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

材料科学 间质液 原位 生物医学工程 可穿戴计算机 自愈水凝胶 持续监测 信号(编程语言) 计算机科学 嵌入式系统 病理 医学 气象学 经济 高分子化学 程序设计语言 物理 运营管理
作者
Yumin Dai,James K. Nolan,Emilee Madsen,Marco Fratus,Junsang Lee,Jinyuan Zhang,Jongcheon Lim,Seokkyoon Hong,Muhammad A. Alam,Jacqueline C. Linnes,Hyowon Lee,Chi Hwan Lee
出处
期刊:ACS Applied Materials & Interfaces [American Chemical Society]
卷期号:15 (49): 56760-56773 被引量:29
标识
DOI:10.1021/acsami.3c12740
摘要

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.
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