ArticleBiomedical microdevices2023
Performance gain and electro-mechanical design optimization of microneedles for wearable sensor systems.
Article in Biomedical microdevices, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 8 citations in OpenAlex.
- A closed-loop microneedle-integrated physiological model for predictive glycemic management.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Can we still use the Michaelis-Menten model for enzymatic microneedle sensors?Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Enhancing melanoma therapy with hydrogel microneedles.Frontiers in oncology · 2025Review
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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