Inhibiting Analyte Theft in Surface-Enhanced Raman Spectroscopy Substrates: Subnanomolar Quantitative Drug Detection

Abstract

Quantitative applications of surface-enhanced Raman spectroscopy (SERS) often rely on surface partition layers grafted to SERS substrates to collect and trap-solvated analytes that would not otherwise adsorb onto metals. Such binding layers drastically broaden the scope of analytes that can be probed. However, excess binding sites introduced by this partition layer also trap analytes outside the plasmonic "hotspots". We show that by eliminating these binding sites, limits of detection (LODs) can effectively be lowered by more than an order of magnitude. We highlight the effectiveness of this approach by demonstrating quantitative detection of controlled drugs down to subnanomolar concentrations in aqueous media. Such LODs are low enough to screen, for example, urine at clinically relevant levels. These findings provide unique insights into the binding behavior of analytes, which are essential when designing high-performance SERS substrates.

Keywords: SERS; THC; drug detection; nanoparticles; self-assembly; spice; synthetic cannabinoids; tetrahydrocannabinol.

Figure 1

SERS substrate formation and properties. (a) Adding CB[n] to a solution of AuNPs (diameter 60 nm) induces aggregation observed as a color change from red to gray, with interparticle spacing of 0.9 nm (the height of the spacer). (b) Extinction spectra of the self-assembly process showing the formation of chain modes in solution over time. Dashed line: finite difference time domain simulated the far-field scattering spectrum for a six-membered AuNP chain. (c) Scanning electron microscopy (SEM) image of AuNP aggregates formed by CB[n] self-assembly showing fractal-like structures. Inset: The modeled AuNP chain showing localized hotspots between the nanoparticles with field enhancements |E/E₀| up to 250. (d) SERS spectra from AuNP aggregates under illumination at 532 nm (top), 633 nm (middle), and 785 nm (bottom) in counts per second per milliWatt (cts·s^–1·mW^–1).

Figure 2

Analyte incorporation mechanisms in plasmonic hotspots. (a) Methyl viologen (MV²⁺) has a strong binding affinity toward CB[7], binding outside the plasmonic hotspots also, effectively lowering the probed MV²⁺ concentration (counter ions have been omitted for clarity). (b) CB[5] is too small to bind MV²⁺ inside, but the constricted hotspot volume (orange shaded) binds analytes interstitially. (c) (Top) SERS spectra for MV²⁺ using CB[5] for different MV²⁺ concentrations down to picomolar. (Bottom) Principal component analysis components from CB[5]:MV²⁺ concentration series, matching CB[5] (comp I) and MV²⁺ bulk Raman (comp II). (d) Integrated spectral changes vs MV²⁺ concentration for AuNP aggregates formed with CB[5] and CB[7]. (e) SERS spectra showing the effect of adding (i) CB[5], then (ii) MV²⁺ resulting in a clear new peak at 1650 cm^–1, and subsequently (iii) CB[7], lowering the intensity of the peak at 1650 cm^–1 as CB[7] scavenges analytes away from the hotspot.

Figure 3

Calculated molecular electrostatic potential maps in implicit water for both CB[5] and citrate showing a strong negative potential for citrate and neutral/positive potential for CB[5].

Figure 4

Molecular dynamics simulations of Δ⁹-tetrahydrocannabinol (THC) interacting with different-sized CB[n] spacers. (a) Scheme depicting the biasing coordinate used for the umbrella sampling (US) free energy calculations for a THC molecule entering the CB[n] cavity, with explicit water. (b) Free energy profiles calculated for each THC–CB[n] complex as a function of center-of-mass (COM) distance showing a free energy dip of −9 and −11 kcal mol^–1 for THC–CB[7] and THC–CB[8] complexes, respectively, decreased binding affinity for CB[6], and no favorable binding free energy for CB[5].

Figure 5

Influence of the analyte binding mechanism on analyte detection. (a) THC binding affinities to each of the CB[n] spacers, modeled using DFT calculations, see Methods for details. (b) Experimental PCA loading plots from concentration series of each THC–CB[n] complex showing (top) comp II: THC and (bottom) comp III: unassigned molecular interactions. (c) PCA scores for each of the four complexes show an increase in scores (proportional to the signal strength) with decrease in the CB[n] spacer size (arrow).

Figure 6

Nonspecific binding of plasmonic hotspots. (a) Four different analytes: THC (2) and three synthetic analogues (3), (4), and (5). (b) PCA loading plots showing distinct spectra for each compound, with little difference whether CB[5] or CB[6] is used. (c) PCA scores and Langmuir isotherm fits for each of the components show LODs clearly in the nanomolar regime with compounds (2–4) showing LODs near or below 1 nanomolar concentration.

Figure 7

Validation of the LOD for analyte (2). (a) SERS spectra of CB[5]:AuNP aggregates with four different analyte concentrations (2.5, 0.5, 0.1, and 0.02 nM). The zoomed-in region of interest showing small spectral changes. (c) SERS spectra with the background subtracted, showing peaks for analyte (2) exceeding the noise threshold for 2.5 and 0.5 nM concentrations (arrows).