A hybrid graphene metamaterial absorber for enhanced modulation and molecular fingerprint retrieval

Surface-enhanced infrared absorption (SEIRA) has proven its ability to improve the detection performance of traditional infrared spectroscopy at unprecedented levels. However, the resonant frequency of the metamaterial absorber (MA) lacks tunability once the structure is fabricated, which poses a challenge for broadband fingerprint retrieval of molecules. Here, we propose a pixelated and electric tunable hybrid graphene MA with a broadband response for molecular fingerprint retrieval. Loss engineering is employed to optimize the sensing sensitivity of MA. The resonant frequency of MA is approximately linearly modulated with a change in the graphene Fermi level. This design allows a meta-pixel to match multiple characteristic absorption spectra, thereby establishing a one-to-many mapping relationship between spatial and spectral information. The one-to-many mapping relationship greatly reduces the number of meta-pixels. As a concept demonstration, we integrate 9 meta-pixels to achieve full spectral coverage from 1000 cm(-1) to 2000 cm(-1). Based on the broadband spectral properties of the sensor, we demonstrate its potential for multi-fingerprint detection, quantitative detection, chemical identification, and compositional analysis. Our proposed hybrid graphene MA can be easily integrated with other on-chip devices, providing a potential platform for optical sensing, infrared spectroscopy, and photodetection.

Automated summary

This paper proposes a pixelated and electric tunable hybrid graphene metamaterial absorber (MA) with a broadband response for molecular fingerprint retrieval. By establishing a one-to-many mapping relationship between spatial and spectral information, a meta-pixel can match multiple characteristic absorption spectra, greatly reducing the number of meta-pixels. The broadband spectral properties of the sensor show potential for multi-fingerprint detection, quantitative detection, chemical identification, and compositional analysis.