High levels of water stress and increased demand for potable water generated via desalination pose significant challenges for sustainable waste brine management in arid regions. Electrochemical techniques, like brine electrolysis, offer an approach for treating brine, preventing environmentally harmful disposal, and facilitating the recycling of valuable ions found in brine. As the large concentration of ions can precipitate and degrade conventional electrolyzer components, membraneless electrolyzers, which lack membranes, can be an alternative for direct brine electrolysis. The absence of membranes enables operation in the presence of impurities and a wide range of pH environments. However, membraneless electrolyzers suffer from a trade-off between current density and current utilization that stems from undesired back-reactions that arise from the crossover of gaseous and aqueous products between the anode and cathode. In this dissertation work, a combination of in situ high-speed video, colorimetric pH imaging, modeling, and electroanalytical methods were used to evaluate how the performance of a porous flow-through cathode is affected by operating current density, electrolyte flow rate, and choice of catalyst placement on a porous support. It was found that catalyst placement is a key knob to control the location of product generation and thereby minimize product crossover and maximize pH differential. Placing the catalyst on the outer surface of the cathode resulted in an average increase of 51% in current utilization, a metric for measuring crossover, compared to the opposite configuration. This finding is explained by the ability of the porous electrode support to serve as a barrier to suppress crossover for the outward-facing catalyst configuration. In addition, the outward-facing catalyst configuration leads to more stable operation while incurring minor increases (90-170 mV) in overpotentials. For both catalyst configurations, it was also shown that the Damköhler number (𝐷𝑎) is a practical descriptor for predicting operating conditions that maximize the concentration of OH⁻ in the cathode effluent stream. Furthermore, this dissertation evaluated the performance of a platinized cathode within a membraneless electrolyzer in the presence of Mg²⁺ impurities. In a 3-hour stability test at 50 mA cm⁻² during brine electrolysis, electrolytes with Mg²+ concentration below 5 mM showed a negligible influence on cathode performance. Electrolytes with Mg²⁺ concentration below 1.2 mM at similar operating conditions exhibited improved cathode performance compared to Mg-free brine. All learnings during this study were captured in a mathematical model that predicts the tolerance threshold at which the cathode would cease to operate due to accumulations of Mg(OH)₂ deposits at different current densities and superficial velocities. Overall, these studies demonstrated the potential of membraneless electrolyzers as an emerging technology for treating brine and converting it into high-value products. Finally, applying an oxide overlayer to planar electrodes has been demonstrated to improve their stability, activity, and/or selectivity. This is relevant for direct brine electrolysis, as brine contains many impurities that can compromise the integrity of electrodes and promote undesirable reactions, generating toxic products like chlorine gas. However, given that high-surface electrodes are required for industrial applications, it is necessary to develop a method to encapsulate high-surface-area electrodes. Applying nanoscopic oxide encapsulation layers to high-surface-area electrodes such as nanoparticle-supported porous electrodes is not an easy task. This dissertation work demonstrated that the recently developed condensed layer deposition (CLD) method can be used for depositing nanoscopic (sub-10 nm thick) titanium oxide (TiO₂) overlayers onto high surface area platinized carbon foam electrodes. Characterization of the overlayers by transmission electron microscopy (TEM) and electron energy loss spectroscopy (EELS) showed they are amorphous, while X-ray photoelectron microscopy confirmed that they exhibit TiO₂ stoichiometry. Electrodes were also characterized by hydrogen underpotential deposition (Hupd) and carbon monoxide (CO) stripping, demonstrating that the Pt electrocatalysts remain electrochemically active after encapsulation. Furthermore, copper underpotential deposition (Cuupd) measurements for bare Pt and TiO₂-encapsulated Pt electrocatalysts revealed that the TiO₂ overlayer effectively prevented Cu₂+ from reaching the buried, allowing this method to determine the coverage of the TiOx coating. In summary, this portion of the dissertation demonstrated that CLD is a promising method for applying nanoscopic overlayers on high-surface electrodes.

Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Among various diagnostics, optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these applications. This thesis reports the first application of high-speed imaging videography and deep learning as real-time diagnostics of rotating MHD modes in a tokamak device. The developed system uses a convolutional neural network (CNN) to predict the amplitudes of the ?=1 sine and cosine mode components using solely optical measurements acquired from one or more cameras. Using the newly assembled high-speed camera diagnostics on the High Beta Tokamak – Extended Pulse (HBT-EP) device, an experimental dataset consisting of camera frame images and magnetic-based mode measurements was assembled and used to develop the mode-tracking CNN model. The optimized models outperformed other tested conventional algorithms given identical image inputs. A prototype controller based on a field-programmable gate array (FPGA) hardware was developed to perform real-time mode tracking using the high-speed camera diagnostic with the mode-tracking CNN model. In this system, a trained model was directly implemented in the firmware of an FPGA device onboard the frame grabber hardware of the camera’s data readout system. Adjusting the model size and its implementation-related parameters allowed achieving an optimal trade-off between a model’s prediction accuracy, its FPGA resource utilization and inference speed. Through fine-tuning these parameters, the final implementation satisfied all of the design constraints, achieving a total trigger-to-output latency of 17.6 ?s and a throughput of up to 120 kfps. These results are on-par with the existing GPU-based control system using magnetic sensor diagnostic, indicating that the camera-based controller will be capable to perform active feedback control of MHD modes on HBT-EP.