In this paper, the authors describe their examination and fabrication of a nanoparticle-decorated microneedle substrate and discuss the translational aspect of the microneedle substrate for surface-enhanced Raman spectroscopy (SERS) in order to detect synthetic drugs in blood plasma.
This article reports on the fabrication of a nanoparticle (NP)-decorated microneedle substrate that is both a SERS substrate and a substrate-supported electrospray ionization (ssESI) mass spectrometry (MS) sample ionization platform. The authors show that the chain length of the polymer ligands dictates the nanorod (NR) adsorption process. Importantly, assembling Au NRs onto the micrometer-diameter needle tips allows the formation of highly concentrated electromagnetic hot spots, which provide the SERS enhancement factor as high as 1.0 × 106. The micrometer-sized area of the microneedle top and high electromagnetic field enhancement of the authors’ system can be loosely compared with tip-enhanced Raman spectroscopy, where the apex of a plasmonic NP-functionalized sharp probe produces high-intensity plasmonic hot spots. Utilizing their NR-decorated microneedle substrates, the synthetic drugs fentanyl and alprazolam were analyzed with a subpicomolar limit of detection. Further analysis of drug-molecule interactions on the NR surface utilizing the Langmuir adsorption model suggested that the higher polarizability of fentanyl allows for a stronger interaction with hydrophilic polymer layers on the NR surface. The authors also discuss the translational aspect of the microneedle substrate for both SERS- and ssESI-MS-based detection of these two potent drugs in 10 drug-of-abuse (DOA) patient plasma samples with minimal pre-analysis sample preparation steps. Chemometric analysis for the SERS-based detection showed a very good classification between fentanyl, alprazolam, or a mixture thereof in our selected 10 samples. Most importantly, ssESI-MS analysis also successfully identified fentanyl or alprazolam in these same 10 DOA plasma samples. The authors suggest that their multimodal detection approach presented herein is a highly versatile detection technology that can be applicable to the detection of any analyte type without performing any complicated sample preparation. Publisher Abstract Provided
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