Print Email Facebook Twitter Optimization of multilayer graphene-based gas sensors by ultraviolet photoactivation Title Optimization of multilayer graphene-based gas sensors by ultraviolet photoactivation Author Peña, Álvaro (UCM-ADIF) Matatagui, Daniel (UCM-ADIF; Universidad Complutense de Madrid; (ITEFI-CSIC)) Ricciardella, F. (TU Delft Electronic Instrumentation) Sacco, L.N. (TU Delft Electronic Components, Technology and Materials) Vollebregt, S. (TU Delft Electronic Components, Technology and Materials) Otero, Daniel (UCM-ADIF) López-Sánchez, Jesús (CSIC - Instituto de Ceramica y Vidrio (ICV)) Marín, Pilar (UCM-ADIF; Universidad Complutense de Madrid) Horrillo, Mari Carmen ((ITEFI-CSIC)) Date 2023 Abstract Nitrogen dioxide (NO2) is a potential hazard to human health at low concentrations, below one part per million (ppm). NO2 can be monitored using gas sensors based on multi-layered graphene operating at ambient temperature. However, reliable detection of concentrations on the order of parts per million and lower is hindered by partial recovery and lack of reproducibility of the sensors after exposure. We show how to overcome these longstanding problems using ultraviolet (UV) light. When exposed to NO2, the sensor response is enhanced by 290 % − 550 % under a 275 nm wavelength light emitting diode irradiation. Furthermore, the sensor's initial state is completely restored after exposure to the target gas. UV irradiation at 68 W/m2 reduces the NO2 detection limit to 30 parts per billion (ppb) at room temperature. We investigated sensor performance optimization for UV irradiation with different power densities and target gases, such as carbon oxide and ammonia. Improved sensitivity, recovery, and reproducibility of UV-assisted graphene-based gas sensors make them suitable for widespread environmental applications. Subject AmmoniaCarbon monoxideGraphene gas sensorsLimit of detectionNitrogen dioxideUltraviolet To reference this document use: http://resolver.tudelft.nl/uuid:54599a2d-a3f5-485e-b370-d3fa8c0030bf DOI https://doi.org/10.1016/j.apsusc.2022.155393 ISSN 0169-4332 Source Applied Surface Science, 610 Part of collection Institutional Repository Document type journal article Rights © 2023 Álvaro Peña, Daniel Matatagui, F. Ricciardella, L.N. Sacco, S. Vollebregt, Daniel Otero, Jesús López-Sánchez, Pilar Marín, Mari Carmen Horrillo Files PDF 1_s2.0_S016943322202921X_main.pdf 4.96 MB Close viewer /islandora/object/uuid:54599a2d-a3f5-485e-b370-d3fa8c0030bf/datastream/OBJ/view