Electronic versions

DOI

  • Mario Lemmer
    Department of Chemistry, Imperial College London, Imperial College Road, London SW7 2AZ, UK.
  • Michael S Inkpen
    Department of Chemistry, Imperial College London, Imperial College Road, London SW7 2AZ, UK.
  • Katja Kornysheva
    Institute for Cognitive Neuroscience, University College London, Alexandra House, 17-19 Queen Square, London WC1N 3AR, UK.
  • Nicholas J Long
    Department of Chemistry, Imperial College London, Imperial College Road, London SW7 2AZ, UK.
  • Tim Albrecht
    Department of Chemistry, Imperial College London, Imperial College Road, London SW7 2AZ, UK.

The stochastic nature of single-molecule charge transport measurements requires collection of large data sets to capture the full complexity of a molecular system. Data analysis is then guided by certain expectations, for example, a plateau feature in the tunnelling current distance trace, and the molecular conductance extracted from suitable histogram analysis. However, differences in molecular conformation or electrode contact geometry, the number of molecules in the junction or dynamic effects may lead to very different molecular signatures. Since their manifestation is a priori unknown, an unsupervised classification algorithm, making no prior assumptions regarding the data is clearly desirable. Here we present such an approach based on multivariate pattern analysis and apply it to simulated and experimental single-molecule charge transport data. We demonstrate how different event shapes are clearly separated using this algorithm and how statistics about different event classes can be extracted, when conventional methods of analysis fail.

Keywords

  • Journal Article
Original languageEnglish
Article number12922
JournalNature Communications
Volume7
Early online date3 Oct 2016
DOIs
Publication statusPublished - 3 Oct 2016
Externally publishedYes
View graph of relations