A plasmonic and intelligent sweat sensing patch for large-scale health monitoring

Abstract

The need for rapid and comprehensive health monitoring is especially critical during health crises involving chronic diseases of epidemic proportions or infectious disease outbreaks. Sweat testing offers a rapid, in situ, and noninvasive alternative to traditional blood testing, minimizing discomfort and cross-infection risks. However, the development and commercialization of simple, highly scalable, and power-free sweat sensing devices have been slow and challenging. Here, we design a miniaturized, modular, and skin-interfaced sweat sensing patch for rapid and efficient large-scale health monitoring and diagnosis through multimodal laser sensing. The patch's workflow involves sweat collection via a microfluidics-based collection module, followed by sweat sensing and artificial intelligence (AI)-assisted diagnosis. The sweat sensing module, prepared by coating silver nanowires on filter paper, enables rapid detection of multi-analytes (e.g., glucose, lactate, urea, sodium, potassium, and lead) using multimodal laser sensing techniques (that combine surface-enhanced Raman spectroscopy with nano-enhanced laser-induced breakdown spectroscopy). Furthermore, the multispectral data, analyzed with AI assistance, can rapidly and efficiently detect abnormalities in sweat components for quick diagnosis. Our volunteer trials also show that real-world health monitoring is feasible. Overall, this straightforward and cost-effective patch, integrated with multimodal laser sensing, can potentially enable large-scale health monitoring and diagnosis.

Keywords

artificial intelligence / diagnostics / microfluidics / nano-enhanced laser-induced breakdown spectroscopy / surface-enhanced Raman spectroscopy

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