Recently advances in miniaturization and automation have been utilized to rapidly decrease the time to result for microbiology testing in the clinic. These advances have been made due to the limitations of conventional culture-based microbiology methods, including agar plate and microbroth dilution, which have long turnaround times and require physicians to treat patients empirically with antibiotics before test results are available. Currently, there exist similar limitations in pharmaceutical sterility and bioburden testing, where the long turnaround times associated with standard microbiology testing drive costly inefficiencies in workflows. These include the time lag associated with sterility screening within drug production lines and the warehousing cost and time delays within supply chains during product testing. Herein, we demonstrate a proof-of-concept combination of a rapid microfluidic assay and an efficient cell filtration process that enables a path toward integrating rapid tests directly into pharmaceutical microbiological screening workflows. We demonstrate separation and detection of Escherichia coli directly captured and analyzed from a mammalian (i.e., CHO) cell culture with a 3.0 h incubation. The demonstration is performed using a membrane filtration module that is compatible with sampling from bioreactors, enabling in-line sampling and process monitoring.
Real time determination of gases in the fuel stream of solid oxide fuel cells (SOFCs) is critical for their operation control and low operating costs. Because conventional gas sensors have gas cross-sensitivity problem, other instruments based on traditional detection principles should be utilized for multi-gas analysis. However, the complexity and cost of these instruments prohibit their uses in field-deployed SOFC units. We are developing a new generation of gas sensors, known as multivariable sensors, that have several independent responses for multi-gas detection with a single sensor. Our sensors are fabricated as nanostructures inspired by the iridescence of Morpho butterflies. Our sensor design rules coupled with machine learning tools (e.g. support vector machine, principal components analysis, hierarchical cluster analysis) provide the ability to independently quantify several gases with one sensor. The unobtrusive form factors of these sensors provide further benefits of their future implementations in SOFC systems.
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