Journal
NATURE COMMUNICATIONS
Volume 11, Issue 1, Pages -Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s41467-019-13615-2
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Funding
- Korean Government Scholarship for Study Overseas by the Korean Ministry of Education
- i-sense Engineering and Physical Sciences Research Council (EPSRC) IRC in Early Warning Sensing Systems for Infectious Diseases [EP/K031953/1, EP/R00529X/1]
- Medical Research Council (MRC) grant m-Africa [MR/P024378/1]
- European Union [701713, 797311, 660757]
- Whitaker International Program, Institute of International Education, United States of America
- National Research Foundation of Korea (NRF) - Ministry of Education [2017R1A6A3A03007397]
- National Institute for Health Research (NIHR) Imperial Biomedical Research Centre
- Institute of Cancer Research, London, through the joint Cancer Research Centre of Excellence (CRCE)
- Royal Society [UF100105, UF150693]
- EPSRC [EP/M028291/1]
- Australian Research Council [DP140101888, DP170100511]
- EPSRC CDT for the Advanced Characterisation of Materials [EP/L015277/1]
- Australian government via the NCI [e87]
- EPSRC [EP/M028291/1, EP/K031953/1] Funding Source: UKRI
- MRC [MR/P024378/1] Funding Source: UKRI
- Marie Curie Actions (MSCA) [660757, 701713, 797311] Funding Source: Marie Curie Actions (MSCA)
- National Research Foundation of Korea [2017R1A6A3A03007397] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- Austrian Science Fund (FWF) [E87] Funding Source: Austrian Science Fund (FWF)
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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.
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