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      Recent advances and perspectives in sweat based wearable electrochemical sensors

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      TrAC Trends in Analytical Chemistry
      Elsevier BV

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          Three-dimensional flexible and conductive interconnected graphene networks grown by chemical vapour deposition.

          Integration of individual two-dimensional graphene sheets into macroscopic structures is essential for the application of graphene. A series of graphene-based composites and macroscopic structures have been recently fabricated using chemically derived graphene sheets. However, these composites and structures suffer from poor electrical conductivity because of the low quality and/or high inter-sheet junction contact resistance of the chemically derived graphene sheets. Here we report the direct synthesis of three-dimensional foam-like graphene macrostructures, which we call graphene foams (GFs), by template-directed chemical vapour deposition. A GF consists of an interconnected flexible network of graphene as the fast transport channel of charge carriers for high electrical conductivity. Even with a GF loading as low as ∼0.5 wt%, GF/poly(dimethyl siloxane) composites show a very high electrical conductivity of ∼10 S cm(-1), which is ∼6 orders of magnitude higher than chemically derived graphene-based composites. Using this unique network structure and the outstanding electrical and mechanical properties of GFs, as an example, we demonstrate the great potential of GF/poly(dimethyl siloxane) composites for flexible, foldable and stretchable conductors. © 2011 Macmillan Publishers Limited. All rights reserved
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            Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis.

            Wearable sensor technologies are essential to the realization of personalized medicine through continuously monitoring an individual's state of health. Sampling human sweat, which is rich in physiological information, could enable non-invasive monitoring. Previously reported sweat-based and other non-invasive biosensors either can only monitor a single analyte at a time or lack on-site signal processing circuitry and sensor calibration mechanisms for accurate analysis of the physiological state. Given the complexity of sweat secretion, simultaneous and multiplexed screening of target biomarkers is critical and requires full system integration to ensure the accuracy of measurements. Here we present a mechanically flexible and fully integrated (that is, no external analysis is needed) sensor array for multiplexed in situ perspiration analysis, which simultaneously and selectively measures sweat metabolites (such as glucose and lactate) and electrolytes (such as sodium and potassium ions), as well as the skin temperature (to calibrate the response of the sensors). Our work bridges the technological gap between signal transduction, conditioning (amplification and filtering), processing and wireless transmission in wearable biosensors by merging plastic-based sensors that interface with the skin with silicon integrated circuits consolidated on a flexible circuit board for complex signal processing. This application could not have been realized using either of these technologies alone owing to their respective inherent limitations. The wearable system is used to measure the detailed sweat profile of human subjects engaged in prolonged indoor and outdoor physical activities, and to make a real-time assessment of the physiological state of the subjects. This platform enables a wide range of personalized diagnostic and physiological monitoring applications.
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              Wearable biosensors for healthcare monitoring

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                Author and article information

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                Journal
                TrAC Trends in Analytical Chemistry
                TrAC Trends in Analytical Chemistry
                Elsevier BV
                01659936
                October 2020
                October 2020
                : 131
                : 116024
                Article
                10.1016/j.trac.2020.116024
                a01752fa-c80c-44b5-bfdd-3dc122180983
                © 2020

                https://www.elsevier.com/tdm/userlicense/1.0/

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