Robust Reconstruction of the Void Fraction from Noisy Magnetic Flux Density Using Invertible Neural Networks.

Electrolysis stands as a pivotal method for environmentally sustainable hydrogen production. However, the formation of gas bubbles during the electrolysis process poses significant challenges by impeding the electrochemical reactions, diminishing cell efficiency, and dramatically increasing energy consumption. Furthermore, the inherent difficulty in detecting these bubbles arises from the non-transparency of the wall of electrolysis cells. Additionally, these gas bubbles induce alterations in the conductivity of the electrolyte, leading to corresponding fluctuations in the magnetic flux density outside of the electrolysis cell, which can be measured by externally placed magnetic sensors. By solving the inverse problem of the Biot-Savart Law, we can estimate the conductivity distribution as well as the void fraction within the cell. In this work, we study different approaches to solve the inverse problem including Invertible Neural Networks (INNs) and Tikhonov regularization. Our experiments demonstrate that INNs are much more robust to solving the inverse problem than Tikhonov regularization when the level of noise in the magnetic flux density measurements is not known or changes over space and time.

Hydrogen Bubble Size Distribution on Nanostructured Ni Surfaces: Electrochemically Active Surface Area Versus Wettability.

Emerging manufacturing technologies make it possible to design the morphology of electrocatalysts on the nanoscale in order to improve their efficiency in electrolysis processes. The current work investigates the effects of electrode-attached hydrogen bubbles on the performance of electrodes depending on their surface morphology and wettability. Ni-based electrocatalysts with hydrophilic and hydrophobic nanostructures are manufactured by electrodeposition, and their surface properties are characterized. Despite a considerably larger electrochemically active surface area, electrochemical analysis reveals that the samples with more pronounced hydrophobic properties perform worse at industrially relevant current densities. High-speed imaging shows significantly larger bubble detachment radii with higher hydrophobicity, meaning that the electrode surface area that is blocked by gas is larger than the area gained by nanostructuring. Furthermore, a slight tendency toward bubble size reduction of 7.5% with an increase in the current density is observed in 1 M KOH.