Recent advances in nanofluidics have allowed the exploration of ion transport down to molecular-scale confinement, yet artificial porins are still far from reaching the advanced functionalities of biological ion machinery. Achieving single ion transport that is tunable by an external gate—the ionic analogue of electronic Coulomb blockade—would open new avenues in this quest. However, an understanding of ionic Coulomb blockade beyond the electronic analogy is still lacking. Here, we show that the many-body dynamics of ions in a charged nanochannel result in quantized and strongly nonlinear ionic transport, in full agreement with molecular simulations. We find that ionic Coulomb blockade occurs when, upon sufficient confinement, oppositely charged ions form ‘Bjerrum pairs’, and the conduction proceeds through a mechanism reminiscent of Onsager’s Wien effect. Our findings open the way to novel nanofluidic functionalities, such as an ion pump based on ionic Coulomb blockade, inspired by its electronic counterpart.
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The data that support the plots within this paper and other findings of this study are available from the corresponding authors upon reasonable request.
The Brownian dynamics code used within this study is available from the corresponding authors upon reasonable request.
Journal peer review information: Nature Nanotechnology thanks Peter (V. E.) McClintock, Aleksandra Radenovic and the other anonymous reviewer(s) for their contribution to the peer review of this work.
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The authors thank V. Démery, R. Vuilleumier, B. Rotenberg, D. Dean, R. Netz, A. Poggioli, T. Mouterde, L. Jubin and H. Yoshida for useful discussions. S.M. and L.B. acknowledge support from ANR Neptune. L.B. acknowledges support from ERC, project Shadoks, and European Union’s H2020 Framework Programme/FET NanoPhlow. This work was granted access to the HPC resources of MesoPSL financed by the Region Ile de France and the project Equip@Meso (reference ANR-10-EQPX-29-01) of the programme Investissements d’Avenir supervised by the Agence Nationale pour la Recherche, as well as to the HPC resources of CINES under allocation 2018-A0040710395 made by GENCI.