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Graphene-VO2 hybrid metamaterial biosensor with machine learning-assisted absorption and virus detection in the THz regime
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DOI：10.1117/1.OE.65.4.047103.png)
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Abstract
En 中文
In this research, we propose a reconfigurable terahertz (THz) metamaterial-based biosensing structure that integrates graphene with vanadium dioxide (VO2) to achieve intelligent absorption control and virus detection. The hybrid configuration leverages the electrically tunable conductivity of graphene together with the thermally induced phase transition of VO2, resulting in dual-mode switching and improved structural flexibility. The designed device exhibits strong sensing capability, achieving a sensitivity of 3.025 THz/RIU. It also offers a high figure of merit of 44.14 RIU-1 and a quality factor (Q) of 63.41, indicating sharp resonance and efficient performance. These results confirm the sensor's capability to detect subtle changes in the refractive index of biological samples, making it highly suitable for identifying different viruses and biochemical analytes. In addition, a machine learning framework is incorporated as a numerical surrogate modeling tool to predict absorption trends based on simulated data, thereby reducing computational effort and accelerating parametric design exploration. Compared with previously reported VO2-, MXene-, and graphene-based absorbers, the proposed biosensor exhibits improved tunability, higher detection accuracy, and adaptive functionality. This work, therefore, demonstrates a viable route toward next-generation smart THz biosensors for biomedical diagnostics and sensing technologies. The reported results are based on full-wave numerical simulations and highlight refractive index-based sensing capability, with machine learning used solely as a computational design-assistance tool.
Keywords:
metamaterial absorber
biosensing
machine learning
graphene
vanadium dioxide
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IF:
1.2
Papers:
178
Citations:
1.1W
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