Assessing the Versatility and Robustness of Pore Network Modeling to Simulate Redox Flow Battery Electrode Performance

Porous electrodes are core components that determine the performance of redox flow batteries. Thus, optimizing their microstructure is a powerful approach to reduce system costs. Here we present a pore network modeling framework that is microstructure and chemistry agnostic, iteratively solves transport equations in both half-cells, and utilizes a network-in-series approach to simulate the local transport phenomena within porous electrodes at a low computational cost. In this study, we critically assess the versatility and robustness of pore network models to enable the modeling of different electrode geometries and redox chemistries. To do so, the proposed model was validated with two commonly used carbon fiber-based electrodes (a paper and a cloth), by extracting topologically equivalent networks from X-ray tomograms, and evaluated for two model redox chemistries (an aqueous iron-based and a non-aqueous TEMPO-based electrolyte). We find that the modeling framework successfully captures the experimental performance of the non-aqueous electrolyte but is less accurate for the aqueous electrolyte which was attributed to incomplete wetting of the electrode surface in the conducted experiments. Furthermore, the validation reveals that care must be taken when extracting networks from the tomogram of the woven cloth electrode, which features a multiscale microstructure with threaded fiber bundles. Employing this pore network model, we elucidate structure-performance relationships by leveraging the performance profiles and the simulated local distributions of physical properties and finally, we deploy simulations to identify efficient operation envelopes

Bottom-up design of porous electrodes by combining a genetic algorithm and a pore network model

The microstructure of porous electrodes determines multiple performance-defining properties, such as the available reactive surface area, mass transfer rates, and hydraulic resistance. Thus, optimizing the electrode architecture is a powerful approach to enhance the performance and cost-competitiveness of electrochemical technologies. To expand our current arsenal of electrode materials, we need to build predictive frameworks that can screen a large geometrical design space while being physically representative. Here, we present a novel approach for the optimization of porous electrode microstructures from the bottom-up that couples a genetic algorithm with a previously validated electrochemical pore network model. In this first demonstration, we focus on optimizing redox flow battery electrodes. The genetic algorithm manipulates the pore and throat size distributions of an artificially generated microstructure with fixed pore positions by selecting the best-performing networks, based on the hydraulic and electrochemical performance computed by the model. For the studied VO2+/VO2+ electrolyte, we find an increase in the fitness of 75 % compared to the initial configuration by minimizing the pumping power and maximizing the electrochemical power of the system. The algorithm generates structures with improved fluid distribution through the formation of a bimodal pore size distribution containing preferential longitudinal flow pathways, resulting in a decrease of 73 % for the required pumping power. Furthermore, the optimization yielded an 47 % increase in surface area resulting in an electrochemical performance improvement of 42 %. Our results show the potential of using genetic algorithms combined with pore network models to optimize porous electrode microstructures for a wide range of electrolyte composition and operation conditions.</p

Engineering Lung-Inspired Flow Field Geometries for Electrochemical Flow Cells with Stereolithography 3D Printing

Electrochemical flow reactors are increasingly relevant platforms in emerging sustainable energy conversion and storage technologies. As a prominent example, redox flow batteries, a well-suited technology for large energy storage if the costs can be significantly reduced, leverage electrochemical reactors as power converting units. Within the reactor, the flow field geometry determines the electrolyte pumping power required, mass transport rates, and overall cell performance. However, current designs are inspired by fuel cell technologies but have not been engineered for redox flow battery applications, where liquid-phase electrochemistry is sustained. Here, we leverage stereolithography 3D printing to manufacture lung-inspired flow field geometries and compare their performance to conventional flow field designs. A versatile two-step process based on stereolithography 3D printing followed by a coating procedure to form a conductive structure is developed to manufacture lung-inspired flow field geometries. We employ a suite of fluid dynamics, electrochemical diagnostics, and finite element simulations to correlate the flow field geometry with performance in symmetric flow cells. We find that the lung-inspired structural pattern homogenizes the reactant distribution throughout the porous electrode and improves the electrolyte accessibility to the electrode reaction area. In addition, the results reveal that these novel flow field geometries can outperform conventional interdigitated flow field designs, as these patterns exhibit a more favorable balance of electrical and pumping power, achieving superior current densities at lower pressure loss. Although at its nascent stage, additive manufacturing offers a versatile design space for manufacturing engineered flow field geometries for advanced flow reactors in emerging electrochemical energy storage technologies.</p

Understanding the Role of Electrode Thickness on Redox Flow Cell Performance

The electrode thickness is a critical design parameter to engineer high performance redox flow cells by impacting the available surface area for reactions, current and potential distributions, and required pumping power. To date, redox flow cell assemblies employ repurposed off-the-shelf fibrous electrodes which feature a broad range of thicknesses. However, comprehensive guidelines to select the optimal electrode thickness for a given reactor architecture remain elusive. Here, we investigate the effect of the electrode thickness in the range of 200–1100 μm on the cell performance by stacking electrode layers in four different flow cell configurations – Freudenberg paper and ELAT cloth electrodes combined with flow-through and interdigitated flow fields. We employ a suite of polarization, electrochemical impedance spectroscopy and pressure drop measurements together with pore network modeling simulations to correlate the electrode thickness for various reactor designs to the electrochemical and hydraulic performance. We find that thicker electrodes (420 μm paper electrodes and 812 μm cloth electrodes) are beneficial in combination with flow-through flow fields, whereas when using interdigitated flow fields, thinner electrodes (210 μm paper electrodes and 406 μm cloth electrodes) result in a better current density and pressure drop trade-off. We hope our findings will aid researchers and technology practitioners in designing their electrochemical flow cells under convective operation.</p

Short-Term Efficacy of Rofecoxib and Diclofenac in Acute Shoulder Pain: A Placebo-Controlled Randomized Trial

OBJECTIVES: To evaluate the short-term symptomatic efficacy of rofecoxib and diclofenac versus placebo in acute episodes of shoulder pain. DESIGN: Randomized controlled trial of 7 days. SETTING: Rheumatologists and/or general practitioners totaling 47. PARTICIPANTS: Acute shoulder pain. INTERVENTIONS: Rofecoxib 50 mg once daily, diclofenac 50 mg three times daily, and placebo. OUTCOME MEASURES: Pain, functional impairment, patient's global assessment of his/her disease activity, and local steroid injection requirement for persistent pain. The primary variable was the Kaplan-Meier estimates of the percentage of patients at day 7 fulfilling the definition of success (improvement in pain intensity and a low pain level sustained to the end of the 7 days of the study; log-rank test). RESULTS: There was no difference in the baseline characteristics between the three groups (rofecoxib n = 88, placebo n = 94, and diclofenac n = 89). At day 7, the Kaplan-Meier estimates of successful patients was higher in the treatment groups than in the placebo (54%, 56%, and 38% in the diclofenac, rofecoxib, and placebo groups respectively, p = 0.0070 and p = 0.0239 for placebo versus rofecoxib and diclofenac, respectively). During the 7 days of the study, there was a statistically significant difference between placebo and both active arms (rofecoxib and diclofenac) in all the evaluated outcome measures A local steroid injection had to be performed in 33 (35%) and 19 (22%) patients in the placebo and rofecoxib group respectively. Number needed to treat to avoid such rescue therapy was 7 patients (95% confidence interval 5–15). CONCLUSION: This study highlights the methodological aspects of clinical trials, e.g., eligibility criteria and outcome measures, in acute painful conditions. The data also establish that diclofenac and rofecoxib are effective therapies for the management of acute painful shoulder and that they reduce the requirement for local steroid injection

Hyperdominance in Amazonian Forest Carbon Cycling

While Amazonian forests are extraordinarily diverse, the abundance of trees is skewed strongly towards relatively few ‘hyperdominant’ species. In addition to their diversity, Amazonian trees are a key component of the global carbon cycle, assimilating and storing more carbon than any other ecosystem on Earth. Here we ask, using a unique data set of 530 forest plots, if the functions of storing and producing woody carbon are concentrated in a small number of tree species, whether the most abundant species also dominate carbon cycling, and whether dominant species are characterized by specific functional traits. We find that dominance of forest function is even more concentrated in a few species than is dominance of tree abundance, with only ≈1% of Amazon tree species responsible for 50% of carbon storage and productivity. Although those species that contribute most to biomass and productivity are often abundant, species maximum size is also influential, while the identity and ranking of dominant species varies by function and by region

Long-term thermal sensitivity of Earth’s tropical forests

The sensitivity of tropical forest carbon to climate is a key uncertainty in predicting global climate change. Although short-term drying and warming are known to affect forests, it is unknown if such effects translate into long-term responses. Here, we analyze 590 permanent plots measured across the tropics to derive the equilibrium climate controls on forest carbon. Maximum temperature is the most important predictor of aboveground biomass (−9.1 megagrams of carbon per hectare per degree Celsius), primarily by reducing woody productivity, and has a greater impact per °C in the hottest forests (>32.2°C). Our results nevertheless reveal greater thermal resilience than observations of short-term variation imply. To realize the long-term climate adaptation potential of tropical forests requires both protecting them and stabilizing Earth’s climate

Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes.

Although several lung cancer susceptibility loci have been identified, much of the heritability for lung cancer remains unexplained. Here 14,803 cases and 12,262 controls of European descent were genotyped on the OncoArray and combined with existing data for an aggregated genome-wide association study (GWAS) analysis of lung cancer in 29,266 cases and 56,450 controls. We identified 18 susceptibility loci achieving genome-wide significance, including 10 new loci. The new loci highlight the striking heterogeneity in genetic susceptibility across the histological subtypes of lung cancer, with four loci associated with lung cancer overall and six loci associated with lung adenocarcinoma. Gene expression quantitative trait locus (eQTL) analysis in 1,425 normal lung tissue samples highlights RNASET2, SECISBP2L and NRG1 as candidate genes. Other loci include genes such as a cholinergic nicotinic receptor, CHRNA2, and the telomere-related genes OFBC1 and RTEL1. Further exploration of the target genes will continue to provide new insights into the etiology of lung cancer