Tunable Graphene Plasmonic Sensor for Multi-Component Molecular Detection in the Mid-Infrared Assisted by Machine Learning

Mid-infrared molecular sensing faces challenges in simultaneously achieving high-resolution qualitative identification and quantitative analysis of multiple biomolecules. To address this, we present a tunable mid-infrared sensing platform, integrating the simulation of a single-layer graphene square-aperture array sensor with a machine learning algorithm called principal component analysis for advanced spectral processing. The graphene square-aperture structure excites dynamically tunable localized surface plasmon resonances by modulating the graphene’s Fermi level, enabling precise alignment with the vibrational fingerprints of target molecules. This plasmon–molecule coupling amplifies absorption signals and serves as discernible “molecular barcodes” for precise identification without change in the structural parameters. We demonstrate the platform’s capability to detect and differentiate carbazole-based biphenyl molecules and protein molecules, even in complex mixtures, by systematically tuning the Fermi level to match their unique vibrational bands. More importantly, for mixtures with unknown total amounts and different concentration ratios, the principal component analysis algorithm effectively processes complex transmission spectra and presents the relevant information in a simpler form. This integration of tunable graphene plasmons with machine learning algorithms establishes a label-free, multiplexed mid-infrared sensing strategy with broad applicability in biomedical diagnostics, environmental monitoring, and chemical analysis.