A Tubular Electrochemical Reactor for Slurry Electrodes

The research on electrochemical reactors is mostly limited to planarly designed modules. In this study, we compare a tubular and a planar electrochemical reactor for the utilization of the slurry electrodes. Cylindrical formed geometries demonstrate a higher surface-to-volume ratio, which may be favorable in terms of current density and volumetric power density. A tubular shaped electrochemical reactor is designed with conductive static mixers to promote the slurry particle mixing, and the vanadium redox flow battery is selected as a showcase application. The new tubular design presents similar cell resistances to the previously designed planar battery and shows increased discharge polarization behavior up to 100 mA cm−2. The volumetric power density reaches up to 30 mW cm−3, which is two times higher than that of the planar one. The battery performance is further investigated and 85 % coulombic, 70 % voltage and 60 % energy efficiency is found at 15 mA cm−2 with 15 wt.% slurry content. © 2020 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA

Tubular hollow fibre electrodes for CO2 reduction made from copper aluminum alloy with drastically increased intrinsic porosity

Electrochemical reduction of CO2 to higher-order hydrocarbon products offers a significant contribution to the challenge of a circular economy. In the pursuit of better copper metal catalyst, it was early on realized that increasing productivity of copper catalysts systems is reliant on high surface area per volume. Tubular gas diffusion electrodes offer such properties. In this work, we present a methodology to fabricate tubular hollow fibre copper electrodes with drastically increased intrinsic porosity. Our described method utilizes a standard dealloying process of copper aluminium particles to induce an intra-particle nanoporosity. The specific surface area increases from 0.126 m2 g−1 before dealloying to 6.194 m2 g−1 after dealloying. In comparison to conventional planar copper electrodes and literature data from conventional copper hollow fibres, the intra-particle porosity leads to a drastically increase in electrochemical activity. Electrochemical measurements reveal increased current densities at low over-potentials in comparison to conventional copper electrodes under identical experimental conditions emphasizing the significant impact of the porosity on the electrode performance. The presented method can be easily transferred to other alloy particles, highlighting its versatility for electrode fabrication. © 2019 The Author(s

Multi-scale membrane process optimization with high-fidelity ion transport models through machine learning

Innovative membrane technologies optimally integrated into large separation process plants are essential for economical water treatment and disposal. However, the mass transport through membranes is commonly described by nonlinear differential-algebraic mechanistic models at the nano-scale, while the process and its economics range up to large-scale. Thus, the optimal design of membranes in process plants requires decision making across multiple scales, which is not tractable using standard tools. In this work, we embed artificial neural networks~(ANNs) as surrogate models in the deterministic global optimization to bridge the gap of scales. This methodology allows for deterministic global optimization of membrane processes with accurate transport models -- avoiding the utilization of inaccurate approximations through heuristics or short-cut models. The ANNs are trained based on data generated by a one-dimensional extended Nernst-Planck ion transport model and extended to a more accurate two-dimensional distribution of the membrane module, that captures the filtration-related decreasing retention of salt. We simultaneously design the membrane and plant layout yielding optimal membrane module synthesis properties along with the optimal plant design for multiple objectives, feed concentrations, filtration stages, and salt mixtures. The developed process models and the optimization solver are available open-source, enabling computational resource-efficient multi-scale optimization in membrane science

3D-printed rotating spinnerets create membranes with a twist

Round hollow fiber membranes are long-established in applications such as gas separation, ultrafiltration and blood dialysis. Yet, it is well known that geometrical topologies can introduce secondary ow patterns counteracting mass transport limitations, stemming from diffusion resistances and fouling. We present a new systematic method- ology to fabricate novel membrane architectures. We use the freedom of design by 3D-printing spinnerets, having multiple bore channels of any geometry. First, such spinnerets are stationary to fabricate straight bore channels inside a monolithic membrane. Second, in an even more complex design, a new mechanical system enables rotating the spinneret. Such rotating multibore spinnerets enable (A) the preparation of twisted channels inside a porous monolithic membrane as well as (B) a helical twist of the outside geometry. The spun material systems comprise classical polymer solutions as well as metal-polymer slurries resulting in solid porous metallic monolithic membrane after thermal post-processing. It is known that twisted spiral-type bore channel geometries are potentially superior over straight channels with respect to mass and heat polarization phenomena, however their fabrication was cumber- some in the past. Now, the described methodology enables membrane fabrication to tailor the membrane geometry to the needs of the membrane process

Data-Driven development of layer-by-layer nanofiltration membranes and processes

Synthetic membranes provide an essential technological basis for modern drinking water treatment and disposal. A quest for ion selectivity becomes increasingly important, as opposed to the conventional task of high ion retention. This quest requires a new versatile membrane fabrication approach and screening pathway. By now, exploring the design space of synthetic membranes is generally based on an evidence-based experimental effort relying on a large number of trails through an educated guess and experimental design context. This thesis offers a novel data-driven methodology using artificial neural networks (ANNs) for the development of layer-by-layer nanofiltration membranes in contrast to established screening pathways. Using statistically substantiated predictions through machine learning techniques provides the possibility to skip a large number of these experimental steps. Furthermore, machine learning enables a well-founded physical analysis of the performance spectrum, creating an interface between the membrane in the laboratory and its model-based optimization. Starting from an extensive data-set of layer-by-layer nanofiltration membranes, this thesis presents for the first time that an ANN can predict and improve ion retention of salts and water flux values based on given membrane synthesis protocols. An innovative deterministic global multi-objective optimization identifies the upper bound (Pareto front) of the delicate retention and permeability trade-off. Next, an extension by a mechanistic ion transport model offers hybrid modeling systems that are embedded in a state-of-the-art membrane process model to simultaneously design the membrane synthesis protocols along with the process layout yielding favorable results immediately. Ultimately, hybrid modeling bridges the gap between knowledge creation at the small-scale (local transport models) and decision making at large-scale (process optimization). These steps pioneer solving complex multi-scale process optimizations in membrane science and enable decision making across multiple scales with accurate transport models. The presented data-driven development methodology unleashes material scientists and engineers to overcome limitations in membrane material development - enabling fast, non-intuitive solutions that may remain hidden when only considering conventional screening pathways

On charge percolation in slurry electrodes used in vanadium redox flow batteries

In vanadium redox flow battery systems porous carbon felts are commonly employed as electrodes inside the flow channel. Recently, slurry electrodes (or flow suspension electrodes) were introduced as a potentially viable electrode system. Such electrode systems are little understood so far. Mass, momentum and charge transfer phenomena co-occur, interactions with each other are nearly impossible to capture experimentally. We present a novel discrete model of the particulate phase combining theories from fluid dynamics, colloidal physics, and electrochemistry with a coupled CFD-DEM approach. The methodology allows to visualize local phenomena occurring during the charging of the battery and to compute the net current of the slurry electrode system. We demonstrate that an increasing particle volume fraction enables the formation of conducting networks in the flow electrode until a threshold is reached. Our study concludes, that the assumption of all particles participating in the charge transfer as assumed in pure CFD investigations is not necessarily valid. Keywords: Vanadium redox flow battery, Flow suspension electrode, Slurry electrodes, CFD-DEM simulation, Particle charge transfer phenomen

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In vanadium redox flow battery systems porous carbon felts are commonly employed as electrodes inside the flow channel. Recently, slurry electrodes (or flow suspension electrodes) were introduced as a potentially viable electrode system. Such electrode systems are little understood so far. Mass, momentum and charge transfer phenomena co-occur, interactions with each other are nearly impossible to capture experimentally. We present a novel discrete model of the particulate phase combining theories from fluid dynamics, colloidal physics, and electrochemistry with a coupled CFD-DEM approach. The methodology allows to visualize local phenomena occurring during the charging of the battery and to compute the net current of the slurry electrode system. We demonstrate that an increasing particle volume fraction enables the formation of conducting networks in the flow electrode until a threshold is reached. Our study concludes, that the assumption of all particles participating in the charge transfer as assumed in pure CFD investigations is not necessarily valid