No abstract

No 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.

In this study, we investigated means of performance enhancement in sensors based on epitaxial graphene on silicon carbide (SiC). Epitaxially grown graphene on SiC substrates were successfully decorated with metal oxide nanoparticles such as TiO2 and Fe3O4 using hollow cathode pulsed plasma sputtering. Atomic Force Microscopy and Raman data verified that no damage was added to the graphene surface. It could be shown that it was easily possible to detect benzene, which is one of the most dangerous volatile organic compounds, with the Fe3O4 decorated graphene sensor down to an ultra-low concentration of 5 ppb with a signal to noise ratio of 35 dB. Moreover, upon illumination with a UV light LED (265 nm) of the TiO2 decorated graphene sensor, the sensitivity towards a change of oxygen could be enhanced such that a clear sensor response could be seen which is a significant improvement over dark conditions, where almost no response occurred. As the last enhancement, the time derivative sensor signal was introduced for the sensor data evaluation, testing the response towards a change of oxygen. This sensor signal evaluation approach can be used to decrease the response time of the sensor by at least one order of magnitude.