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dc.contributor.authorXu, Xuezhu
dc.contributor.authorZhou, Jian
dc.contributor.authorXin, Yangyang
dc.contributor.authorLubineau, Gilles
dc.contributor.authorMa, Qian
dc.contributor.authorJiang, Long
dc.date.accessioned2017-07-03T11:45:31Z
dc.date.available2017-07-03T11:45:31Z
dc.date.issued2017-06-28
dc.identifier.citationXu X, Zhou J, Xin Y, Lubineau G, Ma Q, et al. (2017) Alcohol Recognition by Flexible, Transparent and Highly Sensitive Graphene-Based Thin-Film Sensors. Scientific Reports 7. Available: http://dx.doi.org/10.1038/s41598-017-04636-2.
dc.identifier.issn2045-2322
dc.identifier.pmid28659624
dc.identifier.doi10.1038/s41598-017-04636-2
dc.identifier.urihttp://hdl.handle.net/10754/625143
dc.description.abstractChemical sensors detect a variety of chemicals across numerous fields, such as automobile, aerospace, safety, indoor air quality, environmental control, food, industrial production and medicine. We successfully assemble an alcohol-sensing device comprising a thin-film sensor made of graphene nanosheets (GNs) and bacterial cellulose nanofibers (BCNs). We show that the GN/BCN sensor has a high selectivity to ethanol by distinguishing liquid-phase or vapor-phase ethanol (C2H6O) from water (H2O) intelligently with accurate transformation into electrical signals in devices. The BCN component of the film amplifies the ethanol sensitivity of the film, whereby the GN/BCN sensor has 12400% sensitivity for vapor-phase ethanol compared to the pure GN sensor, which has only 21% sensitivity. Finally, GN/BCN sensors demonstrate fast response/recovery times and a wide range of alcohol detection (10-100%). The superior sensing ability of GN/BCN compared to GNs alone is due to the improved wettability of BCNs and the ionization of liquids. We prove a facile, green, low-cost route for the assembly of ethanol-sensing devices with potential for vast application.
dc.description.sponsorshipThis research was supported by the King Abdullah University of Science and Technology (KAUST). The authors are grateful to KAUST for its continuous support.
dc.publisherSpringer Nature
dc.relation.urlhttps://www.nature.com/articles/s41598-017-04636-2
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleAlcohol Recognition by Flexible, Transparent and Highly Sensitive Graphene-Based Thin-Film Sensors
dc.typeArticle
dc.contributor.departmentComposite and Heterogeneous Material Analysis and Simulation Laboratory (COHMAS)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentMechanical Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.identifier.journalScientific Reports
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionCollege of Textile and Clothing, Yancheng Institute of Industry Technology, Yancheng, 224005, P. R. China
dc.contributor.institutionNorth Dakota State University, Department of Mechanical Engineering, Fargo, ND, 58102, United States
kaust.personXu, Xuezhu
kaust.personZhou, Jian
kaust.personXin, Yangyang
kaust.personLubineau, Gilles
refterms.dateFOA2018-06-13T10:49:20Z
dc.date.published-online2017-06-28
dc.date.published-print2017-12


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This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Except where otherwise noted, this item's license is described as This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.