Refine
Has Fulltext
- yes (11) (remove)
Document Type
- Postprint (11)
Language
- English (11)
Is part of the Bibliography
- yes (11)
Keywords
- diffusion (6)
- first passage time (2)
- random diffusivity (2)
- Brownian motion (1)
- anomalous diffusion (1)
- approximate methods (1)
- aspect ratio (1)
- cylindrical geometry (1)
- exact results (1)
- extremal values (1)
- fastest first-passage time of N walkers (1)
- first-passage (1)
- first-passage time (1)
- first-passage time distribution (1)
- first-reaction time (1)
- maximum and range (1)
- mean versus most probable reaction times (1)
- mixed boundary conditions (1)
- narrow escape problem (1)
- power spectral analysis (1)
- power spectral density (1)
- power spectrum (1)
- probability density function (1)
- protein search (1)
- reaction cascade (1)
- shell-like geometries (1)
- single trajectories (1)
- single trajectory analysis (1)
- single-trajectory analysis (1)
Anomalous-diffusion, the departure of the spreading dynamics of diffusing particles from the traditional law of Brownian-motion, is a signature feature of a large number of complex soft-matter and biological systems. Anomalous-diffusion emerges due to a variety of physical mechanisms, e.g., trapping interactions or the viscoelasticity of the environment. However, sometimes systems dynamics are erroneously claimed to be anomalous, despite the fact that the true motion is Brownian—or vice versa. This ambiguity in establishing whether the dynamics as normal or anomalous can have far-reaching consequences, e.g., in predictions for reaction- or relaxation-laws. Demonstrating that a system exhibits normal- or anomalous-diffusion is highly desirable for a vast host of applications. Here, we present a criterion for anomalous-diffusion based on the method of power-spectral analysis of single trajectories. The robustness of this criterion is studied for trajectories of fractional-Brownian-motion, a ubiquitous stochastic process for the description of anomalous-diffusion, in the presence of two types of measurement errors. In particular, we find that our criterion is very robust for subdiffusion. Various tests on surrogate data in absence or presence of additional positional noise demonstrate the efficacy of this method in practical contexts. Finally, we provide a proof-of-concept based on diverse experiments exhibiting both normal and anomalous-diffusion.