Identification of gas molecules plays a key role a wide range of applications extending from healthcare to security. However, the most widely used gas nano-sensors are based on electrical approaches or refractive index sensing, which typically are unable to identify molecular species. Here, we report label-free identification of gas molecules SO 2 , NO 2 , N 2 O, and NO by detecting their rotational-vibrational modes using graphene plasmon. The detected signal corresponds to a gas molecule layer adsorbed on the graphene surface with a concentration of 800 zeptomole per μm 2 , which is made possible by the strong field confinement of graphene plasmons and high physisorption of gas molecules on the graphene nanoribbons. We further demonstrate a fast response time (<1 min) of our devices, which enables real-time monitoring of gaseous chemical reactions. The demonstration and understanding of gas molecule identification using graphene plasmonic nanostructures open the door to various emerging applications, including in-breath diagnostics and monitoring of volatile organic compounds.
Bibliographical noteFunding Information:
This work was supported by the National Basic Key Research Program of China (Grant No. 2015CB932400), the National Key Research and Development Program of China (Grant No. 2016YFA0201600), the National Natural Science Foundation of China (Grant Nos. 51372045, 11504063, and 11674073), the key program of the bureau of Frontier Sciences and Education Chinese Academy of Sciences (Grant No. QYZDBSSW-SLH021), Youth Innovation Promotion Association CAS and Academy of Finland (Grant Nos. 276376, 284548, 295777, 304666, 312297, 312551, and 314810), Business Finland (OPEC, A-Photonics), the European Union’s Seventh Framework Programme (REA grant agreement No. 631610) and the European Union’s Horizon 2020 research and innovation program (Grant agreement No. 820423, S2QUIP). T.L. acknowledges support from the National Science Foundation under Grant No. NSF/EFRI-1741660. F.J.G.A. acknowledges the ERC (Advanced Grant No. 789104-eNANO), the Spanish MINECO (Grant Nos. MAT2017–88492-R and SEV2015–0522), the Catalan CERCA and AGAUR (2014-SGR-1400) programs, and Fundació Privada Cellex.
© 2019, The Author(s).