Abstract
We consider the dynamics of pore-driven polymer translocation through a nanopore to a two-dimensional semi-infinite space when the chain is initially confined and equilibrated in a narrow channel. To this end, we use Langevin dynamics (LD) simulations and iso-flux tension propagation (IFTP) theory to characterize local and global dynamics of the translocating chain. The dynamics of the process can be described by the IFTP theory in very good agreement with the LD simulations for all values of confinement in the channel. The theory reveals that for channels with a size comparable to or less than the end-to-end distance of the unconfined chain, in which the blob theory works, the scaling form of the translocation time depends on both the chain contour length and the channel width. Conversely, for a very narrow channel, the translocation time only depends on the chain contour length and is similar to that of a rod due to the absence of spatial chain fluctuations.
| Original language | English |
|---|---|
| Article number | 244903 |
| Pages (from-to) | 1-13 |
| Number of pages | 13 |
| Journal | Journal of Chemical Physics |
| Volume | 162 |
| Issue number | 24 |
| DOIs | |
| Publication status | Published - 28 Jun 2025 |
| MoE publication type | A1 Journal article-refereed |
Funding
S.E. and J.S. acknowledge the Iran National Science Foundation: “This work is based upon research funded by the Iran National Science Foundation (INSF) under Project No. 4026895.” R.M. acknowledges the German Science Foundation (DFG, Grant Nos. ME 1535/16-1 and ME 1535/13-1) for the support. T.A.-N. has been supported in part by the Academy of Finland Grant No. 353298 under the European Union—NextGenerationEU instrument. R.M. acknowledges funding from NSF-BMBF CRCNS through Grant No. 2112862/STAXS.
Fingerprint
Dive into the research topics of 'Driven polymer translocation through a nanopore from a confining channel'. Together they form a unique fingerprint.Projects
- 1 Finished
-
GreenDigi/Ala-Nissilä: Experimental and Artificial-Intellience-Based Modeling of Optimal Effiency for Renewable Long-Term Heat Storages
Ala-Nissilä, T. (Principal investigator), Gyursánszky, C. (Project Member), George, A. (Project Member), Alipour, S. (Project Member), Wang, Y. (Project Member), Muhli, H. (Project Member), Vahid, H. (Project Member), Ghasemitarei, M. (Project Member), Front, A. (Project Member), Hashemi Petrudi, A. (Project Member), Tasanen, T. (Project Member), Khakpour, R. (Project Member), Chang, X. (Project Member) & Karjula, M. (Project Member)
EU The Recovery and Resilience Facility (RRF)
01/01/2023 → 31/12/2025
Project: RCF Academy Project targeted call
Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver