Role of Mass Transfer Phenomena in Electrochemical Nitrate Reduction: A Case Study Using Ti and Ag-Modified Ti-Hollow Fiber Electrodes

IF 4.3 Q2 ENGINEERING, CHEMICAL
Ainoa Paradelo Rodríguez, Guido Mul* and Bastian T. Mei*, 
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引用次数: 0

Abstract

Decentralized electrochemical reduction of nitrate into ammonium is explored as a viable approach to mitigate nitrate accumulation in groundwater. In this study, tubular porous electrodes made of titanium (termed hollow fiber electrodes or HFEs) were successfully modified with silver (Ag) nanoparticles through electrodeposition. Under galvanostatic control and in acidic electrolyte, Ag deposition on Ti HFE resulted in an increase in the Faradaic efficiency for ammonium formation from low concentrations of nitrate (50 mM), but only under reaction conditions of restricted mass transport. For conditions of favorable transport, facilitated by an inert gas flow (Ar) exiting the pores, a higher nitrate conversion but an increase in hydroxylamine selectivity at the expense of the ammonium selectivity are observed for Ti/Ag hollow fiber electrodes. For Ti/Ag electrodes, it is concluded that ammonium formation is prevented by effective removal of surface intermediates. Remarkably, for unmodified Ti hollow fiber electrodes, the Faradaic efficiency to ammonium is significantly improved when operated at high current densities and in conditions of high mass transport. The selectivity to liquid products even surpasses the selectivity of Ti/Ag electrodes. These findings indicate that nitrate reduction to ammonium at Ti and Ti/Ag hollow fiber electrodes can be achieved at comparable rates but under distinctly different process conditions. In fact, for Ti electrodes, operation at a lower applied potential compared to Ti/Ag electrodes is feasible, ultimately resulting in reduced energy consumption. This study thus highlights the importance of controlling the interfacial electrode environment, particularly when comparing and evaluating the effectiveness of electrode materials in electrochemical nitrate reduction. The study also reveals that transport phenomena affect electrode material-dependent activity–selectivity correlations and must be considered in ongoing material development efforts.

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ACS Engineering Au
ACS Engineering Au 化学工程技术-
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期刊介绍: )ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)
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