Quantitative detection of trace nanoplastics (down to 50 nm) via surface-enhanced Raman scattering based on the multiplex-feature coffee ring
Quantitative detection of trace small-sized nanoplastics (<100 nm) remains a significant challenge in surface-enhanced Raman scattering (SERS). To tackle this issue, we developed a hydrophobic CuO@Ag nanowire substrate and introduced a multiplex-feature analysis strategy based on the coffee ring...
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| Format: | Article |
| Language: | English |
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Institue of Optics and Electronics, Chinese Academy of Sciences
2025-06-01
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| Series: | Opto-Electronic Advances |
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| Online Access: | https://www.oejournal.org/article/doi/10.29026/oea.2025.240260 |
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| author | Xinao Lin Fengcai Lei Xiu Liang Yang Jiao Xiaofei Zhao Zhen Li Chao Zhang Jing Yu |
| author_facet | Xinao Lin Fengcai Lei Xiu Liang Yang Jiao Xiaofei Zhao Zhen Li Chao Zhang Jing Yu |
| author_sort | Xinao Lin |
| collection | DOAJ |
| description | Quantitative detection of trace small-sized nanoplastics (<100 nm) remains a significant challenge in surface-enhanced Raman scattering (SERS). To tackle this issue, we developed a hydrophobic CuO@Ag nanowire substrate and introduced a multiplex-feature analysis strategy based on the coffee ring effect. This substrate not only offers high Raman enhancement but also exhibits a high probability of detection (POD), enabling rapid and accurate identification of 50 nm polystyrene nanoplastics over a broad concentration range (1–10−10 wt%). Importantly, experimental results reveal a strong correlation between the coffee ring formation and the concentration of nanoplastic dispersion. By incorporating Raman signal intensity, coffee ring diameter, and POD as combined features, we established a machine learning-based mapping between nanoplastic concentration and coffee ring characteristics, allowing precise predictions of dispersion concentration. The mean squared error of these predictions is remarkably low, ranging from 0.21 to 0.54, representing a 19 fold improvement in accuracy compared to traditional linear regression-based methods. This strategy effectively integrates SERS with wettability modification techniques, ensuring high sensitivity and fingerprinting capabilities, while addressing the limitations of Raman signal intensity in accurately reflecting concentration changes at ultra-low levels, providing a new idea for precise SERS measurements of nanoplastics. |
| format | Article |
| id | doaj-art-2d7c8674f9984e339a5e27fb59d2db40 |
| institution | Kabale University |
| issn | 2096-4579 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Institue of Optics and Electronics, Chinese Academy of Sciences |
| record_format | Article |
| series | Opto-Electronic Advances |
| spelling | doaj-art-2d7c8674f9984e339a5e27fb59d2db402025-08-20T03:58:18ZengInstitue of Optics and Electronics, Chinese Academy of SciencesOpto-Electronic Advances2096-45792025-06-018611310.29026/oea.2025.240260OEA-2024-0260ZhangchaoQuantitative detection of trace nanoplastics (down to 50 nm) via surface-enhanced Raman scattering based on the multiplex-feature coffee ringXinao Lin0Fengcai Lei1Xiu Liang2Yang Jiao3Xiaofei Zhao4Zhen Li5Chao Zhang6Jing Yu7Shandong Provincial Engineering and Technical Center of Light Manipulation, School of Physics and Electronics, Shandong Normal University, Jinan 250014, ChinaCollege of Chemistry, Chemical Engineering and Materials Science, Institute of Biomedical Sciences, Shandong Normal University, Jinan 250014, ChinaAdvanced Materials Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, ChinaShandong Provincial Engineering and Technical Center of Light Manipulation, School of Physics and Electronics, Shandong Normal University, Jinan 250014, ChinaShandong Provincial Engineering and Technical Center of Light Manipulation, School of Physics and Electronics, Shandong Normal University, Jinan 250014, ChinaShandong Provincial Engineering and Technical Center of Light Manipulation, School of Physics and Electronics, Shandong Normal University, Jinan 250014, ChinaShandong Provincial Engineering and Technical Center of Light Manipulation, School of Physics and Electronics, Shandong Normal University, Jinan 250014, ChinaShandong Provincial Engineering and Technical Center of Light Manipulation, School of Physics and Electronics, Shandong Normal University, Jinan 250014, ChinaQuantitative detection of trace small-sized nanoplastics (<100 nm) remains a significant challenge in surface-enhanced Raman scattering (SERS). To tackle this issue, we developed a hydrophobic CuO@Ag nanowire substrate and introduced a multiplex-feature analysis strategy based on the coffee ring effect. This substrate not only offers high Raman enhancement but also exhibits a high probability of detection (POD), enabling rapid and accurate identification of 50 nm polystyrene nanoplastics over a broad concentration range (1–10−10 wt%). Importantly, experimental results reveal a strong correlation between the coffee ring formation and the concentration of nanoplastic dispersion. By incorporating Raman signal intensity, coffee ring diameter, and POD as combined features, we established a machine learning-based mapping between nanoplastic concentration and coffee ring characteristics, allowing precise predictions of dispersion concentration. The mean squared error of these predictions is remarkably low, ranging from 0.21 to 0.54, representing a 19 fold improvement in accuracy compared to traditional linear regression-based methods. This strategy effectively integrates SERS with wettability modification techniques, ensuring high sensitivity and fingerprinting capabilities, while addressing the limitations of Raman signal intensity in accurately reflecting concentration changes at ultra-low levels, providing a new idea for precise SERS measurements of nanoplastics.https://www.oejournal.org/article/doi/10.29026/oea.2025.240260quantitative detection of trace nanoplasticssurface-enhanced raman scatteringcoffee ringmultiplex-feature analysismachine learning |
| spellingShingle | Xinao Lin Fengcai Lei Xiu Liang Yang Jiao Xiaofei Zhao Zhen Li Chao Zhang Jing Yu Quantitative detection of trace nanoplastics (down to 50 nm) via surface-enhanced Raman scattering based on the multiplex-feature coffee ring Opto-Electronic Advances quantitative detection of trace nanoplastics surface-enhanced raman scattering coffee ring multiplex-feature analysis machine learning |
| title | Quantitative detection of trace nanoplastics (down to 50 nm) via surface-enhanced Raman scattering based on the multiplex-feature coffee ring |
| title_full | Quantitative detection of trace nanoplastics (down to 50 nm) via surface-enhanced Raman scattering based on the multiplex-feature coffee ring |
| title_fullStr | Quantitative detection of trace nanoplastics (down to 50 nm) via surface-enhanced Raman scattering based on the multiplex-feature coffee ring |
| title_full_unstemmed | Quantitative detection of trace nanoplastics (down to 50 nm) via surface-enhanced Raman scattering based on the multiplex-feature coffee ring |
| title_short | Quantitative detection of trace nanoplastics (down to 50 nm) via surface-enhanced Raman scattering based on the multiplex-feature coffee ring |
| title_sort | quantitative detection of trace nanoplastics down to 50 nm via surface enhanced raman scattering based on the multiplex feature coffee ring |
| topic | quantitative detection of trace nanoplastics surface-enhanced raman scattering coffee ring multiplex-feature analysis machine learning |
| url | https://www.oejournal.org/article/doi/10.29026/oea.2025.240260 |
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