Abstract: Chronic diseases are a major healthcare challenge, resulting in soaring cost and reduced economic productivity. Mobile sensors (e.g., handheld, wearables, implant) that can measure analytes from biofluids have the potential to provide cost-effective and enhanced treatments for chronic diseases. Since mobile sensors need to function reliably with minimal human intervention over extended periods of time, the foremost challenge is ensuring data accuracy. To address this important issue, we have proposed two main innovations.
The bipolar junction transistor (BJT) device is proposed and demonstrated as a significantly superior transducer in comparison with the current state-of-the-art field-effect transistor (FET) transducer. The BJT-based sensors are shown to be well suited for mobile sensing applications with inherently simpler calibration, enhanced sensitivity, and low power requirements.
Another source of data inaccuracy is the failure of reference electrodes due to clogging during prolonged incubation in biofluids. We have demonstrated an innovative reference electrode that not only remains unclogged when inserted in a dense solid matric (e.g., tissue, soil, food) but is easier to miniaturize and integrate on a silicon chip.
Hence, these two enhancements address important challenges in mobile healthcare sensing and are also applicable to Internet of Things (IoT) applications such as in situ soil nutrient measurements and food safety monitoring.
Another important healthcare area is early diagnostics. The ability to detect biomolecules at ultralow concentrations requires that the critical dimension of the biosensor is comparable with that of the biomolecule. Since the FET is the only device that can be miniaturized to nanometer scale without losing device performance, silicon nanowire FET sensors are investigated for early disease diagnostic applications. A combined approach of nanofabrication, device simulation, and material studies is applied to demonstrate nanowire (30-nm width) FET sensors that not only have the lowest (~3%) reported sensor-to-sensor variations but also have significantly enhanced sensing characteristics, thus enabling early-stage cancer and other disease diagnostics.