Nanofluidic systems show great promise for applications in energy conversion, where their performance can be enhanced by nanoscale liquid-solid slip. However, efficiency is also controlled by surface charge, which is known to reduce slip. Combining molecular dynamics simulations and analytical developments, we show the dramatic impact of surface charge distribution on the slip-charge coupling. Homogeneously charged graphene exhibits a very favorable slip-charge relation (rationalized with a new theoretical model correcting some weaknesses of the existing ones), leading to giant electrokinetic energy conversion. In contrast, slip is strongly affected on heterogeneously charged surfaces, due to the viscous drag induced by counterions trapped on the surface. In that case slip should depend on the detailed physical chemistry of the interface controlling the fraction of bound ions. Our numerical results and theoretical models provide new fundamental insight into the molecular mechanisms of liquid-solid slip, and practical guidelines for searching new functional interfaces with optimal energy conversion properties, e.g., for blue energy or waste heat harvesting.

A nanofluidic-based memristor device is obtained by using a track-etched conical nanochannel with mixed ionic liquid/water solutions.

We investigated the efficiency of electrical power generation in single track-etched nanopores by measuring the streaming currents and conductance. Experimental results indicate that both the efficiency and output power depend on the electrolyte concentration and the dimension of the pore. The highest efficiency of 5% was obtained in nanopores with small radii of 31 nm. The surface property of the track-etched nanopores was found very important to the kinetic-electric behaviors in the pores, especially when the electrolyte concentration was low.

In this contribution, we present for the first time the experimental results of energy conversion from the streaming current when a polymer is added to the working solution. We added polyacrylic acid (PAA) in concentrations of 200 ppm to 4000 ppm to a KCl solution. By introducing PAA, the input power, which is the product of volumetric flow rate and the applied pressure, reduced rapidly as compared to the case of using only a normal viscous electrolyte KCl solution. The output power at the same time remained largely constant, whereby an increase of the streaming current and a decrease of the streaming potential simultaneously occurred. These combined factors led to the massive increase of the energy conversion efficiency. Particularly, the results showed that when PAA was in a 0.01 mM KCl solution, the energy conversion efficiency of the system was enhanced by a factor of 447 (±2%), as compared to the case of the solution containing only 0.01 mM KCl. An enhancement factor of 249 (±4%) was also observed when PAA was added to the higher ionic strength background solution, 1 mM KCl. This finding can have practical use in microchannel-array energy conversion systems. When, instead of the negatively charged PAA, a non-ionic polymer polyethylene oxide (PEO) was added to the solution, no efficiency increase was observed, probably due to polymer wall 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.