Bidirectional Negative Coupling Induced Dynamical Patterns in a Branched Epoxy-Based Dual-Electrode Microchip Flow Cell

Abstract

In this contribution, we investigated the spatiotemporal pattern formation of oscillatory electrodissolution of nickel in sulfuric acid with two micro-wires placed in two branches of a microfluidic flow cell. In the flow channel, the reference and counter electrodes are placed at the opposite end of the branched flow channel (contralateral placement). A theory based on modeling the potential drop in the flow channel and the interactions between the reactions showed the presence of bidirectional negative electrical coupling between the electrode potential variables. Experiments and numerical simulations with a kinetic model showed that such bidirectional negative coupling could induce complex dynamics. The experiments showed that anti-phase synchronization occurred only when electrodes were placed in the middle of the branched flow channel. When the electrodes were close to beginning or the end of the channels, where the reference and counter electrodes were placed, respectively, no phase locking was observed. The coupling is intensified in magnitude when the electrodes are in the middle of the flow channel, which maximizes the loop current which can flow towards the reference electrode. The nature of the bidirectional coupling is confirmed in independent experiments with synchrony analysis using the Hilbert transform. Additionally, to further verify the nature of the migration current coupling, a data driven approach, inferring connections of networks (ICON), was utilized. Analysis using the ICON technique showed that the synchronization patterns can be described by a phase repulsive bidirectional coupling. The findings indicate that contralateral placement should be used with great care in electroanalytical application with reactions that exhibit negative differential resistance(NDR) region, e.g., due to passivation or adsorption of an inhibitor. Ultimately, revealing connectivity is essential to characterize the dynamical structures and functions of networks, which in turn, leads to the fundamental understanding of many real-world complex systems such as the topology of the physical or functional connections of neurons in the brain.