Innovative Fluorescence Microscopy and Cloud-Based Algorithm for Real-Time Analysis of Electroconvection Dynamics

In high-current electrochemical cells, electroconvection significantly influences the morphology of electrodeposited metals, leading to dendrite formation and potential battery failures. Despite extensive theoretical and experimental efforts, the intricate structure and dynamics of electroconvection remain elusive due to the lack of high-resolution observation tools and robust data processing algorithms.<br/><br/>To address this gap, we developed an advanced optical electrochemical cell compatible with in situ imaging using a super-resolution fluorescence microscope. This setup allows us to capture real-time motions of electrolytes, providing unprecedented high temporal and spatial resolution views of electroconvection flows. Complementing our visualization studies, we designed a cloud-based analysis algorithm that integrates a high-resolution Particle Image Velocimetry (PIV) algorithm with a machine learning model. This combination enables the generation of detailed velocity distribution data over the entire optical field of view at nanoscale or microscale resolutions.<br/><br/>The resulting velocity maps from our optical electrochemical cell allow for a comprehensive quantitative analysis of the initiation and evolution of hierarchical microstructures within electroconvection under a unidirectional electric field. Using these advanced tools, we investigated the impact of polymer viscoelasticity on electroconvection and electrodeposition. Introducing ultrahigh molar mass polyethylene oxide into the electrolytes altered the fluid state to viscoelastic, modifying the electroconvection's time and voltage dependence and resulting in smoother electrodeposition on the metal anode due to the unique rheological properties of the polymer. Notably, the behavior of long polymer chains in the electrolyte presents a promising method to inhibit dendrite growth. These experimental findings were further analyzed using direct numerical simulations for both Newtonian and viscoelastic fluid models, revealing a strong correlation between the quantitative analysis of experimental data and simulation predictions. This integrated approach offers a powerful framework for understanding and controlling electroconvection dynamics, enhancing battery performance and safety.