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Updated: Jul 8, 2025

Hollow Microneedle-based Sensor for Multiplexed Transdermal Electrochemical Sensing
Published on: June 1, 2012
Performance gain and electro-mechanical design optimization of microneedles for wearable sensor systems
Marco Fratus1, Muhammad Ashraful Alam2
1Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, 47906, Indiana, USA. mfratus@purdue.edu.
Abstract:
Minimally invasive microneedle (MN) is an emerging technology platform for wearable and implantable diagnostics and therapeutics systems. These short MNs offer pain-free insertion and simple operation. Among the MN technologies proposed to enhance interstitial fluid (ISF) extraction, porous and swellable (P-S) hydrogels absorb analyte molecules across the entire lateral surface. Currently, the design, development, and optimization of the MNs rely on empirical, iterative approaches. Based on theory of fluid flow and analyte diffusion through geometrically complex biomimetic systems, here we derive a generalized physics-guided model for P-S MN sensors. The framework (a) quantifies MN extracting efficiency [Formula: see text] in terms of its geometric and physical properties, and (b) suggests strategies to optimize sensor response while satisfying the mechanical constraints related to various skin-types (e.g., mouse, pig, humans, etc.). Our results show that, despite the differences in geometry and composition, P-S MNs obey a universal scaling response, [Formula: see text] with [Formula: see text] being MN length, diffusivity, and radius, respectively, and [Formula: see text], [Formula: see text] and [Formula: see text] are the ratio between approximate vs. exact analytical solutions, the effective biofluid transfer coefficient between dermis and skin, and the exponent for the power-law approximation, respectively. These parameters quantify the biomolecule transfer through the dermis-to-MN interface at different scaling limits. P-S MNs outperform hollow MNs by a 2-6x enhancement factor; however, the buckling-limit of insertion defines the maximized functionality of the sensor. Our model, validated against experimental results and numerical simulations, offers a predictive design framework to significantly reduce the optimization time for P-S MN-based sensor platforms.
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