A stability‐centered framework is established to systematically untangle the multi‐dimensional failure mechanisms across four primary sensing modalities, including ion‐selective potentiometric, enzymatic, nanozyme‐based, and affinity‐based sensors.
Abstract
Wearable sweat electrochemical sensors enable continuous and noninvasive monitoring of electrolytes, metabolites, and stress‐related biomarkers for personalized healthcare. However, the translation of these platforms from proof‐of‐concept prototypes to reliable clinical tools is hindered by a critical bottleneck of long‐term operational stability. In realistic wearable scenarios, complex biofouling, fluctuating microenvironments, and dynamic physical deformations collectively trigger degradation pathways that severely compromise signal fidelity. In this review, we establish a stability‐centered framework to systematically untangle the multi‐dimensional failure mechanisms across four primary sensing modalities, including ion‐selective potentiometric, enzymatic, nanozyme‐based, and affinity‐based sensors. State‐of‐the‐art mitigation strategies are then discussed, encompassing materials engineering, device architecture, and computational calibration. Furthermore, critical challenges and prospects encompassing AI‐driven material discovery, continuous sampling, and edge‐computing‐assisted analytics are introduced, serving as a timely resource to accelerate the translation of wearable electrochemical sensors into reliable platforms for comprehensive health management.
Functionalized design is presented as an application‐backward, cross‐scale framework that links clinical needs and biomarker–matrix constraints with recognition chemistry, biointerfaces, functional materials, transduction architectures, calibration, data interpretation, manufacturability, and validation.
Ya-Nan Li, Yang Zhou, Meng Yang et al.· Advanced Materials & Technol...· 0 citations
Mental health is a pressing and widespread global concern, necessitating accessible, reliable and cost‐effective tools for its ongoing monitoring and effective management. Herein, a portable “molecule‐probe” platform is presented that integrates a specific chemical reaction within a hydrogel microreactor with n‐type se...
Xing-Long Pan, Xiao-Hong Liu, Chang Liu et al.· Advanced Functional Material...· 0 citations
ABSTRACT In vivo continuous molecular monitoring represents a long‐standing, transformative objective in medicine, with the potential to fundamentally reshape disease diagnostics, therapeutic decision‐making, and long‐term patient management by providing dynamic biochemical information that is currently inaccessible. E...
A review of the painless biosensing landscape to include interstitial fluid and breath/exhaled breath condensate is presented, and electrical, non‐faradaic transduction, including field‐effect transistor‐ and impedance‐based sensors are introduced, as a third major modality underpinning much of the current wearable sen...
Wearable sweat sensing technologies have emerged as a promising non-invasive approach for continuous assessment of human hydration status. Among various wearable platforms, textile-based systems provide unique advantages including breathability, mechanical compliance, and seamless integration into garments, enabling...
Xin-Qing Zou, Yi-Peng Zhang, Steph Forrester et al.· Biomedical Materials & D...· 0 citations
Wearable sensors have emerged as important platforms for noninvasive, real‐time, and continuous monitoring of physiological information, with growing applications in personalized healthcare, human‐machine interfaces (HMIs), and robotic empowerment. Recent advances in flexible and stretchable materials, device integra...
Tian-Qi Yang, Han-Kun Zhu, Zhi-Hui Pan et al.· Rare Metals· 0 citations
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MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
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