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Defense of a Ph.D. Dissertation- Rania Oueslati

Rania Oueslati,
Candidate for Doctor of Philosophy
Defense of a Ph.D. Dissertation
Faculty Advisor: Dr. Jayne Wu

Title: 
Development of a smart biodiagnostic platform for point-of-care detection by multiplexed electrokinetic sensing

Abstract:
     Pathogenic infections, foodborne illnesses and drug abuse pose significant public health issues worldwide, and they are of special concern in developing countries, where the medical resources  and  public  hygiene  are  limited.   Therefore, rapid detection and identification of these pathogens are of paramount importance for adopting treatment options and to establish adequate control measures.   The detection and identification of pathogen micro-organisms till rely on complexed methods, which maybe not be suitable for on-site monitoring. Therefore, a great research challenge in this field is focused on the development of rapid, reliable, specific, and sensitive methods to detect these bacteria at low cost.   Moreover, the biochip development for large scale screening analysis implies improved miniaturization, reduction of analysis time and cost, and multianalyte detection.  In addition, simultaneous monitoring of multiple molecular interactions and multiplexed detection of several diagnostic biomarkers at very low concentrations have become important issues in advanced biological and chemical sensing.   This work presents the design of a portable system for sensitive and quantitative detection of DNA and drug biomarkers which will be highly valuable in controlling and preventing disease outbreaks.  First, this work investigates the development of assay protocols for highly sensitive and selective on-site detection of Drugs and bacterial DNA. This is an improvement over the priorily developed AC electrokinetics-based capacitive sensing, which is capable of detecting specific target in a point-of-care setting using micro-fabricated interdigitated electrodes.  Second, this work presents the development of a smart bio-diagnostic platform for point of care detection to interface with capacitive electrokinetic electrodes for a multiplexed sensing.  The circuit has shown good accuracy on various test devices.  In association with the ACEK capacitive sensing, preliminary data were collected and successfully used to characterize physiological samples (10μl).

Monday, November 4, 2019 at 2:00pm

Min H. Kao Electrical Engineering and Computer Science, 434
1520 Middle Drive, Knoxville, TN 37996

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