Please use this identifier to cite or link to this item:
doi:10.22028/D291-27799
Title: | Graphene Decorated with Iron Oxide Nanoparticles for Highly Sensitive Interaction with Volatile Organic Compounds |
Author(s): | Rodner, Marius Puglisi, Donatella Ekeroth, Sebastian Helmersson, Ulf Shtepliuk, Ivan Yakimova, Rositsa Skallberg, Andreas Uvdal, Kajsa Schütze, Andreas Eriksson, Jens |
Language: | English |
Title: | Sensors |
Volume: | 19 |
Issue: | 4 |
Publisher/Platform: | MDPI |
Year of Publication: | 2019 |
Free key words: | epitaxial graphene metal oxide nanoparticle gas sensor volatile organic compounds benzene formaldehyde derivative sensor signal air quality sensor |
DDC notations: | 600 Technology |
Publikation type: | Journal Article |
Abstract: | Gases, such as nitrogen dioxide, formaldehyde and benzene, are toxic even at very low concentrations. However, so far there are no low-cost sensors available with sufficiently low detection limits and desired response times, which are able to detect them in the ranges relevant for air quality control. In this work, we address both, detection of small gas amounts and fast response times, using epitaxially grown graphene decorated with iron oxide nanoparticles. This hybrid surface is used as a sensing layer to detect formaldehyde and benzene at concentrations of relevance (low parts per billion). The performance enhancement was additionally validated using density functional theory calculations to see the effect of decoration on binding energies between the gas molecules and the sensor surface. Moreover, the time constants can be drastically reduced using a derivative sensor signal readout, allowing the sensor to work at detection limits and sampling rates desired for air quality monitoring applications. |
DOI of the first publication: | 10.3390/s19040918 |
Link to this record: | urn:nbn:de:bsz:291--ds-277997 hdl:20.500.11880/29967 http://dx.doi.org/10.22028/D291-27799 |
ISSN: | 1424-8220 |
Date of registration: | 5-Nov-2020 |
Description of the related object: | Supplementary Materials |
Related object: | http://www.mdpi.com/1424-8220/19/4/918/s1 |
Faculty: | NT - Naturwissenschaftlich- Technische Fakultät |
Department: | NT - Systems Engineering |
Professorship: | NT - Prof. Dr. Andreas Schütze |
Collections: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
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sensors-19-00918.pdf | 2,2 MB | Adobe PDF | View/Open |
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