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We investigate the translocation of stiff polymers in the presence of binding particles through a nanopore by two-dimensional Langevin dynamics simulations. We find that the mean translocation time shows a minimum as a function of the binding energy $epsilon$ and the particle concentration $phi$, due to the interplay of the force from binding and the frictional force. Particularly, for the strong binding the translocation proceeds with a decreasing translocation velocity induced by a significant increase of the frictional force. In addition, both $epsilon$ and $phi$ have an notable impact on the distribution of the translocation time. With increasing $epsilon$ and $phi$, it undergoes a transition from an asymmetric and broad distribution under the weak binding to a nearly Gaussian one under the strong binding, and its width becomes gradually narrower.
We investigate the dynamics of DNA translocation through a nanopore using 2D Langevin dynamics simulations, focusing on the dependence of the translocation dynamics on the details of DNA sequences. The DNA molecules studied in this work are built fro
Using Langevin dynamics simulations, we investigate the dynamics of chaperone-assisted translocation of a flexible polymer through a nanopore. We find that increasing the binding energy $epsilon$ between the chaperone and the chain and the chaperone
The translocation dynamics of a polymer chain through a nanopore in the absence of an external driving force is analyzed by means of scaling arguments, fractional calculus, and computer simulations. The problem at hand is mapped on a one dimensional
We investigate the dynamics of DNA translocation through a nanopore driven by an external force using Langevin dynamics simulations in two dimensions (2D) to study how the translocation dynamics depend on the details of the DNA sequences. We consider
We determine the scaling exponents of polymer translocation (PT) through a nanopore by extensive computer simulations of various microscopic models for chain lengths extending up to N=800 in some cases. We focus on the scaling of the average PT time