Anomalous diffusion

Unlike typical diffusion, anomalous diffusion is described by a power law, ${\displaystyle \langle r^{2}(\tau )\rangle =K_{\alpha }\tau ^{\alpha }}$where ${\displaystyle K_{\alpha }}$ is the so-called generalized diffusion coefficient and ${\displaystyle \tau }$ is the elapsed time. The classes of anomalous diffusions are classified as follows:

• α < 1: subdiffusion. This can happen due to crowding or walls. For example, a random walker in a crowded room, or in a maze, is able to move as usual for small random steps, but cannot take large random steps, creating subdiffusion. This appears for example in protein diffusion within cells, or diffusion through porous media. Subdiffusion has been proposed as a measure of macromolecular crowding in the cytoplasm.
• α = 1: Brownian motion.
• ${\displaystyle 1<\alpha <2}$: superdiffusion. Superdiffusion can be the result of active cellular transport processes or due to jumps with a heavy-tail distribution.[13]
• α = 2: ballistic motion. The prototypical example is a particle moving at constant velocity: ${\displaystyle r=v\tau }$.
• ${\displaystyle \alpha >2}$: hyperballistic. It has been observed in optical systems.[14]

In 1926, using weather balloons, Lewis Fry Richardson demonstrated that the atmosphere exhibits super-diffusion.[15] In a bounded system, the mixing length (which determines the scale of dominant mixing motions) is given by the Von Kármán constant according to the equation ${\displaystyle l_{m}={\kappa }z}$, where ${\displaystyle l_{m}}$ is the mixing length, ${\displaystyle {\kappa }}$ is the Von Kármán constant, and ${\displaystyle z}$ is the distance to the nearest boundary.[16] Because the scale of motions in the atmosphere is not limited, as in rivers or the subsurface, a plume continues to experience larger mixing motions as it increases in size, which also increases its diffusivity, resulting in super-diffusion.[17]

The types of anomalous diffusion given above allows one to measure the type, but how does anomalous diffusion arise? There are many possible ways to mathematically define a stochastic process which then has the right kind of power law. Some models are given here.

These are long range correlations between the signals continuous-time random walks (CTRW [18]) and fractional Brownian motion (fBm), and diffusion in disordered media.[19] Currently the most studied types of anomalous diffusion processes are those involving the following

• Generalizations of Brownian motion, such as the fractional Brownian motion and scaled Brownian motion
• Diffusion in fractals and percolation in porous media
• Continuous time random walks

These processes have growing interest in cell biophysics where the mechanism behind anomalous diffusion has direct physiological importance. Of particular interest, works by the groups of Eli Barkai, Maria Garcia Parajo, Joseph Klafter, Diego Krapf, and Ralf Metzler have shown that the motion of molecules in live cells often show a type of anomalous diffusion that breaks the ergodic hypothesis.[20][21][22] This type of motion require novel formalisms for the underlying statistical physics because approaches using microcanonical ensemble and Wiener–Khinchin theorem break down.

• Lévy flight – Random walk with heavy-tailed step lengths
• Random walk – Mathematical formalization of a path that consists of a succession of random steps
• Percolation – Filtration of fluids through porous materials
• Long term correlations
• long range dependencies – Phenomenon in linguistics and data analysis
• Hurst exponent – A measure of the long-range dependence of a time series
• Detrended fluctuation analysis (DFA) – variation of the Hurst Exponent technique, used in the analysis of fractal time seriesPages displaying wikidata descriptions as a fallback
• Fractal – Infinitely detailed mathematical structure

• Weiss, Matthias; Elsner, Markus; Kartberg, Fredrik; Nilsson, Tommy (2004). "Anomalous Subdiffusion Is a Measure for Cytoplasmic Crowding in Living Cells". Biophysical Journal. 87 (5): 3518–3524. Bibcode:2004BpJ....87.3518W. doi:10.1529/biophysj.104.044263. PMC 1304817. PMID 15339818.
• Bouchaud, Jean-Philippe; Georges, Antoine (1990). "Anomalous diffusion in disordered media". Physics Reports. 195 (4–5): 127–293. Bibcode:1990PhR...195..127B. doi:10.1016/0370-1573(90)90099-N.
• von Kameke, A.; et al. (2010). "Propagation of a chemical wave front in a quasi-two-dimensional superdiffusive flow". Phys. Rev. E. 81 (6): 066211. Bibcode:2010PhRvE..81f6211V. doi:10.1103/physreve.81.066211. PMID 20866505. S2CID 23202701.
• Chen, Wen; Sun, HongGuang; Zhang, Xiaodi; Korosak, Dean (2010). "Anomalous diffusion modeling by fractal and fractional derivatives". Computers and Mathematics with Applications. 59 (5): 1754–1758. doi:10.1016/j.camwa.2009.08.020.
• Sun, HongGuang; Meerschaert, Mark M.; Zhang, Yong; Zhu, Jianting; Chen, Wen (2013). "A fractal Richards' equation to capture the non-Boltzmann scaling of water transport in unsaturated media". Advances in Water Resources. 52: 292–295. Bibcode:2013AdWR...52..292S. doi:10.1016/j.advwatres.2012.11.005. PMC 3686513. PMID 23794783.
• Metzler, Ralf; Jeon, Jae-Hyung; Cherstvy, Andrey G.; Barkai, Eli (2014). "Anomalous diffusion models and their properties: non-stationarity, non-ergodicity, and ageing at the centenary of single particle tracking". Phys. Chem. Chem. Phys. 16 (44): 24128–24164. Bibcode:2014PCCP...1624128M. doi:10.1039/c4cp03465a. ISSN 1463-9076. PMID 25297814.
• Krapf, Diego (2015), "Mechanisms Underlying Anomalous Diffusion in the Plasma Membrane", Lipid Domains, Current Topics in Membranes, vol. 75, Elsevier, pp. 167–207, doi:10.1016/bs.ctm.2015.03.002, ISBN 9780128032954, PMID 26015283, S2CID 34712482, retrieved 2018-08-13

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