Novel Portable Sensors and Techniques for Improving On-Site Water Quality and Environmental Monitoring

Recent research advances have improved techniques and devices, both standalone and smartphone-integrated, in the hope of achieving traditional analytical instrument-grade quality in a portable format, but the broad applicability of this work in many resource-limited settings is still a challenge due to inherent limitations in platform sensitivity, environmental interference, and assay versatility. This dissertation encompasses a series of four research projects designed for answering the challenges of portable environmental monitoring for resource-limited settings. First, we investigated control measures for existing portable environmental sensing techniques for heavy metal, organic molecular, inorganic compound, and biological targets. Initially, we characterized assay performance across diverse intensity-based detection methods and under diverse lighting and imaging conditions. Subsequently, we implemented lighting and imaging control measures and compared assay performance under each set of conditions. Following these analyses, we investigated the applications of three novel sensing methods. The second project encompasses our investigations of a transducer mechanism relating pathogen concentration to capillary flow rate based on differing interfacial/rheological-properties of target and antibody-conjugated particle samples. Capillary flow models were constructed and validated with these assay results to characterize the underlying operational mechanisms. The third project details a novel fluorescence-based assay for identifying and quantifying bisphenol A, an emerging environmental contaminant and endocrine disrupting compound, from treated effluent sample models. Additionally, this method was demonstrated on two smartphone platforms for device performance comparison. The fourth and final project covers particle-scattering based detection of heavy metal ions based on differing adsorption to carboxyl groups on different size particles detection at different angles. This work included the development of multivariate classification models by statistical inference methods, which allowed distinction between some heavy metal ion species. These four projects offer but a few directions which may prove valuable toward developing versatile sensing methods for resource-limited settings