On-site quantitative detection of fentanyl in heroin by machine learning-enabled SERS on super absorbing metasurfaces
Abstract The global surge in opioid misuse, particularly fentanyl, presents a formidable public health challenge, highlighted by increasing drug-related mortalities. Our study introduces a novel approach for on-site quantitative detection of fentanyl in heroin, employing machine learning-enabled sur...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
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| Series: | npj Nanophotonics |
| Online Access: | https://doi.org/10.1038/s44310-025-00055-8 |
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| Summary: | Abstract The global surge in opioid misuse, particularly fentanyl, presents a formidable public health challenge, highlighted by increasing drug-related mortalities. Our study introduces a novel approach for on-site quantitative detection of fentanyl in heroin, employing machine learning-enabled surface-enhanced Raman spectroscopy (SERS) on superabsorbing metasurfaces. The metasurface enables superior light absorption (>90%) across a broad wavelength range (580–1100 nm). This architecture facilitates significant electromagnetic field enhancement, over 2.19 × 107, ensuring high sensitivity, uniformity, and reproducibility. Our method precisely captured SERS signals across a detection range of 1–100 μg/mL in fentanyl solutions, fentanyl-heroin mixtures, and fentanyl-spiked saliva, demonstrating its versatility and practical utility. Incorporation of partial least squares regression into our analysis achieved over 93% accuracy in concentration predictions, eliminating the need for pre-data processing or specialized personnel. This marks a key advancement in rapid, accurate fentanyl detection, aiding the fight against the opioid crisis and improving public health safety. |
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| ISSN: | 2948-216X |