Surface enhanced Raman scattering artificial nose for high dimensionality fingerprinting.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
10 01 2020
10 01 2020
Historique:
received:
03
05
2019
accepted:
11
11
2019
entrez:
12
1
2020
pubmed:
12
1
2020
medline:
3
4
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 31924755
doi: 10.1038/s41467-019-13615-2
pii: 10.1038/s41467-019-13615-2
pmc: PMC6954179
doi:
Substances chimiques
Gold
7440-57-5
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
207Subventions
Organisme : Medical Research Council
ID : MR/P024378/1
Pays : United Kingdom
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