2017/09/16 by Takuma Akimoto, Eiji Yamamoto
Engineering · Physics and Astronomy · #Artificial intelligence #Computer science #Diffusion #Function (biology) #Langevin equation #Mass diffusivity #Nanopore and Nanochannel Transport Studies #Particle (ecology) #Physics #Scale (ratio) #Series (stratigraphy) #Spectroscopy and Quantum Chemical Studies #Statistical physics #Thermal diffusivity #Thermodynamics #Tracking (education) #Trajectory #Transition time #cond-mat.stat-mech #physics.data-an #stochastic dynamics and bifurcation
paper · pdf · doi:10.1103/physreve.96.052138
published as Phys. Rev. E 96, 052138 (2017) · 11 pages, 4 figures
arxiv created 2017/09/16 · openalex publication_date 2017/11/28 · arxiv updated 2017/12/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
In heterogeneous environments, the diffusivity is not constant but changes with time. It is important to detect changes in the diffusivity from single-particle-tracking trajectories in experiments. Here, we devise a novel method for detecting the transition times of the diffusivity from trajectory data. A key idea of this method is the introduction of a characteristic time scale of the diffusive states, which is obtained by a fluctuation analysis of the time-averaged mean square displacements. We test our method in silico by using the Langevin equation with a fluctuating diffusivity. We show that our method can successfully detect the transition times of diffusive states and obtain the diffusion coefficient as a function of time. This method will provide a quantitative description of the fluctuating diffusivity in heterogeneous environments and can be applied to time series with transitions of states.