The diffusion of doxorubicin drug molecules in silica nanoslits is non-Gaussian, intermittent and anticorrelated

Fernandez, Amanda Diez; Charchar, Patrick; Cherstvy, Andrey G.; Metzler, Ralf; Finnis, Michael W.

doi:10.1039/d0cp03849k

Cited by 68 publications

(44 citation statements)

References 85 publications

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“…Theoretically, the form and behaviour that distinguish life as an evolving, innovating and adaptive process are bound towards novelty and divergence. Empirically, measurements of living and thinking systems widely fail tests of ergodicity at all levels of organization: motion of protein molecules with a cell [8][9][10][11][12], haemodynamics and intracellular and extracellular transport of complex media in biological systems, such as cytoplasm and nucleoplasm [13,14], intravascular blood flow [15], coupled networks of heterogeneous neurons [16,17] and cognitive processes involved in a variety of perceptuomotor behaviour [18][19][20][21][22]. Description by averages is at odds with our expectations of what life is and as noted above, what is at stake is not a mathematical nicety but rather the issue that applying ergodic models to non-ergodic measurement privileges expedience and convention over access to the cause.…”

Section: Introductionmentioning

confidence: 99%

Ergodic descriptors of non-ergodic stochastic processes

Mangalam

Kelty-Stephen

2022

J. R. Soc. Interface.

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The stochastic processes underlying the growth and stability of biological and psychological systems reveal themselves when far-from-equilibrium. Far-from-equilibrium, non-ergodicity reigns. Non-ergodicity implies that the average outcome for a group/ensemble (i.e. of representative organisms/minds) is not necessarily a reliable estimate of the average outcome for an individual over time. However, the scientific interest in causal inference suggests that we somehow aim at stable estimates of the cause that will generalize to new individuals in the long run. Therefore, the valid analysis must extract an ergodic stationary measure from fluctuating physiological data. So the challenge is to extract statistical estimates that may describe or quantify some of this non-ergodicity (i.e. of the raw measured data) without themselves (i.e. the estimates) being non-ergodic. We show that traditional linear statistics such as the standard deviation, coefficient of variation and root mean square can break ergodicity. Time series of statistics addressing sequential structure and its potential nonlinearity: fractality and multi-fractality, change in a time-independent way and fulfil the ergodic assumption. Complementing traditional linear indices with fractal and multi-fractal indices would empower the study of stochastic far-from-equilibrium biological and psychological dynamics.

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Section: Introductionmentioning

confidence: 99%

Ergodic descriptors of non-ergodic stochastic processes

Mangalam

Kelty-Stephen

2022

J. R. Soc. Interface.

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“…49,50 Furthermore, fractional Brownian walks have been used to model or explain subdiffusion patterns for molecules such as mRNA or other large molecules evolving in crowded environments such as cells 51,52 or with adsorption to surfaces. 53 It remains to be assessed whether such fractional behavior originates from the same physical principles (namely particles transitioning between one region and another) or from other mechanisms.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Intrinsic fractional noise in nanopores: The effect of reservoirs

Marbach

2021

The Journal of Chemical Physics

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Fluctuations affect nanoporous transport in complex and intricate ways, making optimization of signal-to-noise in artificial designs challenging. Here we focus on the simplest nanopore system, where non-interacting particles diffuse through a pore separating reservoirs. We find that the concentration difference between both sides (corresponding to the osmotic pressure drop) exhibits fractional noise in time t with mean square average that grows as t 1/2 . This originates from the diffusive exchange of particles from one region to another. We fully rationalize this effect, with particle simulations and analytic solutions. We further infer the parameters (pore radius, pore thickness) that control this exotic behavior. As a consequence, we show that the number of particles within the pore also exhibits fractional noise. Such fractional noise is responsible for noise spectral density scaling as 1/ f 3/2 with frequency f , and we quantify its amplitude. Our theoretical approach is applicable to more complex nanoporous systems (for example with adsorption within the pore) and drastically simplifies both particle simulations and analytic calculus.

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“…Over the past decade, experiments on the dynamics of passive tracers in a crowded environment as well as computer simulations have been performed by several research groups. [15][16][17][18][19][20][21] The examples include diffusion of lipid vesicles in a solution of entangled filaments 16 , tracer diffusion in an environment consisting of large particles or polymer melts or suspension of polymer chains, 17,22,23 mobile biomolecules on a surface, 19,20 in-plane diffusion of drug molecules in between silica slabs, 24 to name a few. The most notable observation of these studies is the fact that the particle has non-Gaussian position distribution.…”

Section: Introductionmentioning

confidence: 99%