Surface enhanced Raman scattering artificial nose for high dimensionality fingerprinting

Nayoung Kim, Michael R. Thomas, Mads S. Bergholt, Isaac J. Pence, Hyejeong Seong, Patrick Charchar, Nevena Todorova, Anika Nagelkerke, Alexis Belessiotis-Richards, David J. Payne, Amy Gelmi, Irene Yarovsky () and Molly M. Stevens ()
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Nayoung Kim: Department of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London
Michael R. Thomas: Department of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London
Mads S. Bergholt: Department of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London
Isaac J. Pence: Department of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London
Hyejeong Seong: Department of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London
Patrick Charchar: School of Engineering, RMIT University, Melbourne
Nevena Todorova: School of Engineering, RMIT University, Melbourne
Anika Nagelkerke: Department of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London
Alexis Belessiotis-Richards: Department of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London
David J. Payne: Department of Materials, Imperial College London
Amy Gelmi: Department of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London
Irene Yarovsky: School of Engineering, RMIT University, Melbourne
Molly M. Stevens: Department of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: Abstract Label-free surface-enhanced Raman spectroscopy (SERS) can interrogate systems by directly fingerprinting their components’ unique physicochemical properties. In complex biological systems however, this can yield highly overlapping spectra that hinder sample identification. Here, we present an artificial-nose inspired SERS fingerprinting approach where spectral data is obtained as a function of sensor surface chemical functionality. Supported by molecular dynamics modeling, we show that mildly selective self-assembled monolayers can influence the strength and configuration in which analytes interact with plasmonic surfaces, diversifying the resulting SERS fingerprints. Since each sensor generates a modulated signature, the implicit value of increasing the dimensionality of datasets is shown using cell lysates for all possible combinations of up to 9 fingerprints. Reliable improvements in mean discriminatory accuracy towards 100% are achieved with each additional surface functionality. This arrayed label-free platform illustrates the wide-ranging potential of high-dimensionality artificial-nose based sensing systems for more reliable assessment of complex biological matrices.

Date: 2020
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DOI: 10.1038/s41467-019-13615-2

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