Effect of Carboxylic Acid-Doped Carbon Nanotube Catalyst on the Performance of Aqueous Organic Redox Flow Battery Using the Modified Alloxazine and Ferrocyanide Redox Couple

Electrode Treatments for Redox Flow Batteries: Translating Our Understanding from Vanadium to Aqueous-Organic

Redox flow batteries (RFBs) are a promising technology for long-duration energy storage; but they suffer from inefficiencies in part due to the overvoltages at the electrode surface. In this work, more than 70 electrode treatments are reviewed that are previously shown to reduce the overvoltages and improve performance for vanadium RFBs (VRFBs), the most commercialized RFB technology. However, identifying treatments that improve performance the most and whether they are industrially implementable is challenging. This study attempts to address this challenge by comparing treatments under similar operating conditions and accounting for the treatment process complexity. The different treatments are compared at laboratory and industrial scale based on criteria for VRFB performance, treatment stability, economic feasibility, and ease of industrial implementation. Thermal, plasma, electrochemical oxidation, CO2 treatments, as well as Bi, Ag, and Cu catalysts loaded on electrodes are identified as the most promising for adoption in large scale VRFBs. The similarity in electrode treatments for aqueous-organic RFBs (AORFBs) and VRFBs is also identified. The need of standardization in RFBs testing along with fundamental studies to understand charge transfer reactions in redox active species used in RFBs moving forward is emphasized.

A quantitative evaluation of computational methods to accelerate the study of alloxazine-derived electroactive compounds for energy storage

Alloxazines are a promising class of organic electroactive compounds for application in aqueous redox flow batteries (ARFBs), whose redox properties need to be tuned further for higher performance. High-throughput computational screening (HTCS) enables rational and time-efficient study of energy storage compounds. We compared the performance of computational chemistry methods, including the force field based molecular mechanics, semi-empirical quantum mechanics, density functional tight binding, and density functional theory, on the basis of their accuracy and computational cost in predicting the redox potentials of alloxazines. Various energy-based descriptors, including the redox reaction energies and the frontier orbital energies of the reactant and product molecules, were considered. We found that the lowest unoccupied molecular orbital (LUMO) energy of the reactant molecules is the best performing chemical descriptor for alloxazines, which is in contrast to other classes of energy storage compounds, such as quinones that we reported earlier. Notably, we present a flexible in silico approach to accelerate both the singly and the HTCS studies, therewithal considering the level of accuracy versus measured electrochemical data, which is readily applicable for the discovery of alloxazine-derived organic compounds for energy storage in ARFBs.

Anthraquinone-2,6-disulfamidic acid: an anolyte with low decomposition rates at elevated temperatures

A new anthraquinone based anolyte material for redox flow batteries revealed an extraordinarily high stability at elevated electrolyte temperatures.

Comparison of computational chemistry methods for the discovery of quinone-based electroactive compounds for energy storage

High-throughput computational screening (HTCS) is a powerful approach for the rational and time-efficient design of electroactive compounds. The effectiveness of HTCS is dependent on accuracy and speed at which the performance descriptors can be estimated for possibly millions of candidate compounds. Here, a systematic evaluation of computational methods, including force field (FF), semi-empirical quantum mechanics (SEQM), density functional based tight binding (DFTB), and density functional theory (DFT), is performed on the basis of their accuracy in predicting the redox potentials of redox-active organic compounds. Geometry optimizations at low-level theories followed by single point energy (SPE) DFT calculations that include an implicit solvation model are found to offer equipollent accuracy as the high-level DFT methods, albeit at significantly lower computational costs. Effects of implicit solvation on molecular geometries and SPEs, and their overall effects on the prediction accuracy of redox potentials are analyzed in view of computational cost versus prediction accuracy, which outlines the best choice of methods corresponding to a desired level of accuracy. The modular computational approach is applicable for accelerating the virtual studies on functional quinones and the respective discovery of candidate compounds for energy storage.