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Toward point-of-care management of chronic respiratory conditions:Electrochemical sensing of nitrite content in exhaled breath condensate using reduced graphene oxide 被引量:5

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摘要 We present a portable non-invasive approach for measuring indicators of inflammation and oxidative stress in the respiratory tract by quantifying a biomarker in exhaled breath condensate(EBC).We discuss the fabrication and characterization of a miniaturized electrochemical sensor for detecting nitrite content in EBC using reduced graphene oxide.The nitrite content in EBC has been demonstrated to be a promising biomarker of inflammation in the respiratory tract,particularly in asthma.We utilized the unique properties of reduced graphene oxide(rGO);specifically,the material is resilient to corrosion while exhibiting rapid electron transfer with electrolytes,thus allowing for highly sensitive electrochemical detection with minimal fouling.Our rGO sensor was housed in an electrochemical cell fabricated from polydimethyl siloxane(PDMS),which was necessary to analyze small EBC sample volumes.The sensor is capable of detecting nitrite at a low over-potential of 0.7 V with respect to an Ag/AgCl reference electrode.We characterized the performance of the sensors using standard nitrite/buffer solutions,nitrite spiked into EBC,and clinical EBC samples.The sensor demonstrated a sensitivity of 0.21μAμM^(−1) cm^(−2) in the range of 20–100μM and of 0.1μAμM^(−1) cm^(−2) in the range of 100–1000μM nitrite concentration and exhibited a low detection limit of 830 nM in the EBC matrix.To benchmark our platform,we tested our sensors using seven pre-characterized clinical EBC samples with concentrations ranging between 0.14 and 6.5μM.This enzyme-free and label-free method of detecting biomarkers in EBC can pave the way for the development of portable breath analyzers for diagnosing and managing changes in respiratory inflammation and disease.
出处 《Microsystems & Nanoengineering》 EI CSCD 2017年第1期286-293,共8页 微系统与纳米工程(英文)
基金 This work was partially funded by the National Institutes of Health NIEHS Center Grant ES005022 and by the Rutgers University Electrical and Computer Engineering Department.
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