期刊
JOURNAL OF MATERIALS CHEMISTRY C
卷 9, 期 37, 页码 12624-12632出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/d1tc02004h
关键词
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资金
- Leverhulme Trust [RPG-2016-393]
- Royal Society [RG150551]
- UCL BEAMS Future Leader Award - EPSRC [EP/P511262/1]
- A*STAR-UCL Research Attachment Programme through the EPSRC M3S CDT [EP/P511262/1]
- Camtech Innovations
- A*STAR BMRC Central Research Fund (UIBR)
Multiplexed detection and quantification of structurally similar drug molecules can be achieved via solution-based surface-enhanced Raman spectroscopy (SERS), with highly reproducible SERS signals and detection limits down to 50 nM for some molecules. Machine learning algorithms can extend the dynamic range and robustness of the sensing scheme, showing potential for applications in therapeutic drug monitoring, food processing, forensics, and veterinary science.
Multiplexed detection and quantification of structurally similar drug molecules, methylxanthine MeX, incl. theobromine TBR, theophylline TPH and caffeine CAF, have been demonstrated via solution-based surface-enhanced Raman spectroscopy (SERS), achieving highly reproducible SERS signals with detection limits down to similar to 50 nM for TBR and TPH, and similar to 1 mu M for CAF. Our SERS substrates are formed by aqueous self-assembly of gold nanoparticles (Au NPs) and supramolecular host molecules, cucurbit[n]urils (CBn, n = 7, 8). We demonstrate that the binding constants can be significantly increased using a host-guest size matching approach, which enables effective enrichment of analyte molecules in close proximity to the plasmonic hotspots. The dynamic range and the robustness of the sensing scheme can be extended using machine learning algorithms, which shows promise for potential applications in therapeutic drug monitoring, food processing, forensics and veterinary science.
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