Abstract
Vibrio infections progress rapidly with high mortality, yet current diagnostics fail to meet the critical need for rapid, accurate, and field-deployable detection. To bridge this gap, we present a bioreceptor-free, AI-powered surface-enhanced Raman spectroscopy (SERS) platform for rapid Vibrio typing. Our approach leverages Na2HPO4-mediated synergistic anion/cation interactions to direct targeted accumulation of gold nanoparticles onto bacterial surfaces within 5 min, overcoming the uncontrollable aggregation and poor reproducibility of conventional methods. By integrating a gradient-weighted class activation mapping-assisted Vision Transformer model for intelligent spectral analysis with a custom-built portable Raman instrument for automated measurements, a remarkable typing accuracy of 97.62% is achieved across seven bacterial species (five Vibrio species and two interferents). The entire sample-to-result process takes only 10 min. This work establishes a new paradigm for SERS-based bacterial detection/typing and provides a promising solution for point-of-care diagnostics in public health, food safety, and environmental monitoring.
| Original language | English |
|---|---|
| Pages (from-to) | 1385-1393 |
| Number of pages | 9 |
| Journal | Nano Letters |
| Volume | 26 |
| Issue number | 4 |
| DOIs | |
| State | Published - 4 Feb 2026 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- SERS
- Vibrio
- bacterial typing
- deep learning
- on-site sensing
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