The development of a chip-based sensor array composed of individually addressable polystyrene-polyethylene glycol and agarose microspheres has been demonstrated. The microspheres are selectively arranged in micromachined cavities localized on silicon wafers. These cavities are created with an anisotropic etch and serve as miniaturized reaction vessels and analysis chambers. The cavities possess pyramidal pit shapes with trans-wafer openings that allow for both fluid flow through the microreactors/analysis chambers as well optical access to the chemically sensitive microspheres. Identification and quantification of analytes occurs via colorimetric and fluorescence changes to receptor and indicator molecules that are covalently attached to termination sites on the polymeric microspheres. Spectral data is extracted from the array efficiently using a charge-coupled device (CCD) allowing for the near-real-time digital analysis of complex fluids. The power and utility of this new microbead array detection methodology is demonstrated here for the analysis of complex fluids containing a variety of important classes of analytes including acids, bases, metal cations, sugars and antibody reagents. The application of artificial neural network analyses to the microbead array is demonstrated in the context of pH measurements. To assess the utility of the analysis and gain an understanding of the molecular level design of the sensor, parameters such as the choice of the indicator dyes, array size, data pre-processing techniques, as well as different network types and architectures were evaluated. Additionally, the development of miniaturized chromatographic systems localized within individual polymer microspheres and their incorporation into an array is reported. The integrated chromatographic and detection concept is based on the creation of distinct functional layers within the microspheres. Such beads have been incorporated into the array platform and used for speciation and concentration determination of aqueous metal cation solutions. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/29819 |
| Date | 13 May 2015 |
| Creators | Goodey, Adrian Paul |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | electronic |
| Rights | Copyright is held by the author. Presentation of this material on the Libraries' web site by University Libraries, The University of Texas at Austin was made possible under a limited license grant from the author who has retained all copyrights in the works., Restricted |
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