Qualification Type: | PhD |
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Location: | Swansea |
Funding for: | UK Students |
Funding amount: | £19,237 for 2024/25 |
Hours: | Full Time |
Placed On: | 19th December 2024 |
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Closes: | 13th January 2025 |
Reference: | RS744 |
Cells are highly sensitive to environmental changes, with shifts in temperature, pH, or other factors altering their morphology and behavior. This project involves designing, fabricating, and testing microfluidic fibers containing cells as “living sensors” that respond to external stimuli like drugs, electric signals, mechanical forces, and temperature changes. The work aims to advance applications in diagnostic healthcare and drug development.
The candidate will design fibres having controlled cells spacing, by using the principle of viscoelasticity-induced ordering in straight microchannels (https://pubs.acs.org/doi/full/10.1021/acsaenm.2c00060). The advantage over traditional methodologies is that cells will be aligned along a single line in the fibre, meaning that the external stimuli will be uniformly felt along the cell population line, resulting in the first-of-its-kind living tuneable sensor with cell-specific response. Unit sensors will be robustly characterised. Data will train a machine learning model to optimise sensor configurations (for multiple unit sensors) for a given application. The project will bring together Soft Matter, Biomedical Engineering and Data Science to generate a versatile tool with great potential across several fields. Experimental activities will mainly be carried out at the Rheological Microfluidic lab led by Dr. Francesco Del Giudice.
The candidate will use a variety of equipment, including microfluidic fabrication facilities, microfluidic stations to observe the flow and generate the fibres, and state-of-the-art rheometry. The candidate will also have access to a range of advanced biomechanical characterisation tools to test sensor applicability (e.g. test performance in biomaterial phantoms) and benchmark sensor quality (e.g. compare strain measurements against optical methods such as digital image correlation). Additionally, the candidate will be trained on the development of machine learning algorithms developing advanced skills in both experimental and analytical methods. Collaborating research groups and stakeholders from across disciplines in healthcare and industry will regularly engage throughout. By the end of the project, the candidate will have acquired a portfolio of skills and external collaborators that will provide a strong footing for future careers in either academia or industry.
The Rheological Microfluidic lab sits within the broader Complex Fluid group and focuses on areas of research bringing together complex fluids (e.g., polymer solutions) and microfluidics. For instance, we pioneered the use of polymer solutions to promote co-encapsulation of particles above the stochastic limit. We also developed a microfluidic device for rapid simultaneous measurements of rheological properties at different temperature and using fingerpick of fluids. We are currently exploring implementation of machine learning within the field of droplet microfluidics. Our overall vision is to introduce disruptive technologies that challenges the status quo.
The student will also work within the Biomedical Engineering Simulation and Testing (BEST) Lab led by Dr Hari Arora. There are currently >20 researchers in the group with >10 PhD level working on advanced experimental and computational mechanics problems. A relevant area of focus within the group includes the development of novel measurement methods to study medical devices and suitable simulated environments for biomechanical testing. There is a wide range of expertise within the BEST Lab to support on specialist topics as well as interdisciplinary skills development of the successful candidate.
Funding Details
Funding Comment
This scholarship covers the full cost of tuition fees and an annual stipend at UKRI rate (currently £19,237 for 2024/25).
Additional research expenses of up to £1,000 per year will also be available.
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