Temperature Induced Dimensional Tuning and Anomalous Deformation of Micro/Nanopores

Nanofluidic memristor based on the elastic deformation of nanopores with nanoparticle adsorption

The memristor is the building block of neuromorphic computing. We report a new type of nanofluidic memristor based on the principle of elastic strain on polymer nanopores. With nanoparticles absorbed at the wall of a single conical polymer nanopore, we find a pinched hysteresis of the current within a scanning frequency range of 0.01-0.1 Hz, switching to a diode below 0.01 Hz and a resistor above 0.1 Hz. We attribute the current hysteresis to the elastic strain at the tip side of the nanopore, caused by electrical force on the particles adsorbed at the inner wall surface. Our simulation and analytical equations match well with experimental results, with a phase diagram for predicting the system transitions. We demonstrate the plasticity of our nanofluidic memristor to be similar to a biological synapse. Our findings pave a new way for ionic neuromorphic computing using nanofluidic memristors.

Surface-charge governed ionic blockade in angstrom-scale latent-track channels

When channels are scaled down to the size of hydrated ions, Coulomb interactions are enhanced in confinement, resulting in new phenomena. Herein, we found blockade of ionic transport in latent-track angstrom-scale channels governed by surface charge, fundamentally different from Coulomb blockade or Wien effects. The channels are non-conductive at low voltage, blocked by cations bound at the surface in confinement; however, they change to conductive with increasing voltage due to the release of bound ions. The increase in surface charge density gradually causes the conduction to be ohmic. Using Kramers' escape framework, we rationalized an analytical equation to describe the experimental results, uncovering new fundamental insights into ion transport in the smallest channels.