Multiple Particle Positron Emission Particle Tracking and its Application to Flows in Porous Media

Positron emission particle tracking (PEPT) is a method for flow interrogation capable of measurement in opaque systems. In this work a novel method for PEPT is introduced that allows for simultaneous tracking of multiple tracers. This method (M-PEPT) is adapted from optical particle tracking techniques and is designed to track an arbitrary number of positron-emitting tracer-particles entering and leaving the field of view of a detector array. M-PEPT is described, and its applicability is demonstrated for a number of measurements ranging from turbulent shear flow interrogation to cell migration. It is found that this method can locate over 80 particles simultaneously with spatial resolution of order 0.2 mm at tracking frequency of 10 Hz and, at lower particle number densities, can achieve similar spatial resolution at tracking frequency 1000 Hz. The method is limited in its ability to resolve particles approaching close to one another, and suggestions for future improvements are made.M-PEPT is used to study flow in porous media constructed from packing of glass beads of different diameters. Anomalous (i.e. non-Fickian) dispersion of tracers is studied in these systems under the continuous time random walk (CTRW) paradigm. Pore-length transition time distributions are measured, and it is found that in all cases, these distributions indicate the presence of long waiting times between transitions, confirming the central assumption of the CTRW model. All systems demonstrate non-Fickian spreading of tracers at early and intermediate times with a late time recovery of Fickian dispersion, but a clear link between transition time distributions and tracer spreading is not made. Velocity increment statistics are examined, and it is found that temporal velocity increments in the mean-flow direction show a universal scaling. Spatial velocity increments also appear to collapse to a similar form, but there is insufficient data to determine the presence of universal scaling

Multipoint Spectroscopy and Stereoscopic Imaging of Pharmaceutical Particles

Particle and granule properties play a key role in the final product quality of pharmaceuticals. Thus the identification and monitoring of key chemical and physical parameters is essential in the production of pharmaceuticals. The existing off-line methods are generally slow and labour intensive. Near infra-red (NIR) multipoint spectroscopy and image analysis are an attractive alternative compared to the traditional methods because they are both nondestructive and non-interfering allowing the analysis in real time of particles physical and chemical properties. This research is a preliminary study performed at laboratory scale and aims at developing chemometric and imaging algorithms for real time measuring of pharmaceutical chemical and physical properties. These algorithms utilised real time NIR multipoint spectroscopy and a novel imaging system. NIR multipoint spectroscopy followed by a regression technique (such as PLS) was used to build calibration models to quantify a compound in a small size binary granule mixture under both static and dynamic conditions. The imaging technology provided key physical properties such as size, shape and texture. The Haralick correlation property and the variogram were used to analyse the surface texture of particles. These algorithms allowed the classification of particles by their morphological nature under both static and dynamic conditions

Doctor of Philosophy

dissertationAnalyte-detecting sensors have been developed in many fields. Chemical sensors, and especially biomedical sensors, deserve special attention because they can simplify time-consuming, costly and site-limited medical procedures. Sensor efficiency depends on its analyte sensing material, signal transducing device and data processing system. The biggest barrier to devise biomedical sensors is the development of analyte sensing material with a high selectivity for target molecules. The motivation of this research was to develop biomolecule-sensitive polymers that can be used in biomedical sensors. Thus, this thesis covers all stages of chemical sensor development, from developing target analyte sensitive materials to merging the developed materials with a signal transducing system. First, the potential application of a zwitterionic glucose-responsive hydrogel as a body implantable continuous glucose monitoring system was examined. After using thermodynamics to confirm the glucose sensing mechanism, synthesis of the hydrogels was optimized and analyzed using statistical methods (design of experiments (DOE)). Thermodynamics study showed that mixing contribution was an important factor to glucose selectivity as well as elastic contribution. By the DOE study, we confirmed that sensitivity of the hydrogels was determined by the molar ratio of cationic and anionic functional groups, and response time depended on the amount of cross-linker. A hydrogel degradation study was also performed to determine the effect of gamma ray sterilization and neutron irradiations on the hydrogel cross-linking network for biomedical applications. Results showed that gamma ray affected cross-linking networks of UV cured hydrogels. However, the neutron irradiation effect was not considerable. In addition, ferromagnetic particles-embedded, zwitterionic glucose-responsive hydrogels were developed to enable the response processing by a magnetoresistive transducer. The hydrogel with horizontally aligned ferromagnetic particles showed good sensitivity in the physiological glucose range (~10mM). Moreover, response time was reduced by almost seven-fold with twice thicker samples (800 um) than samples (400 um) with a pressure sensor measurement. A second project optimized the synthesis of a glutathione (GSH)-sensitive polymer. The selectivity of the polymer for GSH was improved by synthesizing a GSHimprinted polymer and adopting a cobalt ion-mediated chelating binding structure as analyte binding sites

ROLE OF SOLUBLE AND INSOLUBLE POLYSACCHARIDES IN OMNIVORE AND CARNIVORES NUTRITION

Present PhD thesis was realised at the Department of Veterinary Medicine and Animal Production (University of Napoli, Federico II, Italia) in order to study the functional properties of dietary fiber fractions (soluble and insoluble) in nutrition of different mono-stomached species. With this aim more than 30 different feedstuffs were evaluated using several analytic techniques (Weende, Van Soest and Prosky) (Chapter 2). Thereafter seems interesting to underline that main of the tested substrates are by-products and could represent economical and readily available nutrient sources. Moreover, in order define the functional activities and the potential health benefits some of these feedstuffs were tested also in vitro using the cumulative gas production technique (IVGPT). Tree different trials (Chapters 3 - 4 - 5) of IVGPT were carried out using as inoculum faeces from adult dog, swine and cat, respectively. The comparison of chemical data and fermentation parameters was important to better define the potential functionality of each tested substrate in order to identify new functional ingredients. The obtained results offer a good perspectives for the possibility of using several less known materials (e.g. chestnuts, citrus by-products, legume and fruit fibers) in the formulation of diets. The study evidenced also the specific carbohydrates composition of ingredients, such as seeds of pshyllium, line and hemp, usually utilized only for their fatty acids profile. Each tested feedstuff presented specific characteristics, which allow it to be useful in specific stage of life of omnivores and carnivores species, including human

Modified Dextran Polymers for Drug Delivery

A collaboration between WPI and ENSIC was undertaken to investigate the properties of emulsions as a drug delivery system. Biocompatible amphiphilic polymers are used as emulsifiers for controlled drug delivery. They act as a barrier between phases in oil-in-water emulsions to increase stability. Oil soluble drugs can be encapsulated within the oil nanoparticles and released into a biological system. The goal of this project was to study modified dextran (DexC6), an amphiphilic polymer. The most stable emulsions were formed with a DexC6 aqueous concentration of 40g/L in a system of 40% octyldodecanol oil volume. Drug release kinetic experiments showed that encapsulated lidocaine is released at a slower rate than free lidocaine. Further research in emulsion drug delivery is recommended

Detecting Heart Attacks Using Learning Classifiers

Cardiovascular diseases (CVDs) have emerged as a critical global threat to human life. The diagnosis of these diseases presents a complex challenge, particularly for inexperienced doctors, as their symptoms can be mistaken for signs of aging or similar conditions. Early detection of heart disease can help prevent heart failure, making it crucial to develop effective diagnostic techniques. Machine Learning (ML) techniques have gained popularity among researchers for identifying new patients based on past data. While various forecasting techniques have been applied to different medical datasets, accurate detection of heart attacks in a timely manner remains elusive. This article presents a comprehensive comparative analysis of various ML techniques, including Decision Tree, Support Vector Machines, Random Forest, Extreme Gradient Boosting (XGBoost), Adaptive Boosting, Multilayer Perceptron, Gradient Boosting, K-Nearest Neighbor, and Logistic Regression. These classifiers are implemented and evaluated in Python using data from over 300 patients obtained from the Kaggle cardiovascular repository in CSV format. The classifiers categorize patients into two groups: those with a heart attack and those without. Performance evaluation metrics such as recall, precision, accuracy, and the F1-measure are employed to assess the classifiers’ effectiveness. The results of this study highlight XGBoost classifier as a promising tool in the medical domain for accurate diagnosis, demonstrating the highest predictive accuracy (95.082%) with a calculation time of (0.07995 sec) on the dataset compared to other classifiers

Micro/Nano Devices for Blood Analysis, Volume II

The development of micro- and nanodevices for blood analysis continues to be a growing interdisciplinary subject that demands the careful integration of different research fields. Following the success of the book “Micro/Nano Devices for Blood Analysis”, we invited more authors from the scientific community to participate in and submit their research for a second volume. Researchers from different areas and backgrounds cooperated actively and submitted high-quality research, focusing on the latest advances and challenges in micro- and nanodevices for diagnostics and blood analysis; micro- and nanofluidics; technologies for flow visualization and diagnosis; biochips, organ-on-a-chip and lab-on-a-chip devices; and their applications to research and industry