BSc. (Hons) Forensic Investigation and Analysis
Saoirse Dervin is an Aquasense Marie Curie Early Stage Researcher in the Bendable Electronics and Sensing Technologies (BEST) group at the University of Glasgow. She graduated with a BSc. (Hons) in Forensic Investigation and Analysis from the Institute of Technology Sligo, Ireland in 2014. While studying for her undergraduate degree, Saoirse undertook a number of research projects in functional materials and chemical analysis.
Saoirse is currently working towards the submission of her PhD thesis entitled, “In Vitro activity of graphene and low-dimensional nanomaterials.” Throughout the course of her PhD studies Saoirse acquired extensive experience in the areas of nano-fabrication and nano-characterization and has successfully authored/co-authored 9 publications, including research papers, a review paper and book chapters and contributed in 4 international/national conferences.
Her research interests include the synthesis and characterization of 0, 1 and 2D nanomaterials, biomaterials and their interactions with living organisms, their safety profiles and their potential to contribute towards safeguarding environmental sustainability. She is also interested in the application of these materials as nanotherapeutics, drug delivery systems, biosensors, smart wearable technologies and technologies for environmental monitoring and remediation.
RP6: Pressure and turbidity sensors (ESR 6, UoG)
There could be significant variation in the response of sensors during water quality monitoring as water pressure, slit and mud etc. affect the performance and post calibration challenges. To address this issue, this project will develop pressure and turbidity sensors and integrate them on multi-sensors patch. The interdigitated graphene based highly-sensitive capacitive pressure sensors will be developed on flexible substrate such as PET. The turbidity sensors will be fabricated by using 3D printed packages. The integration of turbidity sensors in the proposed multi-sensors, is advantageous in terms of predicting the selectivity and also for deployment of sensors.
University of Glasgow, United Kingdom