Graphene-Integrated Dual-Channel q-BIC Metasurface for Actively Tunable and Self-Calibrated Mid-Infrared SO2 Sensing

Mid-infrared gas sensors exploit molecular vibrational absorption but remain vulnerable to source fluctuation, background refractive-index drift, and fabrication-induced spectral mismatch. We numerically propose a graphene-integrated dual-channel quasi-bound-state-in-the-continuum (q-BIC) metasurface for SO2 detection near 8.6m. Symmetry breaking in square–hollow silicon nanopillars produces two leaky resonances with distinct modal origins, denoted P1 and P2. P1 is aligned with the SO2 absorption band and serves as the sensing channel, whereas P2 provides an off-band reference. Graphene gating enables post-fabrication spectral alignment. At the graphene Fermi level EF=0.516eV, the refractive-index sensitivities of P1 and P2 are 2280nm/RIU and 564.6nm/RIU, respectively. Under 1 source fluctuation, the ideal common-mode model predicts an approximately 17dB signal-to-noise ratio (SNR) improvement over single-channel detection. The field-overlap model gives an uncalibrated limit of detection (LOD) of 46.1ppm and a conditional value of 6.5ppm under an illustrative rescaled noise budget with negligible additional calibration errors. A value of 4.61ppm requires the total corrected equivalent absorption noise to reach 1.0×10−7. These assumption-dependent estimates are not experimentally measured detection limits. The proposed architecture provides a compact platform for active spectral alignment and dual-channel self-calibration in mid-infrared gas sensing.

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Publication Details

Journal
Sensors
Published
2026-10-30
DOI
https://doi.org/10.3390/s26216889
Primary Topic
Metamaterials and Metasurfaces Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Graphene-Integrated Dual-Channel q-BIC Metasurface for Actively Tunable and Self-Calibrated Mid-Infrared SO2 Sensing

Zhengshan Xu, Yang He, Tonggang Zhao, Dashuai Xu
2 links
Sensors
Metamaterials and Metasurfaces Applications
article

Graphene-Integrated Dual-Channel q-BIC Metasurface for Actively Tunable and Self-Calibrated Mid-Infrared SO2 Sensing

Zhengshan Xu, Yang He, Tonggang Zhao, Dashuai Xu
article en

Abstract

Mid-infrared gas sensors exploit molecular vibrational absorption but remain vulnerable to source fluctuation, background refractive-index drift, and fabrication-induced spectral mismatch. We numerically propose a graphene-integrated dual-channel quasi-bound-state-in-the-continuum (q-BIC) metasurface for SO2 detection near 8.6m. Symmetry breaking in square–hollow silicon nanopillars produces two leaky resonances with distinct modal origins, denoted P1 and P2. P1 is aligned with the SO2 absorption band and serves as the sensing channel, whereas P2 provides an off-band reference. Graphene gating enables post-fabrication spectral alignment. At the graphene Fermi level EF=0.516eV, the refractive-index sensitivities of P1 and P2 are 2280nm/RIU and 564.6nm/RIU, respectively. Under 1 source fluctuation, the ideal common-mode model predicts an approximately 17dB signal-to-noise ratio (SNR) improvement over single-channel detection. The field-overlap model gives an uncalibrated limit of detection (LOD) of 46.1ppm and a conditional value of 6.5ppm under an illustrative rescaled noise budget with negligible additional calibration errors. A value of 4.61ppm requires the total corrected equivalent absorption noise to reach 1.0×10−7. These assumption-dependent estimates are not experimentally measured detection limits. The proposed architecture provides a compact platform for active spectral alignment and dual-channel self-calibration in mid-infrared gas sensing.

SensorsVol. 26(21)
Beijing University of Posts and Telecommunications (CN)
National Key Research and Development Program of China
Openalex Percentile: Top 34%
Metamaterials and Metasurfaces Applications
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Graphene-Integrated Dual-Channel q-BIC Metasurface for Actively Tunable and Self-Calibrated Mid-Infrared SO2 Sensing — Zhengshan Xu, Yang He, et al. · Sensors (2026) | TGRS Research Map | TGRS