Solvent Structure and Dynamics Controlled Memristive Ion Transport in Å-Scale Channels

Area of Science:

• Nanoscience
• Materials Science
• Computational Chemistry

Background:

• Memristors are key components for advanced computing architectures like neuromorphic circuits.
• Nanofluidic memristors utilize ionic and solvent systems for history-dependent conductance.
• Existing mechanisms include ion accumulation and surface charge regulation.

Purpose of the Study:

• To demonstrate a novel solvent structure and dynamics mediated memory mechanism in nanofluidic memristors.
• To investigate the relationship between solvent properties and memristive behavior.
• To explore applications in programmable iontronic devices and neuromorphic computing.

Main Methods:

• Utilized molecular dynamics simulations to model ion transport in an Å-scale graphene channel.
• Investigated the behavior of NaCl conduction across methanol, water, and formamide/water mixture solvents.
• Analyzed current-electric field (I-E) hysteresis and its correlation with solvent properties.

Main Results:

• Observed pronounced I-E hysteresis in NaCl conduction, dependent on the solvent used.
• Identified coupled evolution of in-channel ion concentration and hydrogen bond network order as the hysteresis origin.
• Found that hysteresis magnitude correlates with solvent hydrogen bond relaxation time scales (formamide/water > water > methanol).

Conclusions:

• Solvent structure acts as a tunable internal state variable for nanofluidic memristors.
• This mechanism enables programmable iontronic devices with memory capabilities.
• The findings pave the way for novel applications in neuromorphic computing.