Evidence map›Paper›PMID 31924755›Full record

ArticleNature communications2020

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 and 3 more

Abstract read
In one paragraph

Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

47 citing papers in PubMed.

  1. Article
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  8. Designing Physical Unclonable Functions From Optically Active Materials.Advanced materials (Deerfield Beach, Fla.) · 2025
    Review
  9. Review
  10. Article
  11. Article
  12. Article
  13. Review
  14. Review
  15. Article
  16. Paper/GO/e-Au flexible SERS sensors forNanoscale advances · 2024
    Article
  17. Article
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Nayoung KimDepartment of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London, London, SW7 2AZ, UK.ORCID http://orcid.org/0000-0002-3699-6458
Michael R ThomasDepartment of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London, London, SW7 2AZ, UK.
Mads S BergholtDepartment of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London, London, SW7 2AZ, UK.
Isaac J PenceDepartment of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London, London, SW7 2AZ, UK.ORCID http://orcid.org/0000-0002-5635-1374
Hyejeong SeongDepartment of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London, London, SW7 2AZ, UK.ORCID http://orcid.org/0000-0002-9877-296X
Patrick CharcharSchool of Engineering, RMIT University, Melbourne, Victoria, Australia.ORCID http://orcid.org/0000-0002-9201-4711
Nevena TodorovaSchool of Engineering, RMIT University, Melbourne, Victoria, Australia.ORCID http://orcid.org/0000-0001-6358-2974
Anika NagelkerkeDepartment of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London, London, SW7 2AZ, UK.
Alexis Belessiotis-RichardsDepartment of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London, London, SW7 2AZ, UK.ORCID http://orcid.org/0000-0001-6838-5961
David J PayneDepartment of Materials, Imperial College London, London, SW7 2AZ, UK.ORCID http://orcid.org/0000-0002-2120-6679
Amy GelmiDepartment of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London, London, SW7 2AZ, UK.
Irene YarovskySchool of Engineering, RMIT University, Melbourne, Victoria, Australia. irene.yarovsky@rmit.edu.a.ORCID http://orcid.org/0000-0002-4033-5150
Molly M StevensDepartment of Materials, Department of Bioengineering and Institute of Biomedical Engineering, Imperial College London, London, SW7 2AZ, UK. m.stevens@imperial.ac.uk.ORCID http://orcid.org/0000-0002-7335-266X

Funding

Medical Research Council MR/P024378/1
6 · The paper itself

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.

Indexed as

Biosensing TechniquesElectronic NoseChemical PhenomenaGoldMetal NanoparticlesModels, BiologicalMolecular Dynamics SimulationMultivariate AnalysisSpectrum Analysis, RamanGold

Identifiers

PMID31924755
PMCPMC6954179

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.