Graduation Year

2024

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Physics

Major Professor

Manh-Huong Phan, Ph.D.

Committee Member

Sarath Witanachchi, Ph.D.

Committee Member

Garrett Matthews, Ph.D.

Committee Member

Trung Le, Ph.D.

Keywords

Covid-19 detection, Healthcare monitors, Magnetic biosensors, Magnetic Nanoparticles, Music respiratory therapy

Abstract

The development of biosensing systems that leverage the unique properties of magnetic nanoparticles and the high sensitivity of magnetic sensors for detecting biomolecules and cancer cells has been an active area of research. However, challenges remain in detecting and differentiating the magnetic signals of biomarkers made from nanoparticles that have similar sizes but distinct magnetic properties. Additionally, overcoming the reduction in detection sensitivity caused by non-magnetic coatings on magnetic nanoparticles remains a key hurdle. Concurrently, there is a pressing need to develop advanced magnetic sensor technologies that can enhance existing healthcare monitoring systems and enable more precise, real-time diagnostics.This dissertation explores the development and application of novel magnetic sensing systems for advanced biodetection and real-time respiratory health monitoring. It integrates magneto-LC resonance (MLCR) sensor technology with a permanent magnet and machine learning to enhance detection capabilities and provide accurate health monitoring solutions. The MLCR sensor demonstrates its ability to differentiate between superparamagnetic (SPM) and ferrimagnetic (FM) nanoparticles, such as iron oxide (Fe3O4) superparticles, based on their crystallite sizes and magnetic properties, making it a promising tool for medical diagnostics and microfluidic biosensing applications. By harnessing the LC-resonance and magneto-impedance effects at high frequencies in the capacitive regime, the study also explores the impact of core-shell nanoparticle structures such as Fe3O4/SiO2, revealing that non-magnetic coatings with dielectric characteristics, such as SiO₂, can significantly enhance the detection sensitivity of the biosensor, contrary to the conventional view that the presence of non-magnetic materials on the surfaces of magnetic nanoparticles substantially reduces the detection sensitivity of traditional biosensors, such as magnetoresistive and magnetoimpedance biosensors. This finding opens new avenues for designing high-performance magnetic biosensing systems, where the optimization of non-magnetic coatings can enhance magnetic detection capabilities and enable the detection and identification of both targeted and unknown biomolecules. In addition, the integration of MLCR and Hall sensors into a magnetic respiratory monitoring system allows for precise, non-contact tracking of various breathing states, such as normal breathing, breath-holding, and deep breathing, while distinguishing COVID-19 patients from healthy individuals based on unique respiratory patterns. With the incorporation of advanced signal processing techniques and machine learning models, the system achieved a 95% accuracy in differentiating COVID-19 patients from healthy controls, highlighting its potential in public health applications and clinical diagnostics. Moreover, the research investigates the therapeutic potential of music on respiration using the developed magnetic respiratory monitoring system, showing that piano music positively affects breathing performance in both healthy subjects and COVID-19 patients. The study shows how music influences respiration across different age groups, highlighting variations in respiratory responses based on age. Based on these findings, the dissertation proposes the development of an AI-enhanced music magnetic respiratory therapy system that offers personalized, non-invasive real-time monitoring and therapeutic interventions, providing a promising tool for improving respiratory health and overall well-being.

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