title: Three-dimensional surface-enhanced Raman scattering hotspots in spherical colloidal superstructure for identification and detection of drugs in human urine.
abstract: Rapid component separation and robust surface-enhanced Raman scattering (SERS) identification of drugs in real human urine remain an attractive challenge because of the sample complexity, low molecular affinity for metal surface, and inefficient use of hotspots in one- or two-dimensional (2D) geometries. Here, we developed a 5 min strategy of cyclohexane (CYH) extraction for separating amphetamines from human urine. Simultaneously, an oil-in-water emulsion method is used to assemble monodisperse Ag nanoparticles in the CYH phase into spherical colloidal superstructures in the aqueous phase. These superstructures create three-dimensional (3D) SERS hotspots which exist between every two adjacent particles in 3D space, break the traditional 2D limitation, and extend the hotspots into the third dimension along the z-axis. In this platform, a conservative estimate of Raman enhancement factor is larger than 10(7), and the same CYH extraction processing results in a high acceptability and enrichment of drug molecules in 3D hotspots which demonstrates excellent stability and reproducibility and is suitable for the quantitative examination of amphetamines in both aqueous and organic phases. Parallel ultraperformance liquid chromatography (UPLC) examinations corroborate an excellent performance of our SERS platform for the quantitative analysis of methamphetamine (MA) in both aqueous solution and real human urine, of which the detection limits reach 1 and 10 ppb, respectively, with tolerable signal-to-noise ratios. Moreover, SERS examinations on different proportions of MA and 3,4-methylenedioxymethamphetamine (MDMA) in human urine demonstrate an excellent capability of multiplex quantification of ultratrace analytes. By virtue of a spectral classification algorithm, we realize the rapid and accurate recognition of weak Raman signals of amphetamines at trace levels and also clearly distinguish various proportions of multiplex components. Our platform for detecting drugs promises to be a great prospect for a rapid, reliable, and on-spot analyzer.
authors: Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China.
authors: Zhenzhen
authors: Z
authors: Han
authors: Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China.
authors: Honglin
authors: H
authors: Liu
authors: Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China.
authors: Bin
authors: B
authors: Wang
authors: Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China.
authors: Shizhuang
authors: S
authors: Weng
authors: Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China.
authors: Liangbao
authors: L
authors: Yang
authors: Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui 230031, China.
authors: Jinhuai
authors: J
authors: Liu
mesh_terms: D000662:Amphetamines / Q000652:urine*; D003102:Colloids / Q000737:chemistry; D003510:Cyclohexanes / Q000737:chemistry*; D006801:Humans; D010316:Particle Size; D013059:Spectrum Analysis, Raman; D013499:Surface Properties
chemical_list: D000662:Amphetamines; D003102:Colloids; D003510:Cyclohexanes; C506365:Cyclohexane