Detection of Magnetic Nanoparticles for Bio-sensing Applications

University of Minnesota Ph.D. dissertation. June 2013. Major: Electrical Engineering. Advisor: Liang Tu. 1 computer file (PDF); xvii, 144 pages.Superparamagnetic Nanoparticles (MNPs) are used as probes to detect biomarkers (protein, DNA, etc.) by using a search coil based scheme for volume detection and by using a Giant Magneto-Resistance (GMR) sensor for surface detection. In search coil detection scheme, a low frequency field is applied to saturate the MNPs and a high frequency field is applied to modulate the nonlinearity of the magnetization into the high frequency region where the noise floor is lower. Under an ac magnetic field, MNPs above certain hydrodynamic size (for Iron Oxide is around 20nm) will experience physical rotation called Brownian relaxation. By studying the phase information of the mixing frequencies, the Brownian relaxation time can be monitored in real time thus dynamic bio-molecular interaction can be recovered. The N�el and Brownian relaxation of MNPs with different magnetic and hydrodynamic properties has been investigated by using a different DC bias field and AC field frequency. The specific response from each MNP can be used as magnetic identification in nano-scale application. A Giant Magneto-Resistance (GMR) sensor array is also used for MNPs detection. Compared with the search coil, GMR sensor is more sensitive but requires surface modification for bio- molecular detection. A low-noise Printed Circuit Board is designed and assembled to implement Wheatstone bridge, multiplexing function, and signal amplification. An AC field is applied to the entire sensor array while an AC current is flowing through a specific sensor. The sensor response will generate mixing frequency terms as the multiplication of field frequency and current frequency. All the active sensors printed with specific capture antibodies are scanned sequentially, recorded in real time, and compared with the reference sensor which is covered by a thick protection layer. Signal to noise ratio for the integrated system is studied by considering the noise contribution from all components