Nanofluidic logic with mechano-ionic memristive switches.

Neuromorphic systems are typically based on nanoscale electronic devices, but nature relies on ions for energy-efficient information processing. Nanofluidic memristive devices could thus potentially be used to construct electrolytic computers that mimic the brain down to its basic principles of operation. Here we report a nanofluidic device that is designed for circuit-scale in-memory processing. The device, which is fabricated using a scalable process, combines single-digit nanometric confinement and large entrance asymmetry and operates on the second timescale with a conductance ratio in the range of 9 to 60. In operando optical microscopy shows that the memory capabilities are due to the reversible formation of liquid blisters that modulate the conductance of the device. We use these mechano-ionic memristive switches to assemble logic circuits composed of two interactive devices and an ohmic resistor.

Disentangling 1/f noise from confined ion dynamics.

Ion transport through biological and solid-state nanochannels is known to be a highly noisy process. The power spectrum of current fluctuations is empirically known to scale like the inverse of frequency, following the long-standing yet poorly understood Hooge's law. Here, we report measurements of current fluctuations across nanometer-scale two-dimensional channels with different surface properties. The structure of fluctuations is found to depend on the channel's material. While in pristine channels current fluctuations scale like 1/f1+a with a = 0-0.5, the noise power spectrum of activated graphite channels displays different regimes depending on frequency. Based on these observations, we develop a theoretical formalism directly linking ion dynamics and current fluctuations. We predict that the noise power spectrum takes the form 1/f × Schannel(f), where 1/f fluctuations emerge in fluidic reservoirs on both sides of the channel and Schannel describes fluctuations inside it. Deviations to Hooge's law thus allow direct access to the ion transport dynamics of the channel - explaining the entire phenomenology observed in experiments on 2D nanochannels. Our results demonstrate how current fluctuations can be used to characterize nanoscale ion dynamics.

Enhanced nanofluidic transport in activated carbon nanoconduits.

Carbon has emerged as a unique material in nanofluidics, with reports of fast water transport, molecular ion separation and efficient osmotic energy conversion. Many of these phenomena still await proper rationalization due to the lack of fundamental understanding of nanoscale ionic transport, which can only be achieved in controlled environments. Here we develop the fabrication of 'activated' two-dimensional carbon nanochannels. Compared with nanoconduits with 'pristine' graphite walls, this enables the investigation of nanoscale ionic transport in great detail. We show that activated carbon nanochannels outperform pristine channels by orders of magnitude in terms of surface electrification, ionic conductance, streaming current and (epi-)osmotic currents. A detailed theoretical framework enables us to attribute the enhanced ionic transport across activated carbon nanochannels to an optimal combination of high surface charge and low friction. Furthermore, this demonstrates the unique potential of activated carbon for energy harvesting from salinity gradients with single-pore power density across activated carbon nanochannels, reaching hundreds of kilowatts per square metre, surpassing alternative nanomaterials.

Growth Rates and Spontaneous Navigation of Condensate Droplets Through Randomly Structured Textures.

Dropwise condensation is a phenomenon of common occurrence in everyday life, the understanding and controlling of which is of great interest to applications ranging from technology to nature. Scalable superhydrophobic textures on metals are of direct relevance in improving phase change heat transport in realistic industrial applications. Here we reveal important facets of individual droplet growth rate and droplet departure during dropwise condensation on randomly structured hierarchical superhydrophobic aluminum textures, that is, surfaces with a microstructure consisting of irregular re-entrant microcavities and an overlaying nanostructure. We demonstrate that precoalescence droplet growth on such a surface can span a broad range of rates even when the condensation conditions are held constant. The fastest growth rates are observed to be more than 4 times faster as compared to the slowest growing droplets. We show that this variation in droplet growth on the hierarchical texture is primarily controlled by droplet growth dynamics on the nanostructure overlaying the microstructure and is caused by condensation-induced localized wetting nonuniformity on the nanostructure. We also show that the droplets nucleating and growing within the microcavities are able to spontaneously navigate the irregular microcavity geometry, climb the microtexture, and finally depart from the surface by coalescence-induced jumping. This self-navigation is realized by a synergistic combination of self-orienting Laplace pressure gradients induced within the droplet as it dislodges itself and moves through the texture, as well as multidroplet coalescence.