Electrodeposited Surface-Enhanced Raman Scattering Substrates for Point-of-Care Drug Checking

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Wilson, Nicholas
SERS , Sensors , Nanomaterials , Fentanyl , Harm Reduction , Drug Checking , Multivariate Analysis , Machine Classification , Opioid Crisis , Point-of-care Device
Surface-enhanced Raman scattering (SERS) is an analytical technique that can provide ultra-sensitive chemical detection on a mobile platform when implemented on handheld Raman spectrometers. However, the large laser spot size of handheld Raman spectrometers requires SERS substrates of sufficient surface area. In this work, we present a straightforward method of fabricating silver nanostructured SERS substrates, and subsequently demonstrate the substrates’ viability as drug checking tools to help combat the opioid crisis. We first demonstrate that electrochemically reducing silver ions onto silicon-gold microchips from a large `bath’ of aqueous solution results in highly-branched, nanostructured films that grow laterally across the silicon surface. The process is found to decrease production time and improve batch-to-batch reproducibility when compared to the existing 'droplet' protocol, which uses a smaller volume of reagent solution. In addition, we study the effect of temperature and mass transfer on lateral nanostructure growth with the intent of achieving large surface area substrates for use with handheld Raman spectrometers. While neither of these factors is found to have a significant effect on the extent of nanostructure growth, the study confirms that the nanostructured films grow under a mass transfer-controlled regime. We finally hypothesize that lateral growth is limited by electrical impedance within the nanostructure. We subsequently establish our SERS substrates’ viability as a drug checking tool, specifically for the detection of fentanyl in drugs. We determine fentanyl’s limit of detection on our platform as 0.078 ppm. SERS spectra of fentanyl, furanylfentanyl, and carfentanil are classified using multivariate analysis coupled with machine classification models. The classification model also identifies trace fentanyl amidst high concentrations of heroin and caffeine. Finally, we achieve SERS fentanyl detection using a handheld Raman device, thus setting a precedent for point-of-care drug checking.
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