4.7 Article

Geometry-Defined Response Time and Sensitivity for Microneedle-Based Amperometric Sensors

期刊

IEEE SENSORS JOURNAL
卷 23, 期 13, 页码 14285-14294

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2023.3277425

关键词

Sensors; Manganese; Dermis; Enzymes; Delays; Time factors; Sensitivity; Amperometry; microneedle (MN) patch; modeling; response time; sensitivity; wearable and implantable (WI) sensors

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Smart, ultrascaled, always-on wearable (and implantable) sensors are an exciting frontier of modern medicine. Among them, minimally invasive microneedles (MN) are an emerging technology platform for theragnostic applications. Compared to traditional continuous glucose measurement (CGM) devices, these MNs offer painless insertion and simple operation.
Smart, ultrascaled, always-on wearable (and implantable) sensors are an exciting frontier of modern medicine. Among them, minimally invasive microneedles (MN) are an emerging technology platform for theragnostic applications. Compared to traditional continuous glucose measurement (CGM) devices, these MNs offer painless insertion and simple operation. These MN systems, however, rely on analyte diffusion from the interstitial fluid (ISF) to the sensing site, and thus, 1) introduce a substantial and intrinsic diffusion delay in sensor response, and 2) reduce the analyte concentration to which the sensor must respond. A diversity of experimental platforms has been proposed to improve performance, but their optimization relies on empirical iterative approaches. Here, we integrate the theory of transient flux balance and the biomimetic concepts from ion uptake by bacteria to derive a generalized physics-guided model for MN sensors. The framework suggests strategies to minimize response time and maximize extracted analyte concentration in terms of the geometric and physical properties of the system. Our results show that there exists an intrinsic tradeoff between response time and extracted analyte concentration. Our model, validated against numerical simulations and experiment data, offers a predictive design framework that would significantly reduce the optimization time for MN-based sensor platforms.

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