The objective of this dissertation is to improve the performance of surface acoustic wave (SAW) biosensors for use in point-of-care-testing (POCT) applications. SAW biosensors have the ability to perform fast, accurate detection of an analyte in real time without the use of labels. However, the technology suffers from the inability to differentiate between specific and non-specific binding. Due to this limitation, direct testing of bodily fluids using SAW sensors to accurately determine an analyte's concentration is difficult. In addition, these sensors are challenged by the need to detect small concentrations of a biomarker that are typically required to give a clinical diagnosis. Sensitivity, selectivity and reliability are three critical aspects for any sensing platform. To improve sensitivity the delay path of a SAW sensor has been modified with microcavities filled with various materials. These filled cavities increased sensitivity by confining wave energy to the surface by way of constructive interference and waveguiding. Thus, the improved sensitivity will result in a lower limit of detection. In addition, insertion loss is decreased as a consequence of increased wave confinement to the surface. Sensor selectivity and reliability are adversely affected by non-specific binding of unwanted species present in a sample. To address this issue a multifunctional SAW sensor is presented. The sensor consists of two SAW delay lines oriented orthogonal to each on ST-quartz in order to generate two distinct wave modes. One wave mode is used for sensing while the other is used to remove loosely bound material. By using the same transduction mechanism for both removal and sensing, the sensor chip is simplified and complex electronics are avoided. The findings of this research involve the technological advances for SAW biosensors that make their use in POCT possible.
| Identifer | oai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-6762 |
| Date | 01 January 2014 |
| Creators | Richardson, Mandek |
| Publisher | Scholar Commons |
| Source Sets | University of South Flordia |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate Theses and Dissertations |
| Rights | default |
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