Microfluidic devices fabricated via soft lithography have demonstrated compelling applications such as lab-on-a-chip diagnostics, DNA microarrays, and cell-based assays. These technologies could be further developed by directly integrating microfluidics with electronic sensors and curvilinear substrates as well as improved automation for higher throughput. Current additive manufacturing methods, such as stereolithography and multi-jet printing, tend to contaminate substrates with uncured resins or supporting materials during printing. Here, we present a printing methodology based on precisely extruding viscoelastic inks into self-supporting microchannels and chambers without requiring sacrificial materials. We demonstrate that, in the submillimeter regime, the yield strength of the as-extruded silicone ink is sufficient to prevent creep within a certain angular range. Printing toolpaths are specifically designed to realize leakage-free connections between channels and chambers, T-shaped intersections, and overlapping channels. The self-supporting microfluidic structures enable the automatable fabrication of multifunctional devices, including multimaterial mixers, microfluidic-integrated sensors, automation components, and 3D microfluidics.
A cross-reactive array of semiselective chemiresistive sensors made of polymer-graphene nanoplatelet (GNP) composite coated electrodes was examined for detection and discrimination of chemical warfare agents (CWA). The arrays employ a set of chemically diverse polymers to generate a unique response signature for multiple CWA simulants and background interferents. The developed sensors' signal remains consistent after repeated exposures to multiple analytes for up to 5 days with a similar signal magnitude across different replicate sensors with the same polymer-GNP coating. An array of 12 sensors each coated with a different polymer-GNP mixture was exposed 100 times to a cycle of single analyte vapors consisting of 5 chemically similar CWA simulants and 8 common background interferents. The collected data was vector normalized to reduce concentration dependency, z-scored to account for baseline drift and signal-to-noise ratio, and Kalman filtered to reduce noise. The processed data was dimensionally reduced with principal component analysis and analyzed with four different machine learning algorithms to evaluate discrimination capabilities. For 5 similarly structured CWA simulants alone 100% classification accuracy was achieved. For all analytes tested 99% classification accuracy was achieved demonstrating the CWA discrimination capabilities of the developed system. The novel sensor fabrication methods and data processing techniques are attractive for development of sensor platforms for discrimination of CWA and other classes of chemical vapors.
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