Deep Learning-Assisted Nanocavity Sensor for Amphiphilic Biomarker Analysis

Fluorescence enhancement using nanocavity structures is a promising approach for rapid detection of amphiphilic biomarkers, which are essential for diagnosing diseases such as cancer and infections. This paper presents the use of a silver nanocube (AgNC)-gold mirror nanocavity integrated with a support lipid bilayer (SLB) for enhanced fluorescence detection. The SLB ensures optimal spacing, minimizing quenching and enhancing the fluorescence signal of amphiphilic biomarkers. A microfluidic platform allows precise and reproducible control of the detection environment, and the fluorescence enhancement factor is calculated using a deep learning-based semantic segmentation approach. This method achieves exceptional sensitivity, with the maximum enhancement factor reaching 868.64, detecting biomarkers down to the femtomolar level, demonstrating the platform's potential for clinical diagnostics. The nanocavity system combines high sensitivity, rapid detection, and low-cost preparation, making it an effective tool for advancing biological sensing applications in healthcare.