| dc.description.abstract | Memristors have been propelled into the scientific consciousness in recent decades. Memristors are resistors with memory, a history-dependent electrical device predicted by Chua in the 1970s as the missing fourth passive circuit element on the basis of symmetry. The first memristive devices were discovered in 2012 in semiconductor solid-state devices. Recently, our research group predicted the memristive capabilities of microfluidic systems containing an aqueous electrolyte in a cone-shaped channel [1], such iontronic devices can perform computing tasks [2]. The theoretical model for these systems is developed from the Poisson-Nernst-Planck-Stokes equation, where we extend these studies to include bipolar cylindrical and conical channels, rather than unipolar channels which has been at the forefront of current research. This model is used to understand the effect of changing geometries for an applied steady-state voltage. The analytical model is successful at providing accurate predictions of Ohmic and Diodic electric current behaviour that we can attribute to salt accumulation or depletion in the channel due to an applied static electric potential drop over the channel. Moreover, we consider the volumetric electro-osmotic fluid flow upon an electric driving force, a phenomenon that has no alternative in solid-state systems. To test the accuracy of the model we compare it to finite element calculations, revealing that the electric current behaviour in both geometries has been successfully predicted, with the volumetric flow matching well with finite element calculations only for the cylindrical channel. Further research into the effects of geometry in ionic channels is encouraged to understand memristive behaviour.
[1] T. M. Kamsma, W. Q. Boon, T. ter Rele, C. Spitoni, and R. van Roij. Iontronic neuromorphic signaling
with conical microfluidic memristors. Physical Review Letters, 130, (2023).
[2] T. M. Kamsma, J. Kim, K. Kim, W. Q. Boon, C. Spitoni, J. Park, and R. van Roij. Brain-inspired
computing with fluidic iontronic nanochannels, (2023). | |
