Deep Learning and Optimised Nanoplasmonic Sensors for Label-free Biomedical Applications

Extracellular vesicles (EVs) are nanometric lipid-enclosed packages released by all leaving cells. Recently, they have been exploited as liquid biopsy biomarkers for various diseases. In theory, an ultra-sensitive biosensor should be able to detect and identify them from a liquid biopsy sample. Surface Enhanced Raman Spectroscopy (SERS) is an ideal biosensor candidate for the investigation of biological and chemical species, especially when dealing with small volumes or low concentrations of the investigated sample. Usually, SERS are used for a small molecules investigation as their Raman enhancement is associated with nano-scale geometrical features on their surface (known as a hotspot). These hotspots are in the order of a few nanometers, and they are too small for the EVs (size between 30-150nm) to perfectly fit in. Therefore, they need to be extensively optimized to produce large enough hotspots and enhancement factors to be suited for EV-related research. 1. Semi-analytical and numerical approach for the investigation of plasmonic nanostructures: I have developed two methods for investigating plasmonic nanoparticles, including a semi-analytical approach and a fully numerical one. Both methods use spatial isomorphism to deal with curved boundaries and introduce an unconditionally stable first-order geometrically accurate meshing scheme for the finite difference time domain (FDTD) method. 2. Fabrication of optimized plasmonic nanostructures: Mentioned numerical methods are then used for the investigation of plasmonic nanoparticles in a curved substrate. The investigated and optimized geometries are then fabricated using two different methods including combined nanoparticle and soft lithography (bottom to top) and direct writing of the structure using femtosecond laser machining (top to bottom). 3. Deep learning for the direct classification and processing of the raw Raman signal: The fabricated plasmonic surfaces are then used as SERS substrates for EV characterization. Due to the lack of chromophore molecules in EVs, they produce very weak Raman signals even with surface enhancement of their Raman signal. These signals require extensive unbiased pre-processing before being fed to the automated classification techniques. To address this issue, I have developed two deep learning techniques capable of direct and accurate classification and processing of the raw Raman signal of EVs.

Name thesis:degree_name

PhD

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor dc:publisher

ResearchSpace@Auckland

Year dc:date.issued

2022

Author dc:creator

• Kazemzadeh, Mohammadrahim

Advisors dc:contributor.advisor

• Xu, Peter
• Broderick, Neil
• Zargar, Kamran

Statement dc:rights

• Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Licence dc:rights.uri

• https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm

Handle dc:identifier.uri

https://hdl.handle.net/2292/63883

OAI identifier oai:identifier

oai:researchspace.auckland.ac.nz:2292/63883