Fersiynau electronig

Dangosydd eitem ddigidol (DOI)

  • Mario Lemmer
    Grand Challenges in Ecosystem and the Environment Initiative, Imperial College London, Silwood Park Campus, Ascot, Berkshire SL5 7PY, UK alexander.papadopulos@plants.ox.ac.uk.
  • Michael S Inkpen
    Grand Challenges in Ecosystem and the Environment Initiative, Imperial College London, Silwood Park Campus, Ascot, Berkshire SL5 7PY, UK alexander.papadopulos@plants.ox.ac.uk.
  • Katja Kornysheva
    University College London
  • Nicholas J Long
    Grand Challenges in Ecosystem and the Environment Initiative, Imperial College London, Silwood Park Campus, Ascot, Berkshire SL5 7PY, UK alexander.papadopulos@plants.ox.ac.uk.
  • Tim Albrecht
    Grand Challenges in Ecosystem and the Environment Initiative, Imperial College London, Silwood Park Campus, Ascot, Berkshire SL5 7PY, UK alexander.papadopulos@plants.ox.ac.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.

Allweddeiriau

Iaith wreiddiolSaesneg
Rhif yr erthygl12922
CyfnodolynNature Communications
Cyfrol7
Dyddiad ar-lein cynnar3 Hyd 2016
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 3 Hyd 2016
Cyhoeddwyd yn allanolIe
Gweld graff cysylltiadau