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Measurements of Particle-Surface Interactions in Both Equilibrium and Nonequilibrium Systems.

Feng CaoJiahao WuYunxing LiTo Ngai
Published in: Langmuir : the ACS journal of surfaces and colloids (2019)
Total internal reflection microscopy (TIRM) is a passive technique that measures colloidal interactions in aqueous solution. A traditional Boltzmann method requires that particles must fluctuate around equilibrium positions for a long time. A method based on multiparticle tracking and drift velocity method was developed to measure interactions in both equilibrium and nonequilibrium systems. This method relaxed the limitation of the traditional Boltzmann method and do not require any external force like optical tweezer. Theoretical predictions of particle sedimentation under the influence of various forces were investigated to determine the proper particle size and solution properties. We found that the polystyrene (PS) particle with a size of 2.1 μm took the longest time to finish sedimentation, and 5% (w/w) sucrose was chosen to suppress the Brownian motion. For single and ensemble particles in equilibrium, the experimental diffusion coefficients and potential energy profiles were consistent with the theoretical prediction. In nonequilibrium experiments, the van der Waals force between the bare/hybrid particles and flat surface was measured, and the silica shell acted to strengthen the van der Waals attraction. This method extends the application of TIRM to nonequilibrium systems without any active control. Moreover, the silica-coated PS core-shell hybrid particles facilitate surface modification with a variety of active chemicals. It would be a great advantage to measure all kinds of long-range interactions between surface-modified particles and surface in aqueous solution with TIRM.
Keyphrases
  • aqueous solution
  • molecular dynamics
  • molecular dynamics simulations
  • single molecule
  • high resolution
  • high speed
  • high throughput
  • optical coherence tomography
  • atomic force microscopy
  • convolutional neural network