Nanoplasmonic SERS on Fidget Spinner for Digital Bacterial Identification

Raman spectroscopy offers non-destructive and highly sensitive molecular insights into bacterial species, making it a valuable tool for detection, identification, and antibiotic susceptibility testing. However, achieving clinically relevant accuracy, quantitative data, and reproducibility remains challenging due to the dominance of bulk signals and the uncontrollable heterogeneity of analytes. In this study, we introduce an innovative diagnostic tool: a plasmonic fidget spinner (P-FS) integrated with a membrane substrate modified with a nanoplasmonic-enhanced matrix, designed for simultaneous bacterial filtration and detection. We developed a method to fabricate a plasmonic microarray patterned nitrocellulose membrane using photolithography, which is then integrated with a customized fidget spinner. Testing the P-FS device with various bacterial species (E. coli 25922, S. aureus 25923, E. coli MG1655, Lactobacillus brevis, and S. mutans 3065) demonstrated successful identification based on their unique Raman fingerprints. The bacterial interface with nanoplasmonic hotspots on the P-FS significantly enhances sensitivity, allowing for more precise detection, while SERS intensity mappings obtained from the Raman spectrometer are transformed into digital signals using machine learning techniques to identify and quantify bacterial distribution. Given the P-FS's ability to enhance vibrational signatures and its scalable fabrication under routine conditions, we anticipate that nanoplasmonic-enhanced Raman spectroscopy will become the preferred technology for the reliable and ultrasensitive detection of various analytes, including those crucial to human health, with strong potential for transitioning from laboratory research to clinical applications.