Active Ionic Fluxes Induce Symmetry Breaking in Charge-Patterned Nanochannels

Abstract

Biological systems rely on autonomous modes of charge transport to transmit signals, whereas conventional artificial systems typically depend on external fields, such as voltage or pressure gradients, limiting their adaptability. Here we investigate nanochannels in which an electrolyte is confined by symmetric boundary configurations combining patterned surface charge with active ionic fluxes. We show that the interplay between diffusive, electrostatic and hydrodynamic interactions in such active-charged nanosystems can trigger a symmetry breaking as the activity increases. Our results suggest that active-charged nanochannels could amplify directed flows up to the order of meters per second, opening pathways toward adaptable iontronic devices and neuromorphic architectures.

Figures (3)

• Figure 1: All figures correspond to a salt concentration of $\rho_0=100$ mM. a) Snapshot of the DPDS simulations showing the geometry of the charged-pattern active nanochannel. The channel is centered at x=0, and extends between x=-L/2 and x=L/2. In the z direction it extends between z=0 and z=w. The surface charge of the patches corresponds to $\sigma_\pm=\pm0.6 e_0/$nm$^2$, and the active flux corresponds to $j_+=0.4$ mM$\cdot$m/s. b) Snapshot of the DPDS simulation showing the ion distribution to spontaneously displace in one direction. The surface charge of the patches corresponds to $\sigma_\pm=\pm0.6 e_0/$nm$^2$, and the active flux corresponds to $j_+=0.98$ mM$\cdot$m/s. c) Velocity as a function of time for different active ionic fluxes. Surface charge corresponds to $\sigma_\pm=\pm0.6 e_0/$nm$^2$. d) Distribution of charge along the channel (x-direction), showing that the emergence of a directed flow is related to the its asymmetric charge distribution. e) Diagram exploring which combinations of surface charge and active fluxes can lead to a net solvent flow.
• Figure 2: a) LB diagram of symmetry breaking in LB simulations. Grey regions correspond to simulations where is the cationic fluxes lead to a net charged system. The salt concentration corresponds to $\rho_0=100$ mM. b) Charge density profile comparison between the DPDS and LB simulations. Both cases correspond to a surface charge $\sigma=0.6$ e$_0$/nm$^2$.
• Figure 3: a) Slip velocity $u_s$ extracted from the LB and DPD simulations. When the symmetry is broken, the averaged slip velocity is positive (or negative). To approximate these slip velocities in the analytical model, we use $u_s(x)=u_0[1-\cos(\text{k}x)]$. Surface charge $\sigma_0$ corresponds to 0.56 $e_0/$nm$^2$. b) Comparison between the DPDS simulations for $\sigma_0$=0.56 $e_0/$nm$^2$ Eq. \ref{['eq:anl']}. For the analytical model we take $u_0\sim{0.1}$ m/s as a typical value at the onset of the net flow regimes according to both LB and DPDS simulations. We use typical values from the simulations, $j_0=1$ mM$\cdot$m/s, $\sigma_0=0.37$$e_0$/nm$^2$, $\rho_0=100$ mM. c) Pe$_u$ number suggested by the theoretical model as a function of the dimensionless flux. Left pannel shows the LB simulations, which exhibits net flow when Pe$_u>$0.5. Tight pannel shows the DPD simulations, which exhibits net flow when Pe$_u>$0.02.