Dielectric interlayer-enhanced surface plasmon resonance biosensor with 1D convolutional neural network surrogate modelling for cancer biomarker, glucose, and low refractive index analyte detection

Introduction Surface plasmon resonance (SPR) biosensors based on the Kretschmann configuration offer label-free, real-time detection of molecular interactions and are applicable to cancer-associated bioreceptors, glucose, and low refractive index (RI) analytes. Existing dielectric-enhanced SPR designs and machine learning optimization studies are typically conducted as separate research topics, with few frameworks combining both approaches within a single multianalyte platform. Methods A multilayer SPR biosensor incorporating dielectric and two-dimensional nanomaterial interlayers was designed using a BK-7 prism, a copper plasmonic film, silicon nitride (Si 3 N 4 ), zinc oxide (ZnO), molybdenum disulfide (MoS 2 ), and a bismuth trioxide (Bi 2 O 3 ) overlayer. The optical response was evaluated using the Transfer Matrix Method (TMM) and finite element modelling (FEM) in COMSOL Multiphysics. Individual layer thicknesses were optimized through systematic parametric analysis. A one-dimensional convolutional neural network (1D CNN) surrogate model was trained on FEM-generated parametric sweep data to predict sensor spectral responses across varying structural parameters. Model performance was assessed using the coefficient of determination, relative absolute error, relative squared error, and symmetric mean absolute percentage error. Results and discussion The sensor was evaluated over three refractive index ranges: cancer-associated bioreceptors (1.360–1.401 RIU), glucose solutions (1.335–1.347 RIU), and low-RI chemical analytes (1.29–1.38 RIU). The optimized design achieved a peak angular sensitivity of 1,100°/RIU for both cancer-associated bioreceptor and glucose detection, a highest figure of merit of 157.143 RIU −1 for glucose sensing, and a maximum angular sensitivity of 550°/RIU for low-RI analytes. The 1D CNN surrogate model achieved coefficients of determination greater than 0.994 for MoS 2 thicknesses up to approximately 1.35 nm and greater than 0.961 for ZnO thicknesses between 1.5 and 6.0 nm, with performance degradation at higher thicknesses attributed to output dynamic range compression. The proposed design combines numerical electromagnetic modelling with machine learning to accelerate sensor optimization for SPR-based biosensing applications.