Inkjet-Printed rGO/binary Metal Oxide Sensor for Predictive Gas Sensing in a Mixed Environment
Authors
Ogbeide, O
Bae, G
Yu, W
Morrin, E
Song, Y
Song, W
Li, Y
Su, BL
An, KS
Publication Date
2022Journal Title
Advanced Functional Materials
ISSN
1616-301X
Publisher
Wiley
Language
en
Type
Article
This Version
AO
VoR
Metadata
Show full item recordCitation
Ogbeide, O., Bae, G., Yu, W., Morrin, E., Song, Y., Song, W., Li, Y., et al. (2022). Inkjet-Printed rGO/binary Metal Oxide Sensor for Predictive Gas Sensing in a Mixed Environment. Advanced Functional Materials https://doi.org/10.1002/adfm.202113348
Description
Funder: Alphasense Limited
Abstract
Abstract: 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.
Keywords
Research Article, Research Articles, gas prediction, gas sensors, graphene, inkjet printing, machine learning, metal oxide, principal component analysis
Sponsorship
Engineering and Physical Sciences Research Council (EP/L016087/1)
Identifiers
adfm202113348
External DOI: https://doi.org/10.1002/adfm.202113348
This record's URL: https://www.repository.cam.ac.uk/handle/1810/335239
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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