ScholarWorks@성균관대학교

초록

Drug-facilitated crimes (DFC) involve the administration of illicit substances, leaving traces in biological samples within particular period of time. A reliable method for detecting and monitoring illicit drug in biofluids is critical for forensic and clinical applications. Herein, we introduce the hotspot-driven molecular entrapment (HDME) method as highly sensitive strategy for trapping and label-free detection of illicit drug. This approach utilizes in-situ surface growth of plasmonic particles, forming hotspots around analytes and generating intense electromagnetic fields for enhanced Raman signal detection. The HDME technique demonstrated exceptional capability in detecting and monitoring trace levels of drugs in plasma and urine, with consistent results across various concentrations and time intervals. These findings were cross-validated with LC-MS/MS, showing strong correlation and confirming the reliability of HDME for accurate and sensitive analyte detection. Furthermore, HDME was combined with machine learning to validate drug analytes in urine samples with high sensitivity, specificity, and accuracy, confirming the reliability of this method. Urine was selected over plasma due to its non-invasive collection, and suitability for on-site analysis. The integration of HDME with machine learning offers a robust and adaptable platform for detecting and monitoring illicit drug, providing significant potential for advancing forensic investigations and improving clinical diagnostics. © 2025 The Authors

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