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

Immune clearance of attenuated rabies virus results in neuronal survival with altered gene expression.

Rabies virus (RABV) is a highly neurotropic pathogen that typically leads to mortality of infected animals and humans. The precise etiology of rabies neuropathogenesis is unknown, though it is hypothesized to be due either to neuronal death or dysfunction. Analysis of human brains post-mortem reveals surprisingly little tissue damage and neuropathology considering the dramatic clinical symptomology, supporting the neuronal dysfunction model. However, whether or not neurons survive infection and clearance and, provided they do, whether they are functionally restored to their pre-infection phenotype has not been determined in vivo for RABV, or any neurotropic virus. This is due, in part, to the absence of a permanent mark on once-infected cells that allow their identification long after viral clearance. Our approach to study the survival and integrity of RABV-infected neurons was to infect Cre reporter mice with recombinant RABV expressing Cre-recombinase (RABV-Cre) to switch neurons constitutively expressing tdTomato (red) to expression of a Cre-inducible EGFP (green), permanently marking neurons that had been infected in vivo. We used fluorescence microscopy and quantitative real-time PCR to measure the survival of neurons after viral clearance; we found that the vast majority of RABV-infected neurons survive both infection and immunological clearance. We were able to isolate these previously infected neurons by flow cytometry and assay their gene expression profiles compared to uninfected cells. We observed transcriptional changes in these cured neurons, predictive of decreased neurite growth and dysregulated microtubule dynamics. This suggests that viral clearance, though allowing for survival of neurons, may not restore them to their pre-infection functionality. Our data provide a proof-of-principle foundation to re-evaluate the etiology of human central nervous system diseases of unknown etiology: viruses may trigger permanent neuronal damage that can persist or progress in the absence of sustained viral antigen

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

SBMLsqueezer 2: context-sensitive creation of kinetic equations in biochemical networks

BACKGROUND: The size and complexity of published biochemical network reconstructions are steadily increasing, expanding the potential scale of derived computational models. However, the construction of large biochemical network models is a laborious and error-prone task. Automated methods have simplified the network reconstruction process, but building kinetic models for these systems is still a manually intensive task. Appropriate kinetic equations, based upon reaction rate laws, must be constructed and parameterized for each reaction. The complex test-and-evaluation cycles that can be involved during kinetic model construction would thus benefit from automated methods for rate law assignment. RESULTS: We present a high-throughput algorithm to automatically suggest and create suitable rate laws based upon reaction type according to several criteria. The criteria for choices made by the algorithm can be influenced in order to assign the desired type of rate law to each reaction. This algorithm is implemented in the software package SBMLsqueezer 2. In addition, this program contains an integrated connection to the kinetics database SABIO-RK to obtain experimentally-derived rate laws when desired. CONCLUSIONS: The described approach fills a heretofore absent niche in workflows for large-scale biochemical kinetic model construction. In several applications the algorithm has already been demonstrated to be useful and scalable. SBMLsqueezer is platform independent and can be used as a stand-alone package, as an integrated plugin, or through a web interface, enabling flexible solutions and use-case scenarios. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-015-0212-9) contains supplementary material, which is available to authorized users