SERSbot: Revealing the Details of SERS Multianalyte Sensing Using Full Automation.
American Chemical Society (ACS)
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Grys, D., de Nijs, B., Huang, J., Scherman, O., & Baumberg, J. (2021). SERSbot: Revealing the Details of SERS Multianalyte Sensing Using Full Automation.. ACS Sens https://doi.org/10.1021/acssensors.1c02116
Surface-enhanced Raman spectroscopy (SERS) is considered an attractive candidate for quantitative and multiplexed molecular sensing of analytes whose chemical composition is not fully known. In principle, molecules can be identified through their fingerprint spectrum when binding inside plasmonic hotspots. However, competitive binding experiments between methyl viologen (MV2+) and its deuterated isomer (d8-MV2+) here show that determining individual concentrations by extracting peak intensities from spectra is not possible. This is because analytes bind to different binding sites inside and outside of hotspots with different affinities. Only by knowing all binding constants and geometry-related factors, can a model revealing accurate concentrations be constructed. To collect sufficiently reproducible data for such a sensitive experiment, we fully automate measurements using a high-throughput SERS optical system integrated with a liquid handling robot (the SERSbot). This now allows us to accurately deconvolute analyte mixtures through independent component analysis (ICA) and to quantitatively map out the competitive binding of analytes in nanogaps. Its success demonstrates the feasibility of automated SERS in a wide variety of experiments and applications.
EPSRC Grants (EP/L027151/1, EP/R020965/1, EP/P029426/1) and ERC PICOFORCE (883703). EPSRC grant EP/L015889/1 for the EPSRC Centre for Doctoral Training in Sensor Technologies and Applications.
Engineering and Physical Sciences Research Council (EP/L027151/1)
Engineering and Physical Sciences Research Council (EP/L015889/1)
Engineering and Physical Sciences Research Council (EP/P029426/1)
Engineering and Physical Sciences Research Council (EP/R020965/1)
European Commission Horizon 2020 (H2020) ERC (883703)
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External DOI: https://doi.org/10.1021/acssensors.1c02116
This record's URL: https://www.repository.cam.ac.uk/handle/1810/330745
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