Tracer diffusion in a sea of polymers with binding zones: mobile vs. frozen traps

Samanta, Nairhita; Chakrabarti, Ramananda

doi:10.1039/c6sm01943a

Cited by 65 publications

(72 citation statements)

References 53 publications

(52 reference statements)

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“…Hence, we can get an intuitive picture about the dynamics of the probe in polymer gel. In general, CTRW and FBM both operate at the same time and account for the trapped motion that lead to negative velocity autocorrelation at short time [9] but as the polymers become more rigid the contributions from confined CTRW dominate.…”

Section: B Velocity Autocorrelation (C V (τ ))mentioning

confidence: 99%

Transport of probe particles in a polymer network: effects of probe size, network rigidity and probe–polymer interaction

et al. 2019

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Fundamental understanding of the effect of microscopic parameters on the dynamics of probe particles in different complex environments has wide implications. Examples include diffusion of proteins in the biological hydrogels, porous media, polymer matrix, etc. Here, we use extensive molecular dynamics simulations to investigate the dynamics of the probe particle in a polymer network on a diamond lattice which provides substantial crowding to mimic the cellular environment. Our simulations show that the dynamics of the probe increasingly becomes restricted, non-Gaussian and subdiffusive on increasing the network rigidity, binding affinity and the probe size.In addition, the velocity autocorrelation functions show negative dips owing to the viscoelasticity and caging due to surrounding network. These observations go with general experimental findings. Surprisingly for a probe particle of size comparable to the mesh size, unrestricted motion engulfing large length scales has been observed.This happens with the more flexible polymer network, which is easily pushed by the bigger probe. Our study gives a general qualitative picture of transport of probes in gel like medium, as encountered in different contexts.

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Section: B Velocity Autocorrelation (C V (τ ))mentioning

confidence: 99%

Transport of probe particles in a polymer network: effects of probe size, network rigidity and probe–polymer interaction

et al. 2019

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confidence: 93%

“…At the same time, the central limit theorem ensures that longer-time displacements are Gaussian, with linearly increasing MSD. This picture, developed qualitatively in [12,13] and explained theoretically * Present address: in [25], has inspired much theoretical investigation [26][27][28][29][30][31].In the experiments done to date, the characteristics of the complex environment, such as the local value of D, were not known, except perhaps statistically. Here, we report experimental observations of single-particle diffusion in a system where the underlying D variations are known independently.…”

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confidence: 99%

Test of the diffusing-diffusivity mechanism using near-wall colloidal dynamics

2017

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The mechanism of diffusing diffusivity predicts that, in environments where the diffusivity changes gradually, the displacement distribution becomes non-Gaussian, even though the mean-square displacement grows linearly with time. Here, we report single-particle tracking measurements of the diffusion of colloidal spheres near a planar substrate. Because the local effective diffusivity is known, we have been able to carry out a direct test of this mechanism for diffusion in inhomogeneous media.

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“…We also mention ecological processes, involving the characterisation of organism movement and dispersal [53,54], as well as processes, that are Brownian but non-Gaussian in certain time windows of their dynamics. These concern the dynamics of disordered solids, such as glasses and supercooled liquids [55][56][57] as well as interfacial dynamics [58,59]. Also anomalous diffusion processes of the viscoelastic class that typically are expected to exhibit Gaussian statistic of displacements, were reported to have non-Gaussian displacements along with distinct distributions of diffusivity values.…”

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confidence: 99%

Random diffusivity from stochastic equations: comparison of two models for Brownian yet non-Gaussian diffusion

et al. 2018

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A considerable number of systems have recently been reported in which Brownian yet non-Gaussian dynamics was observed. These are processes characterised by a linear growth in time of the mean squared displacement, yet the probability density function of the particle displacement is distinctly non-Gaussian, and often of exponential (Laplace) shape. This apparently ubiquitous behaviour observed in very different physical systems has been interpreted as resulting from diffusion in inhomogeneous environments and mathematically represented through a variable, stochastic diffusion coefficient. Indeed different models describing a fluctuating diffusivity have been studied. Here we present a new view of the stochastic basis describing time-dependent random diffusivities within a broad spectrum of distributions. Concretely, our study is based on the very generic class of the generalised Gamma distribution. Two models for the particle spreading in such random diffusivity settings are studied. The first belongs to the class of generalised grey Brownian motion while the second follows from the idea of diffusing diffusivities. The two processes exhibit significant characteristics which reproduce experimental results from different biological and physical systems. We promote these two physical models for the description of stochastic particle motion in complex environments. uncovered anomalous diffusion of the power-law formwith the anomalous diffusion exponent 0<α<1 and the generalised diffusion coefficient D α [11], for the motion of charge carriers in amorphous semiconductors [12]. With the advance of modern microscopy techniques, in particular, superresolution microscopy, as well as massive progress in supercomputing, anomalous diffusion of the type (3) has been reported in numerous complex and biological systems [13,14]. Thus, subdiffusion with 0<α<1 was observed for submicron tracers in the crowded cytoplasm of biological cells [15][16][17][18][19] as well as in artificially crowded environments [20][21][22][23]. Further reports of subdiffusion come from the motion of proteins embedded in the membranes of living cells [24][25][26]. Subdiffusion is also seen in extensive simulations studies, for instance, of lipid bilayer membranes [27][28][29][30] and relative diffusion in proteins [31]. Superdiffusion, due to active motion of molecular motors, was observed in various biological cell types for both introduced and endogenous tracers [16,17,32,33].Most of the anomalous diffusion phenomena mentioned here belong to two main classes of anomalous diffusion: (i) the class of continuous time random walk processes, in which scale-free power-law waiting times in between motion events give rise to the law (3) [12,34], along with a stretched Gaussian displacement probability density G(x, t) [11,12,34] as well as weak ergodicity breaking and ageing [35,36]. We note that similar effects of non-Gaussianity, weak non-ergodicity, and ageing also occur in spatially heterogeneous diffusion processes [37][38][39][40]. (ii) The secon...

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Tracer diffusion in a sea of polymers with binding zones: mobile vs. frozen traps

Cited by 65 publications

References 53 publications

Transport of probe particles in a polymer network: effects of probe size, network rigidity and probe–polymer interaction

Transport of probe particles in a polymer network: effects of probe size, network rigidity and probe–polymer interaction

Test of the diffusing-diffusivity mechanism using near-wall colloidal dynamics

Random diffusivity from stochastic equations: comparison of two models for Brownian yet non-Gaussian diffusion

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