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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| Format: | Article |
| Language: | English |
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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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| author | Yingkun Zhu Haomin Song Ruiying Liu Yunyun Mu Murali Gedda Abdullah N. Alodhay Lei Ying Qiaoqiang Gan |
| author_facet | Yingkun Zhu Haomin Song Ruiying Liu Yunyun Mu Murali Gedda Abdullah N. Alodhay Lei Ying Qiaoqiang Gan |
| author_sort | Yingkun Zhu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c5e24667a8834739bad94640c42e0369 |
| institution | Kabale University |
| issn | 2948-216X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Nanophotonics |
| spelling | doaj-art-c5e24667a8834739bad94640c42e03692025-02-09T12:40:33ZengNature Portfolionpj Nanophotonics2948-216X2025-02-01211910.1038/s44310-025-00055-8On-site quantitative detection of fentanyl in heroin by machine learning-enabled SERS on super absorbing metasurfacesYingkun Zhu0Haomin Song1Ruiying Liu2Yunyun Mu3Murali Gedda4Abdullah N. Alodhay5Lei Ying6Qiaoqiang Gan7Material Science and Engineering, Physical Science and Engineering Division, King Abdullah University of Science and TechnologyMaterial Science and Engineering, Physical Science and Engineering Division, King Abdullah University of Science and TechnologyDepartment of Electrical Engineering, University at Buffalo, The State University of New YorkMaterial Science and Engineering, Physical Science and Engineering Division, King Abdullah University of Science and TechnologyMaterial Science and Engineering, Physical Science and Engineering Division, King Abdullah University of Science and TechnologyDepartment of Physics and Astronomy, College of Science, King Saud UniversityDepartment of Electrical Engineering, University at Buffalo, The State University of New YorkMaterial Science and Engineering, Physical Science and Engineering Division, King Abdullah University of Science and TechnologyAbstract 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.https://doi.org/10.1038/s44310-025-00055-8 |
| spellingShingle | Yingkun Zhu Haomin Song Ruiying Liu Yunyun Mu Murali Gedda Abdullah N. Alodhay Lei Ying Qiaoqiang Gan On-site quantitative detection of fentanyl in heroin by machine learning-enabled SERS on super absorbing metasurfaces npj Nanophotonics |
| title | On-site quantitative detection of fentanyl in heroin by machine learning-enabled SERS on super absorbing metasurfaces |
| title_full | On-site quantitative detection of fentanyl in heroin by machine learning-enabled SERS on super absorbing metasurfaces |
| title_fullStr | On-site quantitative detection of fentanyl in heroin by machine learning-enabled SERS on super absorbing metasurfaces |
| title_full_unstemmed | On-site quantitative detection of fentanyl in heroin by machine learning-enabled SERS on super absorbing metasurfaces |
| title_short | On-site quantitative detection of fentanyl in heroin by machine learning-enabled SERS on super absorbing metasurfaces |
| title_sort | on site quantitative detection of fentanyl in heroin by machine learning enabled sers on super absorbing metasurfaces |
| url | https://doi.org/10.1038/s44310-025-00055-8 |
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