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Playing catch and release with single molecules: mechanistic insights into plasmon-controlled nanogaps.

Katrin F DomkeAlbert C Aragonès
Published in: Nanoscale (2022)
Single-molecule (SM) detection is essential for investigating processes at the molecular level. Nanogap-based detection approaches have proven to be highly accurate SM capture and detection platforms in the last decade. Unfortunately, these approaches face several inherent drawbacks, such as short detection times and the effects of Brownian motion, that can hinder molecular capture. Nanogap-based SM detection approaches have been successfully coupled to optical-based setups to exploit nearfield-assisted trapping to overcome these drawbacks and thus improve SM capture and detection. Here we present the first mechanistic study of nearfield effects on SM capture and release in nanogaps, using unsupervised machine learning methods based on hidden Markov models. We show that the nearfield strength can manipulate the kinetics of the SM capture and release processes. With increasing field strength, the rate constant of the capture kinetics increase while the release kinetics decrease, favouring the former over the latter. As a result, the SM capture state is more likely and more stable than the release state above a specific threshold nearfild strength. We have also estimated the decrease in the capture free-energy profile and the increase in the release profiles to be around 5 kJ mol -1 for the laser powers employed, ranging from laser-OFF conditions to 11 mW μm -2 . We envisage that our findings can be combined with the electrocatalytic capabilities of the (nearfield) nanogap to develop next-generation molecular nanoreactors. This approach will open the door to highly efficient SM catalysis with precise extended monitoring timescales facilitated through the longer residence times of the reactant trapped inside the nanogap.
Keyphrases
  • single molecule
  • loop mediated isothermal amplification
  • machine learning
  • label free
  • real time pcr
  • highly efficient
  • high speed
  • high resolution
  • reduced graphene oxide
  • deep learning
  • fluorescent probe