Genetic Algorithm-Optimized Hyperbolic Metamaterial SPR Sensors for Ultrasensitive Biosensing Applications.

Enhancing the sensitivity of surface plasmon resonance (SPR) sensors is critical for detecting lower analyte concentrations and smaller biomolecular interactions. In this study, we designed and developed a surface plasmon resonance (SPR) sensor platform based on hyperbolic metamaterials (HMMs). The sensor is composed of Ag/Al2O3 multilayer composites, and a genetic algorithm (GA) was employed to systematically determine the optimal parameter configuration for the HMMs-based SPR sensor. Furthermore, theoretical analysis was conducted using the finite element analysis method, revealing that the electric field intensity within the hyperbolic metamaterial exhibits a layer-by-layer increasing trend, with significant field enhancement predominantly occurring within the dielectric layers. Compared with conventional SPR sensors, where electric field enhancement is confined to a single interface, the HMMs structure demonstrates a clear advantage in field enhancement, thereby substantially improving the sensitivity of the SPR sensor. Experimental results further validated the performance of the sensor, achieving a refractive index (RI) sensitivity of 43.62 μm/RIU, which represents an improvement of approximately 1 order of magnitude compared with traditional SPR sensors. This platform was used to test for the Alzheimer's disease biomarker β-amyloid 1-42 (Aβ1-42), achieving an ultralow limit of detection (LOD) of 0.18 pg/mL. GA design provides a generalized approach to improving device performance and is expected to have a significant impact on the design and application of sensors.