Rapid identification and quantification of opioid drugs are of significant importance and an urgent need in drug regulation and control, considering the serious social and economic impact of the opioid epidemic in the United States. Unfortunately, techniques for accurate detection of these opioids, particularly for fentanyl, an extremely potent synthetic drug of abuse and a main perpetrator in the opioid crisis, are often not readily accessible. Therefore, a fast, highly sensitive, and preferably quantitative technique, with excellent portability, is highly desirable. Such a technique can potentially offer timely and crucial information for drug control officials, as well as health professionals, about drug distribution and overdose prevention. We therefore propose a portable surface-enhanced Raman scattering (SERS) approach by pairing an easy to perform yet reliable SERS protocol with a compact Raman module suitable for rapid, on-site identification and quantification of trace fentanyl. Fentanyl spiked in urine control was successfully detected at concentrations as low as 5 ng/mL. Portable SERS also enabled detection of trace fentanyl laced in recreational drugs at mass concentrations as low as 0.05% (5 ng in 10 μg total) and 0.1% (10 ng in 10 μg total) in heroin and tetrahydrocannabinol (THC), respectively. Drug interaction with the nanoparticle surface was simulated through molecular dynamics to investigate the molecular adsorption mechanism and account for SERS signal differences observed for opioid drugs. Furthermore, resolution of fentanyl in binary and ternary opioid mixtures was readily achieved with multivariate data analysis. In sum, we developed a rapid, highly sensitive, and reliably quantitative method for trace fentanyl analysis by synergizing a streamlined SERS procedure and a portable Raman module at low cost.
The development of surface-enhanced Raman spectroscopy (SERS)-based sensors necessitates a deeper understanding of the analyte–nanoparticle interaction. For optimal reliability, factors that may affect the resulting spectra need to be understood. First and foremost, the signal enhancement (and hence the improved sensitivity) offered by these systems highly relies on the localization of molecules or moieties in molecules as close as possible to the nanoparticle surface and decreases the farther a molecule is from the surface. Furthermore, the relative peak intensity, and thus the possibility to rely on a specific peak (or set of peaks) to build a calibration curve, depends on the orientation of the molecule with respect to the metallic surface due to the tensorial nature of the Raman polarizability. As a consequence, a change in analyte orientation on a nanoparticle surface impacts the resulting spectral pattern. Herein, factors that affect analyte orientation on a nanoparticle surface and their effect on the resulting SERS spectra are investigated. To do so, two unique nanostar morphologies and three analytes were selected. SERS spectra were acquired at varying analyte concentrations, and deconvoluted. X-ray photoelectron spectroscopy (XPS) and molecular dynamics (MD) simulations were conducted to confirm the hypothesized adsorbate/nanostars environment. Our study reveals three factors theorized to impact the molecular orientations: (1) analyte concentration, (2) nanoparticle surface properties, and (3) analyte–nanoparticle bond nature. Results herein suggest that when the analyte concentration is sufficiently high, the molecules reorient from parallel to perpendicular or remain perpendicular relative to the nanoparticle surface compared to the situation at low concentration. The way in which the analyte and nanoparticle interact (e.g., physisorb or chemisorb) will determine the preferred analyte orientation at low concentration. If covalently bound, this preliminary orientation is believed to be dictated by the preferred bond angle between surface and bound moiety. If physisorbed, the analyte will be parallel relative to the nanostar surface at low concentrations and then reorient perpendicular at increased concentrations. The work presented here, explaining in detail the concentration-dependent nature of the analyte orientation, will aid in the development of more reliable SERS sensors.
Metal–organic frameworks (MOFs) are promising materials for selectively removing sulfur contaminants from petroleum fuels. Unlike gas adsorption, there are no clear rules for their desulfurization performance in liquid fuels. In this work, we use MOF-505, UMCM-150, and MOF-177 as representative materials and perform molecular dynamics simulations to investigate the interactions between the MOFs and two commonly observed organosulfur compounds, benzothiophene (BT) and 4,6-dimethyldibenzothiophene (DMDBT), in the absence and presence of a model fuel isooctane. From the simulations, we find that the conjugated organosulfur compounds bind to the MOF linkers through π stacking, and the difference in the intermolecular forces allows for their separation from the isooctane solvents. The chemical nature of the molecules and the size matching effect combine to modulate the adsorption geometries, translational and rotational movements, and uptake efficiencies of BT and DMDBT. These findings reveal the key interactions that drive the binding of BT and DMDBT in the three MOFs, which will aid the design of materials for efficient adsorptive desulfurization to produce clean fuels.
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