Portable Device Based Optical Sensors For Water Related Environmental Monitoring
Publisher
The University of Arizona.Rights
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.Embargo
Release after 08/16/2020Abstract
Demand for field-usable analytical devices is growing in many areas, including medical diagnosis, food safety, and environmental monitoring. There is commercially available equipment but they are not intended for use in the field or at home because of high prices and large size. To overcome these shortcomings, portable devices are needed that are easy to fabricate, low-cost, user-friendly and sufficiently sensitive. The spread of smartphones and the development of microcontrollers have the potential to be used as portable diagnostic devices. This dissertation includes a series of three research articles and one review article that is aimed at developing portable water related environmental monitoring devices. The first project focused on developing a biosensor for the immediate detection of norovirus in a water sample. We visualized norovirus directly on paper microfluidics chip through the addition of antibody-conjugated submicron fluorescent particles using smartphone-based fluorescence microscope. This method allows for the diagnosis of virus close to a single virus particle level in a short amount of time. The second project demonstrates an optical sensor used to evaluate normalized difference vegetation index (NDVI) from plant leaves using a smartphone camera and 800 nm high-pass filter. NDVI values were correlated with chlorophyll concentration and water content and allowed to predict the health of plants. The third project details an optical sensor for distinguishing the type and origin of different oils. A Raspberry Pi and camera were used to collect capillary flow rate on paper chip. Depending on the composition and viscosity of each oil, the flow pattern differed and made it possible to distinguish between samples using principal component analysis (PCA) and the support vector machine classification algorithm (SVM). The review article introduces capillary flow dynamics-based method using microfluidic paper-based analytic device which has the potential for future applications towards point-of-care diagnostics and field applications. These articles present some of the directions for developing portable sensors for water related environmental monitoring.Type
textElectronic Dissertation
Degree Name
Ph.D.Degree Level
doctoralDegree Program
Graduate CollegeBiosystems Engineering