Recent advances in engineered graphene and composites for detection of volatile organic compounds (VOCs) and non-invasive diseases diagnosis

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• 作者：Kumud Malika Tripathi^a ; TaeYoung Kim^a ; ^{taeykim@gachon.ac.kr" class="auth_mail" title="E-mail the corresponding author} ; Dusan Losic^b ; Tran Thanh Tung^b ; ^{tran.tung@adelaide.edu.au" class="auth_mail" title="E-mail the corresponding author} ; ^{thanhtungtran77@gmail.com" class="auth_mail" title="E-mail the corresponding author}
• 刊名：Carbon
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：110
• 期：Complete
• 页码：97-129
• 全文大小：10268 K

文摘

Point-of-care (POC) diagnostic technologies for early stage diagnosis and real-time monitoring of medical conditions are important element of healthcare strategy to improve medical treatment outcomes. Graphene, one-atom-thick fabric of carbon, has attracted enormous attention as a new sensing platform for the development of a new generation of nanoscale sensing devices. The two-dimensional (2D) nanostructure and high surface-to-volume ratio of graphene provide a strategy for designing sensing devices with capability to detect diverse analyte molecules. Their excellent conductivity and zero-band gap features promote electron transport between the sensor and analyte molecules, which is crucial for the development of ultra-fast-responsive and high sensitive devices for numerous biomedical applications. Particularly, owing to ease of fabrication and miniaturization, low cost, and simplicity of operation, graphene-based sensors offer a great potential for portable real-time medical diagnostics, when compared with conventional techniques based on expensive and labor extensive lab-bench instruments. This review provides a brief overview of recent progress in graphene-based sensors for the detection of volatile organic compounds (VOCs) and diagnosis of diseases via non-invasive analysis. Techniques for the fabrication of sensors and critical analysis of VOCs detection devices associated with various diseases are presented. We also summarized approaches to overcome the remaining obstacles in real-world applications of sensors in clinical diagnosis.