared with the corresponding results with a range of reference materials. In addition, to the determination of conventional diagnostic PAH and biomarker ratios, a number of “source-specific markers” isomeric PAHs within the same alkylation levels were determined, and their relative abundance ratios were computed in order to definitively identify and differentiate the various sources. Statistical logarithmic star plots were generated from both sets of data to give a pictorial representation of the comparison between the unknown sample and reference products. The study successfully characterised the unknown sample as being contaminated with a “coal tar” and clearly demonstrates the future role of compound ratio analysis (CORAT) in the identification of possible source contaminants.

Application of multivariate statistical process control to fuel cell manufacturing

Univariate statistical control charts, such as the Shewhart chart, do not satisfy the requirements for process monitoring on a high volume automated fuel cell manufacturing line. This is because of the number of variables that require monitoring. The risk of elevated false alarms, due to the nature of the process being high volume, can present problems if univariate methods are used. Multivariate statistical methods are discussed as an alternative for process monitoring and control. The research presented is conducted on a manufacturing line which evaluates the performance of a fuel cell. It has three stages of production assembly that contribute to the final end product performance. The product performance is assessed by power and energy measurements, taken at various time points throughout the discharge testing of the fuel cell. The literature review performed on these multivariate techniques are evaluated using individual and batch observations. Modern techniques using multivariate control charts on Hotellings T2 are compared to other multivariate methods, such as Principal Components Analysis (PCA). The latter, PCA, was identified as the most suitable method. Control charts such as, scores, T2 and DModX charts, are constructed from the PCA model. Diagnostic procedures, using Contribution plots, for out of control points that are detected using these control charts, are also discussed. These plots enable the investigator to perform root cause analysis. Multivariate batch techniques are compared to individual observations typically seen on continuous processes. Recommendations, for the introduction of multivariate techniques that would be appropriate for most high volume processes, are also covered.

Image registration techniques with multiresolution analysis in satellite oceanography

En aquest article es fa una descripció dels procediments realitzats per enregistrar dues imatges geomètricament, de forma automàtica, si es pren la primera com a imatge de referència. Es comparen els resultats obtinguts mitjançant tres mètodes. El primer mètode és el d’enregistrament clàssic en domini espacial maximitzant la correlació creuada (MCC)[1]. El segon mètode es basa en aplicar l’enregistrament MCC conjuntament amb un anàlisi multiescala a partir de transformades wavelet [2]. El tercer mètode és una variant de l’anterior que es situa a mig camí dels dos. Per cada un dels mètodes s’obté una estimació dels coeficients de la transformació que relaciona les dues imatges. A continuació es transforma per cada cas la segona imatge i es georeferencia respecte la primera. I per acabar es proposen unes mesures quantitatives que permeten discutir i comparar els resultats obtinguts amb cada mètode.

Hydrogeophysical data integration at larger scales: Application of Bayesian sequential simulation for the characterization of heterogeneous alluvial aquifers

Knowledge of the spatial distribution of hydraulic conductivity (K) within an aquifer is critical for reliable predictions of solute transport and the development of effective groundwater management and/or remediation strategies. While core analyses and hydraulic logging can provide highly detailed information, such information is inherently localized around boreholes that tend to be sparsely distributed throughout the aquifer volume. Conversely, larger-scale hydraulic experiments like pumping and tracer tests provide relatively low-resolution estimates of K in the investigated subsurface region. As a result, traditional hydrogeological measurement techniques contain a gap in terms of spatial resolution and coverage, and they are often alone inadequate for characterizing heterogeneous aquifers. Geophysical methods have the potential to bridge this gap. The recent increased interest in the application of geophysical methods to hydrogeological problems is clearly evidenced by the formation and rapid growth of the domain of hydrogeophysics over the past decade (e.g., Rubin and Hubbard, 2005).

Combining classification techniques to define topo-climatic landscapes

Landscape classification tackles issues related to the representation and analysis of continuous and variable ecological data. In this study, a methodology is created in order to define topo-climatic landscapes (TCL) in the north-west of Catalonia (north-east of the Iberian Peninsula). TCLs relate the ecological behaviour of a landscape in terms of topography, physiognomy and climate, which compound the main drivers of an ecosystem. Selected variables are derived from different sources such as remote sensing and climatic atlas. The proposed methodology combines unsupervised interative cluster classification with a supervised fuzzy classification. As a result, 28 TCLs have been found for the study area which may be differentiated in terms of vegetation physiognomy and vegetation altitudinal range type. Furthermore a hierarchy among TCLs is set, enabling the merging of clusters and allowing for changes of scale. Through the topo-climatic landscape map, managers may identify patches with similar environmental conditions and asses at the same time the uncertainty involved.

Model based fault diagnosis with application to an automotive engine

Report for the scientific sojourn at the University of Linköping between April to July 2007. Monitoring of the air intake system of an automotive engine is important to meet emission related legislative diagnosis requirements. During the research the problem of fault detection in the air intake system was stated as a constraint satisfaction problem over continuous domains with a big number of variables and constraints. This problem was solved using Interval-based Consistency Techniques. Interval-based consistency techniques are shown to be particularly efficient for checking the consistency of the Analytical Redundancy Relations (ARRs), dealing with uncertain measurements and parameters, and using experimental data. All experiments were performed on a four-cylinder turbo-charged spark-ignited SAAB engine located in the research laboratory at Vehicular System Group - University of Linköping.

What Belongs Where? Variable Selection for Zero-Inflated Count Models with an Application to the Demand for Health Care

This paper develops stochastic search variable selection (SSVS) for zero-inflated count models which are commonly used in health economics. This allows for either model averaging or model selection in situations with many potential regressors. The proposed techniques are applied to a data set from Germany considering the demand for health care. A package for the free statistical software environment R is provided.

The application of chemometrics on Infrared and Raman spectra as a tool for the forensic analysis of paints

The aim of this work is to evaluate the capabilities and limitations of chemometric methods and other mathematical treatments applied on spectroscopic data and more specifically on paint samples. The uniqueness of the spectroscopic data comes from the fact that they are multivariate - a few thousands variables - and highly correlated. Statistical methods are used to study and discriminate samples. A collection of 34 red paint samples was measured by Infrared and Raman spectroscopy. Data pretreatment and variable selection demonstrated that the use of Standard Normal Variate (SNV), together with removal of the noisy variables by a selection of the wavelengths from 650 to 1830 cm−1 and 2730-3600 cm−1, provided the optimal results for infrared analysis. Principal component analysis (PCA) and hierarchical clusters analysis (HCA) were then used as exploratory techniques to provide evidence of structure in the data, cluster, or detect outliers. With the FTIR spectra, the Principal Components (PCs) correspond to binder types and the presence/absence of calcium carbonate. 83% of the total variance is explained by the four first PCs. As for the Raman spectra, we observe six different clusters corresponding to the different pigment compositions when plotting the first two PCs, which account for 37% and 20% respectively of the total variance. In conclusion, the use of chemometrics for the forensic analysis of paints provides a valuable tool for objective decision-making, a reduction of the possible classification errors, and a better efficiency, having robust results with time saving data treatments.

Molecular Modeling Approaches for Determining Gene Function: application to a Putative Poly-A Binding Protein from Leishmania amazonensis (LaPABP)

The great expansion in the number of genome sequencing projects has revealed the importance of computational methods to speed up the characterization of unknown genes. These studies have been improved by the use of three dimensional information from the predicted proteins generated by molecular modeling techniques. In this work, we disclose the structure-function relationship of a gene product from Leishmania amazonensis by applying molecular modeling and bioinformatics techniques. The analyzed sequence encodes a 159 aminoacids polypeptide (estimated 18 kDa) and was denoted LaPABP for its high homology with poly-A binding proteins from trypanosomatids. The domain structure, clustering analysis and a three dimensional model of LaPABP, basically obtained by homology modeling on the structure of the human poly-A binding protein, are described. Based on the analysis of the electrostatic potential mapped on the model's surface and conservation of intramolecular contacts responsible for folding stabilization we hypothesize that this protein may have less avidity to RNA than it's L. major counterpart but still account for a significant functional activity in the parasite. The model obtained will help in the design of mutagenesis experiments aimed to elucidate the mechanism of gene expression in trypanosomatids and serve as a starting point for its exploration as a potential source of targets for a rational chemotherapy.