Aqueous redox flow battery systems that use a zinc negative electrode have a relatively high energy density. However, high current densities can lead to zinc dendrite growth and electrode polarization, which limit the battery's high power density and cyclability. In this study, a perforated copper foil with a high electrical conductivity was used on the negative side, combined with an electrocatalyst on the positive electrode in a zinc iodide flow battery. A significant improvement in the energy efficiency (ca. 10% vs using graphite felt on both sides) and cycling stability at a high current density of 40 mA cm −2 was observed. A long cycling stability with a high areal capacity of 222 mA h cm −2 is obtained in this study, which is the highest reported areal capacity for zinc−iodide aqueous flow batteries operating at high current density, in comparison to previous studies. Additionally, the use of a perforated copper foil anode in combination with a novel flow mode was discovered to achieve consistent cycling at exceedingly high current densities of >100 mA cm −2 . In situ and ex situ characterization techniques, including in situ atomic force microscopy coupled with in situ optical microscopy and X-ray diffraction, are applied to clarify the relationship between zinc deposition morphology on the perforated copper foil and battery performance in two different flow field conditions. With a portion of the flow going through the perforations, a significantly more uniform and compact zinc deposition was observed compared to the case where all of the flow passed over the surface of the electrode. Results from modeling and simulation support the conclusion that the flow of a fraction of electrolyte through the electrode enhances mass transport, enabling a more compact deposit.
Aqueous, two-phase systems (ATPSs) may form upon mixing two solutions of independently water-soluble compounds. Many separation, purification, and extraction processes rely on ATPSs. Predicting the miscibility of solutions can accelerate and reduce the cost of the discovery of new ATPSs for these applications. Whereas previous machine learning approaches to ATPS prediction used physicochemical properties of each solute as a descriptor, in this work, we show how we can impute missing miscibility outcomes directly from an incomplete collection of pairwise miscibility experiments. We use graph-regularized logistic matrix factorization to learn a latent vector of each solution from (i) the observed entries in the pairwise miscibility matrix and (ii) a graph (nodes: solutes, edges: shared relationships) indicating the general category of the solute (i.e., polymer, surfactant, salt, protein). Using an experimental dataset of the pairwise miscibility of 68 solutions from Peacock et al. [ACS Appl. Mater. Interfaces 2021, 13, 9], we show that graph-regularized logistic matrix factorization more accurately predicts missing (im)miscibility outcomes of pairs of solutions than ordinary logistic matrix factorization and random forest classifiers using physicochemical features of the compounds.
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