SERSbot: Revealing the Details of SERS Multianalyte Sensing Using Full Automation.
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Publication Date
2021-12-24Journal Title
ACS Sens
ISSN
2379-3694
Publisher
American Chemical Society (ACS)
Volume
6
Issue
12
Pages
4507-4514
Language
eng
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Grys, D., de Nijs, B., Huang, J., Scherman, O. A., & Baumberg, J. J. (2021). SERSbot: Revealing the Details of SERS Multianalyte Sensing Using Full Automation.. ACS Sens, 6 (12), 4507-4514. https://doi.org/10.1021/acssensors.1c02116
Description
Funder: Isaac Newton Trust
Funder: Leverhulme Trust
Abstract
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.
Keywords
Lab Automation, Liquid Handling, Competitive Binding, Surface-enhanced Raman, Langmuir Isotherm, Multiplexed Sensing, Quantitative Sers, Lab Robot, Nanogap Sequestration
Sponsorship
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.
Funder references
EPSRC (1783357)
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)
Engineering and Physical Sciences Research Council (EP/G060649/1)
Identifiers
34882398, PMC8715530
External DOI: https://doi.org/10.1021/acssensors.1c02116
This record's URL: https://www.repository.cam.ac.uk/handle/1810/333109
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Licence URL: https://creativecommons.org/licenses/by-nc-nd/4.0/
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