Theory of nanostructured sensors integrated in/on microneedles for diagnostics and therapy.

Efficient real-time diagnostics and on-demand drug delivery are essential components in modern healthcare, especially for managing chronic diseases. The lack of a rapid and effective sensing and therapeutic system can result in analyte level deviations, leading to severe complications. Minimally invasive microneedle (MN)-based patches integrating nanostructures (NSs) in their volume or on their surface have emerged as a biocompatible technology for delay-free analyte sensing and therapy. However, a quantitative relationship for the signal response in NS-assisted reactions remains elusive. Existing generalized formalisms are derived for in-vitro applications, raising questions about their direct applicability to in-situ wearable sensors. In this study, we apply the reaction-diffusion theory to establish a generalized physics-guided framework for NS-in-MN platforms in wearable applications. The model relates the signal response to analyte concentration, incorporating geometric, physical, and catalytic platform properties. Approximating the model under NS (binding or catalytic) and environmental (mass transport) limitations, we validate it against numerical simulations and various experimental results from diverse conditions - analyte sensing (glucose, lactic acid, pyocyanin, miRNA, etc.) in artificial and in-vivo environments (humans, mice, pigs, plants, etc.) through electrochemical and optical/colorimetric, enzymatic and non-enzymatic platforms. The results plotted in the scaled response show that (a) NS-limited platforms exhibit a linear dependence, (b) Mass transport-limited platforms saturate to 1, (c) a one-to-one mapping against traditional sensitivity plots unifies the scattered data points reported in literature. The universality of the model provides insightful perspectives for the design and optimization of MN-based sensing technologies, with potential extensions to dissolvable MNs as part of analyte-responsive closed-loop therapeutic applications.

Wearable Sensor Patch with Hydrogel Microneedles for In Situ Analysis of Interstitial Fluid.

Continuous real-time monitoring of biomarkers in interstitial fluid is essential for tracking metabolic changes and facilitating the early detection and management of chronic diseases such as diabetes. However, developing minimally invasive sensors for the in situ analysis of interstitial fluid and addressing signal delays remain a challenge. Here, we introduce a wearable sensor patch incorporating hydrogel microneedles for rapid, minimally invasive collection of interstitial fluid from the skin while simultaneously measuring biomarker levels in situ. The sensor patch is stretchable to accommodate the swelling of the hydrogel microneedles upon extracting interstitial fluid and adapts to skin deformation during measurements, ensuring consistent sensing performance in detecting model biomarker concentrations, such as glucose and lactate, in a mouse model. The sensor patch exhibits in vitro sensitivities of 0.024 ± 0.002 µA mM-1 for glucose and 0.0030 ± 0.0004 µA mM-1 for lactate, with corresponding linear ranges of 0.1-3 and 0.1-12 mM, respectively. For in vivo glucose sensing, the sensor patch demonstrates a sensitivity of 0.020 ± 0.001 µA mM-1 and a detection range of 1-8 mM. By integrating a predictive model, the sensor patch can analyze and compensate for signal delays, improving calibration reliability and providing guidance for potential optimization in sensing performance. The sensor patch is expected to serve as a minimally invasive platform for the in situ analysis of multiple biomarkers in interstitial fluid, offering a promising solution for continuous health monitoring and disease management.

Performance gain and electro-mechanical design optimization of microneedles for wearable sensor systems.

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.

Modeling, design guidelines, and detection limits of self-powered enzymatic biofuel cell-based sensors.

Enzymatic biofuel cell (EBFC)-based self-powered biochemical sensors obviate the need for external power sources thus enabling device miniaturization. While recent efforts driven by experimentalists illustrate the potential of EBFC-based sensors for real-time monitoring of physiologically relevant biochemicals, a robust mathematical model that quantifies the contributions of sensor components and empowers experimentalists to predict sensor performance is missing. In this paper, we provide an elegant yet simple equivalent circuit model that captures the complex, three-dimensional interplay among coupled catalytic redox reactions occurring in an EBFC-based sensor and predicts its output signal with high correlations to experimental observations. The model explains the trade-off among chemical design parameters such as the surface density of enzymes, various reaction constants as well as electrical parameters in the Butler-Volmer relationship. The model shows that the linear dynamic range and sensitivity of the EBFC-based sensor can be independently fine-tuned by changing the surface density of enzymes and electron mediators at the anode and by enhancing reductant concentrations at the cathode. The mathematical model derived in this work can be easily adapted to understand a wide range of two-electrode systems, including sensors, fuel cells, and energy storage devices.