Simulation Study of Seemingly Fickian but Heterogeneous Dynamics of Two Dimensional Colloids

Kim, Jeongmin; Kim, Chan-Joong; Sung, Bong June

doi:10.1103/physrevlett.110.047801

Cited by 102 publications

(115 citation statements)

References 28 publications

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“…We notice that some theoretical results suggested the non-Gaussian parameter α would decrease rapidly to zero at the beginning of the long-time Brownian stage. 21,24 However, the present results show that the non- …”

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confidence: 55%

“…20,21 The theoretical models considering particle restriction could not predict the features of higher moment of displacement. 13,18 Other physical influences such as the interaction between particle and surrounding molecules 22,23 and the dynamics in the heterogeneous structures 17,20,24,25 were also addressed, yet no consensus has been reached. Thus, the purpose of this study is to identify the physical source of nonGaussianity in confined diffusion and clarify its characteristics.…”

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confidence: 99%

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Probing Non-Gaussianity in Confined Diffusion of Nanoparticles

Xue

Zheng

Chen

et al. 2016

J. Phys. Chem. Lett.

103

116

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Confined diffusion is ubiquitous in nature. Ever since the “anomalous yet Brownian” motion was observed, the non-Gaussianity in confined diffusion has been unveiled as an important issue. In this Letter, we experimentally investigate the characteristics and source of non-Gaussian behavior in confined diffusion of nanoparticles suspended in polymer solutions. A time-varied and size-dependent non-Gaussianity is reported based on the non-Gaussian parameter and displacement probability distribution, especially when the nanoparticle’s size is smaller than the typical polymer mesh size. This non-Gaussianity does not vanish even at the long-time Brownian stage. By inspecting the displacement autocorrelation, we observe that the nanoparticle–structure interaction, indicated by the anticorrelation, is limited in the short-time stage and makes little contribution to the non-Gaussianity in the long-time stage. The main source of the non-Gaussianity can therefore be attributed to hopping diffusion that results in an exponential probability distribution with the large displacements, which may also explain certain processes dominated by rare events in the biological environment.

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confidence: 55%

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confidence: 99%

Probing Non-Gaussianity in Confined Diffusion of Nanoparticles

Xue

Zheng

Chen

et al. 2016

J. Phys. Chem. Lett.

103

116

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“…This behavior was termed "anomalous yet Brownian" diffusion [22]. Very recently, similar behavior was also observed for diffusion of tracer molecules on polymer thin films [24] and in simulations of a 2D system of discs [25]. Since this is observed in several different cases, it is likely a generic feature of a certain class of systems.…”

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confidence: 54%

Diffusing Diffusivity: A Model for Anomalous, yet Brownian, Diffusion

2014

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Wang et al. [PNAS 106 (2009) 15160] have found that in several systems the linear time dependence of the mean-square displacement (MSD) of diffusing colloidal particles, typical of normal diffusion, is accompanied by a non-Gaussian displacement distribution (DisD), with roughly exponential tails at short times, a situation they termed "anomalous yet Brownian" diffusion. The diversity of systems in which this is observed calls for a generic model. We present such a model where there is "diffusivity memory" but no "direction memory" in the particle trajectory, and we show that it leads to both a linear MSD and a non-Gaussian DisD at short times. In our model, the diffusivity is undergoing a (perhaps biased) random walk, hence the expression "diffusing diffusivity". The DisD is predicted to be exactly exponential at short times if the distribution of diffusivities is itself exponential, but an exponential remains a good fit to the DisD for a variety of diffusivity distributions. Moreover, our generic model can be modified to produce subdiffusion.In a microscopically homogeneous and rheologically simple (Newtonian) fluid like water, the diffusion of microscopic particles obeys simple laws of Brownian motion known since Einstein [1]. For instance, the mean-square displacement (MSD) x 2 of a particle along a particular direction, x, is linear in time t,where D is the diffusion constant, or the diffusivity, while the distribution of displacements is Gaussian [2]. In "crowded" fluids containing colloidal particles, macromolecules, filaments, etc., the situation can be more complicated and Eq. (1) is generally not valid at all times. In many such cases (see, e.g., Refs. [3-13]), experimental data are consistent withwhere ν < 1, over a significant time range. Processes described by Eq. (2) with ν = 1 are called anomalous diffusion, more specifically, subdiffusion for ν < 1. While much experimental work has concentrated on the MSD, the full displacement distribution (DisD) can be measured using single-particle tracking techniques (SPT) [3-6, 11, 12, 14, 15]. In the continuous-time random walk (CTRW) model of anomalous diffusion [16,17] the DisD is significantly non-Gaussian with a characteristic cusp at x = 0 [18]. However, the fractional Brownian motion (fBm) model [19][20][21] demonstrates that the combination of anomalous MSD with the normal, Gaussian shape of the DisD is possible.On the other hand, it is often tacitly assumed that if the DisD is non-Gaussian, then the factors that cause it to deviate from Gaussian should also make the MSD nonlinear. Recent SPT experiments by Granick's group [22,23] show that this is not always the case. Several systems were considered: submicroscopic polystyrene beads on the surface of a lipid bilayer tube [22], beads in an entangled solution of actin filaments [22], and liposomes in a nematic solution of aligned actin filaments [23]. In all three systems, the MSD is essentially precisely linear over the whole experimental time range, from ∼ 0.1 s to a few seconds. Yet, coexisting w...

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“…Varying from the situation at silica/water interfaces [18], there are multiple peaks in the range of | x| ≥ 1.3Å. Kim et al [30] suggest that G s ( x, t) often shows multiple peaks due to mobile particles undergoing hopping motions. Here, the positions of the second peaks in G s ( x, t) are at | x| = 2.5Å which exactly equal to 0.5a.…”

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confidence: 97%