2018
DOI: 10.1103/physreve.98.022122
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Non-Gaussianity of the van Hove function and dynamic-heterogeneity length scale

Abstract: Non-Gaussian nature of the probability distribution of particles' displacements in the supercooled temperature regime in glass-forming liquids are believed to be one of the major hallmarks of glass transition. It has already been established that this probability distribution, which is also known as the van Hove function, shows universal exponential tail. The origin of such an exponential tail in the distribution function is attributed to the hopping motion of particles observed in the supercooled regime. The … Show more

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Cited by 34 publications
(30 citation statements)
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“…However, we observe distinctly non-Gaussian distributions, a hallmark of dynamic heterogeneity in crowded media. [67][68][69]79,89,90 Further, the temporal persistence of the non-Gaussian dynamics and the lack of a crossover to a Gaussian regime at longer times indicate that the overall relaxation of the dynamical processes dictating transport is longer than our measurement timescale. While this phenomenon is consistent given the measured relaxation times of the cytoskeleton networks, 29,91 it is distinct from many other systems where the non-Gaussian transport is transient, reverting to Gaussian behavior after some critical time.…”
Section: Heterogeneous and Non-ergodic Transportmentioning
confidence: 79%
“…However, we observe distinctly non-Gaussian distributions, a hallmark of dynamic heterogeneity in crowded media. [67][68][69]79,89,90 Further, the temporal persistence of the non-Gaussian dynamics and the lack of a crossover to a Gaussian regime at longer times indicate that the overall relaxation of the dynamical processes dictating transport is longer than our measurement timescale. While this phenomenon is consistent given the measured relaxation times of the cytoskeleton networks, 29,91 it is distinct from many other systems where the non-Gaussian transport is transient, reverting to Gaussian behavior after some critical time.…”
Section: Heterogeneous and Non-ergodic Transportmentioning
confidence: 79%
“…Gs is expanded in Gaussian bases, q(r,M)=(1/Ļ€M)exp(āˆ’r2/M), asGs(r,t)=āˆ«dMP(M,t)q(r,M).Given a noisy estimate of Gs(r,t), P(M,t) is extracted as coefficients of expansion using the RL iterative scheme ( Methods ). The RL algorithm has been extensively used in image processing (32, 33) and also, in monitoring diffusion of liposomes in a nematic solution (34) and particles in simulated supercooled liquids (35ā€“37). In SI Appendix , Figs.…”
Section: Resultsmentioning
confidence: 99%
“…This phenomenon is commonly seen in crowded media in both synthetic and biological systems, and is a hallmark of dynamic heterogeneity [32][33][34][35][36]45,46 . However, in these systems, non-Gaussian transport is often transient and reverts to Gaussian at long enough times [47][48][49] . Conversely, we note the absence of a crossover to Gaussian transport in our systems at long lag times (100 s), suggesting that the dynamical processes governing transport in these networks have relaxation times longer than our measurement time scale.…”
mentioning
confidence: 99%