All-flexible chronoepifluidic nanoplasmonic patch for label-free metabolite profiling in sweat

Jaehun Jeon, Sangyeon Lee, Seongok Chae, Joo Hoon Lee, Hanjin Kim, Eun-Sil Yu, Hamin Na, Taejoon Kang, Hyung-Soon Park, Doheon Lee and Ki-Hun Jeong ()
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Jaehun Jeon: Korea Advanced Institute of Science and Technology (KAIST)
Sangyeon Lee: Korea Advanced Institute of Science and Technology (KAIST)
Seongok Chae: Korea Advanced Institute of Science and Technology (KAIST)
Joo Hoon Lee: Korea Research Institute of Bioscience and Biotechnology (KRIBB)
Hanjin Kim: Korea Advanced Institute of Science and Technology (KAIST)
Eun-Sil Yu: Korea Advanced Institute of Science and Technology (KAIST)
Hamin Na: Korea Advanced Institute of Science and Technology (KAIST)
Taejoon Kang: Korea Research Institute of Bioscience and Biotechnology (KRIBB)
Hyung-Soon Park: Korea Advanced Institute of Science and Technology (KAIST)
Doheon Lee: Korea Advanced Institute of Science and Technology (KAIST)
Ki-Hun Jeong: Korea Advanced Institute of Science and Technology (KAIST)

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract Wearable sensors allow non-invasive monitoring of sweat metabolites, but their reliance on molecular recognition elements limits both physiological coverage and temporal resolution. Here we report an all-flexible chronoepifluidic surface-enhanced Raman spectroscopy (CEP-SERS) patch for label-free and chronometric profiling of sweat metabolites. The CEP-SERS patch integrates plasmonic nanostructures in epifluidic microchannels for chronological sweat sampling and molecular analysis. An ultrathin fluorocarbon nanofilm modulates surface chain mobility to guide low-temperature solid-state dewetting, forming large-area silver nanoislands on a structured flexible substrate. The wearable patch adheres conformally to skin, collects sequential sweat samples, and supports label-free and multiplexed SERS detection of assorted metabolites. Machine learning-assisted quantification of lactate, uric acid, and tyrosine yields robust metabolic profiles in distinct physical activity states. This wearable optofluidic platform refines molecular sweat sensing and expands the potential for individualized phenotyping in proactive and data-driven healthcare.

Date: 2025
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DOI: 10.1038/s41467-025-63510-2

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