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Title: Design and implementation of a microstrip filter biosensor for healthcare applications
Authors: Ashelaish, Hayder Qassem Mashri
Issue Date: 2019
Publisher: Newcastle University
Abstract: The aim of this research was to develop high-frequency biosensors by a combination of traditional microstrip filters and microfluidics. Lowpass and bandpass microstrip filters were designed for operational frequencies less than 3 GHz. Analytical modelling was used to initially determine microstrip filter geometry and then 3D electromagnetic simulation software utilised to examine their performance. Once the design was optimised, devices were fabricated using traditional PCB manufacturing approaches and clean room evaporation techniques. The fabricated filters were compared with the simulation results. The characteristic filter features at 0.66 GHz, 0.80 GHz, and 1.60 GHz demonstrated good agreement to within 90% of the simulated models. Microfluidic reservoirs were then attached to the microstrip filters prior to biological testing. The targeted biomolecules for detection were prostate specific antigen (PSA). A vector network analyser was used to measure the S-parameters of the filters at each stage of functionalisation and immobilisation. Biosensor performance was assessed by measurement of the resonant amplitude and frequency shifts at the characteristic operational frequencies as a function of concentration of the immobilised PSA. The efficacy test of the produced biosensors demonstrated label-free detection down to a minimum analyte concentration of 6.125 ng/ml, this corresponding to an amplitude change of 9 dB and a frequency shift of 10 MHz in the characteristic feature of the S11 signal. This work has demonstrated the applicability of both lowpass and bandpass microstrip filters, with an operational frequency range less than 3 GHz and with suitably integrated microfluidics, to perform as biosensors. This is the first experimental assessment report of this type of radio frequency-based biosensor showing the real-time detection of PSA biomarkers.
Description: PhD Thesis
Appears in Collections:School of Engineering

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