A
Alexander M. Berezhkovskii
Researcher at Center for Information Technology
Publications - 272
Citations - 6371
Alexander M. Berezhkovskii is an academic researcher from Center for Information Technology. The author has contributed to research in topics: Brownian motion & Diffusion (business). The author has an hindex of 40, co-authored 264 publications receiving 5740 citations. Previous affiliations of Alexander M. Berezhkovskii include National Institutes of Health & Academia Sinica.
Papers
More filters
Journal ArticleDOI
Single-file transport of water molecules through a carbon nanotube
TL;DR: A continuous-time random-walk model is used to describe concerted transport through channels densely filled with molecules in a single-file arrangement, as also found in zeolites, as well as ion channels and aquaporins in biological membranes.
Journal ArticleDOI
One-dimensional reaction coordinates for diffusive activated rate processes in many dimensions
TL;DR: It is shown that a one-dimensional reaction coordinate can be constructed even when the diffusion anisotropy is arbitrary, and the rate constant is identical to that predicted by the multidimensional Kramers-Langer theory.
Journal ArticleDOI
Reactive flux and folding pathways in network models of coarse-grained protein dynamics.
TL;DR: The results for the flux in a network with complex connectivity, obtained using the discrete counterpart of Kramers' theory of activated rate processes, show that the number of reactive transitions is typically much smaller than the total number of transitions that cross a dividing surface at equilibrium.
Journal ArticleDOI
Kinetics of escape through a small hole
TL;DR: In this paper, the authors studied the time dependence of the survival probability of a Brownian particle that escapes from a cavity through a round hole and showed that the rate constant is given by k = 4Da/V, where a and V are the hole radius and the cavity volume and D is the diffusion constant of the particle.
Journal ArticleDOI
Optimizing transport of metabolites through large channels: molecular sieves with and without binding.
TL;DR: Using a diffusion model of molecules moving through a pore, this work rationalizes why biological channels have an affinity for the molecules they have evolved to translocate.