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Inductive measurement techniques are extremely sensitive and thus offer a high potential for monitoring industrial processes that include electrically conductive fluids e.g., casting processes in metallurgy, numerous flows in the chemical industry and in process engineering, in particular the characterisation of multiphase flows, e.g., in the detection of gas bubbles in conductive fluids. The term inductive measurements comprises several methods that are all based on the interplay of electric currents and magnetic fields. The contactless inductive flow tomography (CIFT) is developed at HZDR over the last 20 years to visualize three-dimensional velocity fields in liquid metals. The method is based on the principle of induction by motion: very weak induced magnetic fields rise from the fluid motion under the influence of one or several primary excitation magnetic field(s) and can be precisely measured from the outside by magnetic field sensors. As the induced magnetic fields carry a “fingerprint” of the causative flow field, the solution of the associated linear inverse problem is a reconstruction of the velocity field. Due to its non-contact nature, CIFT enables to reconstruct the time-resolved global velocity vector field even in chemically aggressive and/or very hot liquid metals that are inaccessible by other techniques as ultrasound-Doppler velocimetry or particle-image velocimetry. We will outline the general principle of the method and present results of various measuring tasks from different processes, e.g., continuous casting of steel. Another novel concept of a measurement technology to localize and determine the size of gas bubbles in fluids is currently under development at HZDR. The use of current tomography is intended to contribute to a further understanding of the dynamics of efficiency-reducing gas bubbles in electrolysers. A simplified proof-of-concept (POC) model is used to numerically simulate the electric current flow through materials with signif

We present an inductive measurement technique for the identification of gas bubbles in liquid metals e.g., liquid sodium as is used as coolant in fast fission reactors. Gas bubbles in the coolant are an indication of damage to the tubing of the steam generator unit and can lead to severe accidents [Cavaro M., Payan C. and J.P. Jeannot. 3rd International Conference on Advancements in Nuclear Instrumentation, Measurement Methods and their Applications (ANIMMA), Marseille, France, 2013.]. We propose a contactless inductive bubble detection (CIBD) method. In a laboratory table-top experimental set-up we utilize the method to detect rising Argon bubbles in the liquid metal alloy Gallium-Indium-Tin (GaInSn) that is used as a non-dangerous model fluid. CIBD consists of an excitation coil generating an alternating magnetic field that induces eddy currents in the fluid. Non-conducting gas bubbles in the conducting fluid act as obstacles to these eddy currents and lead to slight changes of the current distribution, that can be detected outside of the fluid. A combination of two pickup coils positioned on top of each other which are wound in opposite direction and connected in series gives a so-called planar gradiometer that is only sensitive to asymmetric magnetic field distributions. Gundrum et al. [Sensors 16, 63. 2016.] used one planar gradiometer positioned opposite to the excitation coil to detect the rising velocity of Argon bubbles in GaInSn as well as liquid Sodium. We extend this approach by the use of several planar gradiometers at different sides of the vessel and with different orientations to determine the size and position of the bubbles as was not possible with only one detection coil. First experimental results will be presented. The laboratory experiments are accompanied by COMSOL simulations for different bubble radii, positions and excitation frequencies of the excitation coil that reflect in the penetration depth of the magnetic field. The CIBD method offe

We present an inductive measurement technique for the identification of gas bubbles in liquid metals e.g., liquid sodium as is used as coolant in fast fission reactors. Gas bubbles in the coolant are an indication of damage to the tubing of the steam generator unit and can lead to severe accidents by chemical reactions of the liquid sodium. The contactless inductive bubble detection (CIBD) consists of an excitation coil generating an alternating magnetic field that induces eddy currents in the fluid. Electrically non-conducting gas bubbles in the otherwise conducting fluid act as obstacles to these eddy currents and the slight change of current distribution can be detected outside of the fluid by magnetic field sensors. One sensor is sufficient to verify the existence of gas bubbles robustly and it is possible to estimate the bubble velocity without any calibration. We outline how the use of multiple sensors enables to extract additional quantities of gas bubbles as size and position.

We present an inductive measurement technique for the identification of gas bubbles in liquid metals e.g., liquid sodium as is used as coolant in fast fission reactors. Gas bubbles in the coolant are an indication of damage to the tubing of the steam generator unit and can lead to severe accidents by chemical reactions of the liquid sodium. The contactless inductive bubble detection (CIBD) consists of an excitation coil generating an alternating magnetic field that induces eddy currents in the fluid. Electrically non-conducting gas bubbles in the otherwise conducting fluid act as obstacles to these eddy currents and the slight change of current distribution can be detected outside of the fluid by magnetic field sensors. One sensor is sufficient to verify the existence of gas bubbles robustly and it is possible to estimate the bubble velocity without any calibration. We outline how the use of multiple sensors enables to extract additional quantities of gas bubbles as size and position.

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 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. Fortunately, these gas bubbles induce alterations in the cell’s conductivity, leading to corresponding fluctuations in the surrounding magnetic flux density. In this context, we can leverage external magnetic sensors to measure the magnetic flux density fluctuations induced by gas bubbles. Next, by solving the inverse problem of the Biot-Savart Law, we can estimate the conductivity, bubble size, and location within the cell. Nevertheless, reconstructing a high-resolution conductivity map from limited induced magnetic flux density measurements poses a formidable challenge as an ill-posed inverse problem. To overcome this challenge, we employ Invertible Neural Networks (INNs) to reconstruct the conductivity field. The inherent property of INNs, characterized by a bijective mapping between the input and output space, makes them exceptionally well-suited for resolving ill-posed inverse problems. We conducted extensive qualitative and quantitative evaluations to compare the performance of INNs with traditional approaches such as Tikhonov regularization. Our experiments demonstrate that, particularly in the presence of noise in the magnetic sensor data, our INN-based approach outperforms Tikhonov regularization in accurately reconstructing bubble distributions and conductivity fields. We hope that, given the efficacy of INNs shown in this work, they will become an indispensable deep-learning based approach for addressing inverse problems not only in Process Tomography but across various other domains.

Objectives Membraneless alkaline electrolyzer (MAEL) allow higher current densities compared to conventional designs [1] and provide very good access to the electrodes, making them ideal for research to better understand bubble formation and detachment. Methods In the present study both elements of a MAEL, porous electrodes and cell geometry, are optimized individually. For the geometrical optimization, CFD and current simulations were performed to obtain an optimized cell geometry that ensures constant conditions for the water splitting reaction over the entire electrode area. A three-electrode cell was used to perform parametric studies of HER on porous electrodes [2] and functionalized surfaces [3]. Therefore, Particle Image Velocimetry and Shadowgraphy were used to systematically study the influence of the electrode surface and the electrolyte flow as driving force for an effective H2 and O2 separation in a MAEL. Results & Conclusions It is shown that below a critical Recrit the evolving bubbles are stuck on the porous electrodes and lead to a blockage of the electrochemical active sites and to an increase of the cell potential. At the optimal flow rate to current density ratio high gas purity and overall efficiency were observed. Importantly, this study presented an experimental framework that guides the electrode and cell design of MAELs and analyzes their performance limits. Literature [1] D.V. Esposito, Joule. 2017, 1, 651-658. [2] H. Rox et al., Int. J. Hydrog. Energy. 2023, 48, 2892-2905. [3] L. Krause et al., ACS Applied Materials & Interfaces. 2023, 15, 14, 18290-18299.

Electrolysis is crucial for eco-friendly hydrogen production, but gas bubbles generated during the process hinder reactions, reduce cell efficiency, and increase energy consumption. Additionally, these gas bubbles cause changes in the conductivity inside the cell, resulting in corresponding variations in the induced magnetic field around the cell. Therefore, measuring these gas bubble-induced magnetic field fluctuations using external magnetic sensors and solving the inverse problem of Biot-Savart’s Law allows for estimating the conductivity in the cell and, thus, bubble size and location. However, determining high-resolution conductivity maps from only a few induced magnetic field measurements is an ill-posed inverse problem. To overcome this, we exploit Invertible Neural Networks (INNs) to reconstruct the conductivity field. Our qualitative results and quantitative evaluation using random error diffusion show that INN achieves far superior performance compared to Tikhonov regularization.

A novel concept of a measurement technology for the localization and determination of the size of gas bubbles is presented, which is intended to contribute to a further understanding of the dynamics of efficiency-reducing gas bubbles in electrolyzers. A simplified proof-of-concept (POC) model is used to numerically simulate the electric current flow through materials with significant differences in electrical conductivity. Through an automated approach, an extensive data set of electric current density and conductivity distributions is generated, complemented with determined magnetic flux densities in the surroundings of the POC cell at virtual sensor positions. The generated data set serves as testing data for various reconstruction approaches. Based on the measurable magnetic flux density, solving Biot-Savart’s law inversely is demonstrated and discussed with a model-based solution of an optimization problem, of which the gas bubble locations are derived.

A novel concept of a measurement technology for the localization and determination of the size of gas bubbles is presented, which is intended to contribute to a further understanding of the dynamics of efficiency-reducing gas bubbles in electrolyzers. A simplified proof-of-concept (POC) model is used to numerically simulate the electric current flow through materials with significant differences in electrical conductivity. Through an automated approach, an extensive data set of electric current density and conductivity distributions is generated, complemented with determined magnetic flux densities in the surroundings of the POC cell at virtual sensor positions. The generated data set serves as testing data for various reconstruction approaches. Based on the measurable magnetic flux density, solving Biot-Savart’s law inversely is demonstrated and discussed with a model-based solution of an optimization problem, of which the gas bubble locations are derived.

Electrolysis is crucial for eco-friendly hydrogen production, but gas bubbles generated during the process hinder reactions, reduce cell efficiency, and increase energy consumption. Additionally, these gas bubbles cause changes in the conductivity inside the cell, resulting in corresponding variations in the induced magnetic field around the cell. Therefore, measuring these gas bubble-induced magnetic field fluctuations using external magnetic sensors and solving the inverse problem of Biot-Savart’s Law allows for estimating the conductivity in the cell and, thus, bubble size and location. However, determining high-resolution conductivity maps from only a few induced magnetic field measurements is an ill-posed inverse problem. To overcome this, we exploit Invertible Neural Networks (INNs) to reconstruct the conductivity field. Our qualitative results and quantitative evaluation using random error diffusion show that INN achieves far superior performance compared to Tikhonov regularization.

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 towards bubble size reduction of 7.5% with an increase in the current density is observed in 1 M KOH.

Catalysts can be successfully prepared by a simple electrochemical process. Their surface composition distinguishes catalytic activity toward hydrogen and oxygen evolution reactions. In this work, uniform Co-Ni cones were synthesized using the onestep method from an electrolyte containing a crystal modifier. Electrodeposited layers were oxidized and/or reduced in the furnace at 100°C. Freshly electrodeposited coating was stored in air atmosphere for seven days. This results in an oxide layer forming on the surface of the catalyst. Changes in the surface composition, confirmed by the XPS method, strongly influenced the wettability, catalytic performance, and size of evolved hydrogen bubbles. The conical Co-Ni surface oxidized in a controlled way possesses the best catalytic activity towards hydrogen and oxygen evolution. Conversely, the spontaneously formed oxide layer decreases the catalytic performance in mentioned reactions compared with the fresh sample. The proper storage of synthesized samples is essential due to their desired catalytic applications. Proposed controlled oxidation can be an accessible way to increase nanomaterials catalytic performance.

Noble metals in form of bulk materials are rarely used as catalysts in industrial applications due to their price and limited accessibility. However, the surface of commonly-used materials can be modified with an exceptionally low amount of platinoids to enhance their electrochemical activity. In this work, the surface of the free-standing nanoconical Ni structures was modified with a thin metallic rhodium layer with a spontaneous galvanic displacement process. It emerged that Rh-decorated Ni nanoconical electrodes exhibited higher catalytic activity in a model hydrogen evolution reaction in an alkaline environment in comparison to the unmodified electrode. Furthermore, the proposed synthesis protocol is high-speed and straightforward, making it promising in application on a semi and industrial scale. Linear Sweep Voltammetry measurements were used to test the catalytic activity in 1 M NaOH electrolyte. The nanoconical structures were highly-hydrophobic, what has been observed by dedicated camera-based equipment. In addition, the electrochemically-active surface area (ECSA) of tested electrodes were estimated and confirmed in atomic force microscope AFM measurements. The durability of coatings was tasted in 1 M NaOH for 14 days.