Self-Assembled Plasmonic Magnifier: A New Platform for Ultra-Sensitive Detection of Respiratory Viruses Using Surface-Enhanced Raman Spectroscopy.

Abstract

Accurate and sensitive pathogen detection is critical for the prevention and control of respiratory viral infections, which pose significant threats to global health, particularly for vulnerable populations such as children, the elderly, and immunocompromised individuals. Here, we present a novel detection platform, Surface-Enhanced Raman Scattering combined with Artificial Intelligence (SERS-AI). At the core of this platform lies the independently developed self-assembled plasmonic magnifier (SPM), an enhanced substrate. Unlike traditional SERS substrates that rely on random aggregation or prefabricated nanostructures, this platform employs a virus-triggered self-assembly mechanism. Through the electrostatic attraction of C12 DNA molecules and the aggregation regulation of calcium ions, the self-assembled plasmonic magnifier (SPM) can significantly increase the probability of forming highly localized plasmonic "hotspots" near viral particles. This virus-associated hotspot formation strategy, which enhances the correlation between hotspot distribution and viral particles, significantly improves the specificity, intensity, and reproducibility of signals. Integrating surface-enhanced Raman spectroscopy (SERS) with artificial intelligence (AI) technology, the platform enables rapid, accurate, and label-free identification and quantitative analysis of respiratory viruses. The platform demonstrated exceptional sensitivity and reproducibility in detecting respiratory syncytial virus, human adenovirus type 5, influenza B virus, and H1N1 virus, with unique SERS fingerprints showing strong linear correlations with viral concentrations. The AI-driven spectral analysis allowed accurate differentiation of these viruses in serum and saliva samples, achieving detection within 2 min. Detection limits reached as low as 5 × 10-5 copies/mL demonstrating robustness and reliability even in complex biological matrices. This SERS-AI-SPM platform represents a significant breakthrough in SERS technology by integrating advanced nanomaterial engineering with AI-powered data analysis. Its rapid, sensitive, and reliable performance underscores its transformative potential in clinical diagnostics, large-scale epidemic prevention, and personalized medicine. This innovation provides a powerful tool for real-time infectious disease monitoring and public health management.