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Accessing self-diffusion on nanosecond time and nanometre length scales with minute kinetic resolution.

Christian BeckFelix Roosen-RungeMarco GrimaldoDominik ZellerJudith PetersFrank SchreiberTilo Seydel
Published in: Journal of applied crystallography (2024)
Neutron spectroscopy uniquely and non-destructively accesses diffusive dynamics in soft and biological matter, including for instance proteins in hydrated powders or in solution, and more generally dynamic properties of condensed matter on the molecular level. Given the limited neutron flux resulting in long counting times, it is important to optimize data acquisition for the specific question, in particular for time-resolved (kinetic) studies. The required acquisition time was recently significantly reduced by measurements of discrete energy transfers rather than quasi-continuous neutron scattering spectra on neutron backscattering spectrometers. Besides this reduction in acquisition times, smaller amounts of samples can be measured with better statistics, and most importantly, kinetically changing samples, such as aggregating or crystallizing samples, can be followed. However, given the small number of discrete energy transfers probed in this mode, established analysis frameworks for full spectra can break down. Presented here are new approaches to analyze measurements of diffusive dynamics recorded within fixed windows in energy transfer, and these are compared with the analysis of full spectra. The new approaches are tested by both modeled scattering functions and a comparative analysis of fixed energy window data and full spectra on well understood reference samples. This new approach can be employed successfully for kinetic studies of the dynamics focusing on the short-time apparent center-of-mass diffusion.
Keyphrases
  • density functional theory
  • energy transfer
  • single molecule
  • big data
  • high resolution
  • case control
  • magnetic resonance imaging
  • machine learning
  • solid state
  • magnetic resonance
  • molecular dynamics
  • data analysis