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dc.contributor.authorOgbeide, O
dc.contributor.authorBae, G
dc.contributor.authorYu, W
dc.contributor.authorMorrin, E
dc.contributor.authorSong, Y
dc.contributor.authorSong, W
dc.contributor.authorLi, Y
dc.contributor.authorSu, BL
dc.contributor.authorAn, KS
dc.contributor.authorHasan, T
dc.date.accessioned2022-03-21T02:02:02Z
dc.date.available2022-03-21T02:02:02Z
dc.date.issued2022
dc.date.submitted2021-12-29
dc.identifier.issn1616-301X
dc.identifier.otheradfm202113348
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/335239
dc.descriptionFunder: Alphasense Limited
dc.description.abstractAbstract: Selectivity for specific analytes and high‐temperature operation are key challenges for chemiresistive‐type gas sensors. Complementary hybrid materials, such as reduced graphene oxide (rGO) decorated with metal oxides enables realization of room‐temperature sensors with enhanced sensitivity. However, sensor training to identify target gases and accurate concentration measurement from gas mixtures still remain very challenging. This work proposes hybridization of rGO with CuCoO x binary metal oxide as a sensing material. Highly stable, room‐temperature NO2 sensors with a 50 ppb of detection limit is demonstrated using inkjet printing. A framework is then developed for machine‐intelligent recognition with good visibility to identify specific gases and predict concentration under an interfering atmosphere from a single sensor. Using ten unique parameters extracted from the sensor response, the machine learning‐based classifier provides a decision boundary with 98.1% accuracy, and is able to correctly predict previously unseen NO2 and humidity concentrations in an interfering environment. This approach enables implementation of an intelligent platform for printable, room‐temperature gas sensors in a mixed environment irrespective of ambient humidity.
dc.languageen
dc.publisherWiley
dc.subjectResearch Article
dc.subjectResearch Articles
dc.subjectgas prediction
dc.subjectgas sensors
dc.subjectgraphene
dc.subjectinkjet printing
dc.subjectmachine learning
dc.subjectmetal oxide
dc.subjectprincipal component analysis
dc.titleInkjet-Printed rGO/binary Metal Oxide Sensor for Predictive Gas Sensing in a Mixed Environment
dc.typeArticle
dc.date.updated2022-03-21T02:02:01Z
prism.publicationNameAdvanced Functional Materials
dc.identifier.doi10.17863/CAM.82670
rioxxterms.versionofrecord10.1002/adfm.202113348
rioxxterms.versionAO
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidHasan, T [0000-0002-6250-7582]
dc.identifier.eissn1616-3028
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/L016087/1)
cam.issuedOnline2022-03-20


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