Capillary electrophoresis with boron doped diamond electrode for amperometric detection of low molecular weight biological substrate

The research work in this thesis included the sensitive and selective separation of biological substance by capillary electrophoresis with a boron doped diamond electrode for amperometric detection. Chapter 1 introduced the capillary electrophoresis and electrochemical detection. It included the different modes of capillary electrophoresis, polyelectrolyte multilayers coating for open tubular capillary electrochromatography, different modes of electrochemical detection and carbon based electrodes. Chapter 2 showed the synthesized and electropolymerized N-acetyltyramine with a negatively charged sulfobutylether-β-cyclodextrin on a boron doped diamond (BDD) electrode followed by the electropolymerzation of pyrrole to form a stable and permselective film for selective dopamine detection. For comparison, a glassy carbon (GC) electrode with a combined electropolymerized permselective film of polytyramine and polypyrrole-1-propionic acid was used for selective detection of dopamine. The detection limit of dopamine was improved from 100 nM at a GC electrode to 5 nM at a BDD electrode. Chapter 3 showed field-amplified sample stacking using a fused silica capillary coated with gold nanoparticles embedded in poly(diallyldimethylammonium) chloride, which has been investigated for the electrophoretic separation of indoxyl sulphate, homovanillic acid and vanillylmandelic acid. The detection limit of the three analytes obtained by using a boron doped diamond electrode was around 75 nM, which was significantly below their normal physiological levels in biological fluids. This combined separation and detection scheme was applied to the direct analysis of these analytes and other interfereing chemicals including uric and ascorbic acids in urine samples without off-line sample treatment or preconcentration. Chapter 4 showed the selective detection of Pseudomonas Quinolone Signal, PQS for quorum sensing from its precursor HHQ, using a simply boron doped diamond electrode. Furthermore, by combining poly(diallyldimethylammonium) chloride modified fused silica capillary with a BDD electrode for amperometric detection, PQS was separated from HHQ and other analogues. The detection limit of PQS was as low as 65 nM. Different P. aeruginosa mutant strains were studied. Chapter 5 showed the separation of aminothiols by layer-by-layer coating of silica capillary with a boron doped diamond electrode. The capillary was layer-by-layer coated with the polycation poly(diallyldimethylammonium) chloride and negatively charged silica nanoparticles. All the aminothiols was separated and detected using a BDD electrode in an acidic electrolyte. It was a novel scheme for the separation and detection of glutathione reduced and oxidized forms, which is important for estimated overstressed level in the human system.

Automatic detection of learner-style for adaptive eLearning

The advent of modern wireless technologies has seen a shift in focus towards the design and development of educational systems for deployment through mobile devices. The use of mobile phones, tablets and Personal Digital Assistants (PDAs) is steadily growing across the educational sector as a whole. Mobile learning (mLearning) systems developed for deployment on such devices hold great significance for the future of education. However, mLearning systems must be built around the particular learner’s needs based on both their motivation to learn and subsequent learning outcomes. This thesis investigates how biometric technologies, in particular accelerometer and eye-tracking technologies, could effectively be employed within the development of mobile learning systems to facilitate the needs of individual learners. The creation of personalised learning environments must enable the achievement of improved learning outcomes for users, particularly at an individual level. Therefore consideration is given to individual learning-style differences within the electronic learning (eLearning) space. The overall area of eLearning is considered and areas such as biometric technology and educational psychology are explored for the development of personalised educational systems. This thesis explains the basis of the author’s hypotheses and presents the results of several studies carried out throughout the PhD research period. These results show that both accelerometer and eye-tracking technologies can be employed as an Human Computer Interaction (HCI) method in the detection of student learning-styles to facilitate the provision of automatically adapted eLearning spaces. Finally the author provides recommendations for developers in the creation of adaptive mobile learning systems through the employment of biometric technology as a user interaction tool within mLearning applications. Further research paths are identified and a roadmap for future of research in this area is defined.