Transport of nano-objects in narrow channels: influence of Brownian diffusion, confinement and particle nature

Abstract

This paper presents experimental results about transport of dilute suspensions of nano-objects in silicon-glass micrometric and sub-micrometric channels. Two kinds of objects are used: solid, rigid latex beads and spherical capsule-shaped, soft polymersomes. They are tracked using fluorescence microscopy. Three aspects are studied: confinement (ratio between particle diameter and channel depth), Brownian diffusion and particle nature. The aim of this work is to understand how these different aspects affect the transport of suspensions in narrow channels and to understand the different mechanisms at play. Concerning the solid beads we observe the appearance of two regimes, one where the experimental mean velocity is close to the expected one and another where this velocity is lower. This is directly related to a competition between confinement, Brownian diffusion and advection. These two regimes are shown to be linked to the inhomogeneity of particles distribution in the channel depth, which we experimentally deduce from velocity distributions. This inhomogeneity appears during the entrance process into the sub-micrometric channels, as for hydrodynamic separation or deterministic lateral displacement. Concerning the nature of the particles we observed a shift of transition towards the second regime likely due to the relationships between shear stress and polymersomes mechanical properties which could reduce the inhomogeneity imposed by the geometry of our device.

Tracer diffusion in crowded narrow channels

“…Interestingly, the validity of the Einstein relation is ensured here by the subdominant in time terms in eqs. (32) and (33), as verified in Ref. [137].…”

“…In consequence, one may claim that the super-diffusion in eqs. (32) and (33) paves the way to giant diffusion. We also note a recent Ref.…”

“…On the one hand, this interest stems from the relevance of the problem to a variety of realistic physical, biophysical and chemical systems, as well as important applications in nanotechnology and nanomedicine, e.g., in the creation of artificial molecular nanofilters. Few stray examples include, e.g., transport in porins [2][3][4], through the nuclear pores in eukaryotic cells [5][6][7], or along the microtubules [8] and dendritic spines [9], transport of microswimmers in narrow pores [10][11][12], translocation of polymers in pores [13][14][15][16][17][18] and their sequencing in nanopore-based devices [19], ionic currents across nanopores [20,21], ionic liquids in supercapacitors [22][23][24][25][26][27][28][29], nanoand microfluidics [30][31][32][33] and transport of inertial particles advected by laminar flows [34][35][36]. On the other hand, the diversity and the significance of the problems emerging in this field represent a challenging area of research for theoreticians [37].…”

Viro-fluidics: Real-time analysis of virus production kinetics at the single-cell level

“…When V_max was measured, the one-frame diffusion along the x axis was neglected when average flow speeds were higher than 75 μ m/s, which represents 10 times more than 1D characteristic length of diffusion for a 100-nm bead (∼1.13 μ m during 150 ms frame time). The hydrodynamic interparticle interactions were also neglected when particle concentration was lower than 10 6 particles/mL ( 36 ). Moreover, because δ is close to 1 in the virometry channels, the velocity profile can be considered as parabolic and the average flow velocity was calculated as v = 2/3 v_max.…”

Combining dynamic Monte Carlo with machine learning to study nanoparticle translocation

“…Average values corresponding to the ensemble of objects can be obtained by analyzing the results of many translocation events. Experimental translocation measurements have successfully studied polymeric and inorganic spheres, 6–44 rods, 41,44 cuboids, 45 vesicles 9–11,46–49 and virus-like particles. 20,29,50–58 In particular, it has been successful in characterizing the size, 6,9,13,15,20,21,26,27,29,32,34–36,48,54,57–61 surface charge 5,6,13,15,21,26,32,36,41,57,58,61 and concentration 32,33,35,54 of particles with submicrometer sizes.…”