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Direct measurement of single-molecule dynamics and reaction kinetics in confinement using time-resolved transmission electron microscopy.

Kayleigh L Y FungStephen T SkowronRuth HayterStephen E MasonBenjamin L WeareNicholas A BesleyQuentin M RamasseChristopher S AllenAndrei N Khlobystov
Published in: Physical chemistry chemical physics : PCCP (2023)
We report experimental methodologies utilising transmission electron microscopy (TEM) as an imaging tool for reaction kinetics at the single molecule level, in direct space and with spatiotemporal continuity. Using reactions of perchlorocoronene (PCC) in nanotubes of different diameters and at different temperatures, we found a period of molecular movement to precede the intermolecular addition of PCC, with a stronger dependence of the reaction rate on the nanotube diameter, controlling the local environments around molecules, than on the reaction temperature (-175, 23 or 400 °C). Once initiated, polymerisation of PCC follows zero-order reaction kinetics with the observed reaction cross section σ obs of 1.13 × 10 -9 nm 2 (11.3 ± 0.6 barn), determined directly from time-resolved TEM image series acquired with a rate of 100 frames per second. Polymerisation was shown to proceed from a single point, with molecules reacting sequentially, as in a domino effect, due to the strict conformational requirement of the Diels-Alder cycloaddition creating the bottleneck for the reaction. The reaction mechanism was corroborated by correlating structures of reaction intermediates observed in TEM images, with molecular weights measured by using mass spectrometry (MS) when the same reaction was triggered by UV irradiation. The approaches developed in this study bring the imaging of chemical reactions at the single-molecule level closer to traditional concepts of chemistry.
Keyphrases
  • single molecule
  • mass spectrometry
  • high resolution
  • electron microscopy
  • atomic force microscopy
  • living cells
  • electron transfer
  • multiple sclerosis
  • optical coherence tomography
  • machine learning