DCU Expo 2024 - Final Year Projects

88 174. Machine LearningUsingMicroparticle-Based Sensor Data for Acetone Detection This study explores the viability of micro particle-based copper sensors as a non-invasive monitoring solution for blood glucose levels in individuals with type 2 diabetes. The investigation focuses on the correlation between acetone levels in exhaled breath and blood sugar levels. The copper sensors underwent testing to assess their capability to detect acetone within human breath. The project’s objective was to identify acetone levels amidst the presence of other common gases (ethanol and water vapour) within human breath. To understand the data, Multiple Regression andmultiple classification machine learning algorithms were utilised for accurate acetone detection. The experimental data served as a train-test dataset for the machine learning algorithms, aiming to achieve a high level of accuracy. Student Programme Biomedical Engineering (Year 5) Project Area Arduino, Biomedical Engineering, Sensor Data Project Technology Excel/VB, Python, Solidworks, Machine Learning, PuTTY Student Name(s) Sorcha Costello Email sorcha.costello32@mail.dcu.ie Supervisor Prof Dermot Brabazon and Dr Anesu Nyabadza 175. Bubble Formation fromMicrotextured Surfaces in Alkaline Electrolysis The objective of this project is to investigate the ideal parameters for laser texturing nickel electrodes to enhance hydrogen production in alkaline electrolysis. This method aims to reduce the bubble formation at the electrode interface to improve the efficiency of green hydrogen generation. The surface morphology of the laser-treatedmicrotextured surfaces is analysed to understand its impact on bubble formation and kinetics. Through experimentation and analysis, the effect of the laser texturing parameters on the electrode surface characteristics is found and their influence on bubble nucleation, growth and detachment in alkaline electrolysis are analysed. Student Programme Mechanical andManufacturing Engineering (Year 5) Project Area Renewable Energy Technology Project Technology Excel/VB, Gamry Student Name(s) Niamh Kilgallen Email niamh.kilgallen3@mail.dcu.ie Supervisor Dr CornéMuilwijk 176. Improving Print Resolution for LowViscosity Bioinks This project investigates the use of the FreeformReversible Embedding of Suspended Hydrogels (FRESH) bioprintingmethod to enhance the print resolution for low-viscosity bioinks (LVBs). LVBs are cell-laden materials with ideal characteristics needed to print viable tissue constructs for use in tissue engineering or regenerative medicine. However, these LVBs cannot be printed freely in the air due to their poor mechanical properties, causing poor filament resolution, and limiting their applications. Using the FRESH method, a gelatin support bath was fabricated to allow the LVBs to be supported within amediumduring the bioprinting process. The gelatin type and concentration used for the fabrication of the support bath were varied in this study to evaluate the effect on the print resolution. Student Programme Biomedical Engineering (Year 5) Project Area Biomedical Engineering Project Technology Excel/VB, ImageJ Student Name(s) Stephen Delaney Email stephen.delaney25@mail.dcu.ie Supervisor Dr Tanya Levingstone