Generating Predictive Chemical Kinetic Models for Quaternary Ammonium Monomers Chemical Degradation in Anion Exchange Membrane

Abstract

It is widely accepted that the high cost of Proton Exchange Membrane (PEM) fuel cells (FC) is a major inhibitor of the transition to extensive commercialization of hydrogen-fueled FCs. One known alternative to this technology is Anion Exchange Membrane (AEM), greatly reducing the need for precious metals, e.g., platinum, and allowing a wider choice of lower cost polymeric materials1. Although research in the field of AEM has greatly evolved recently, AEM-FCs still lack sufficient durability and cell longevity. The current major challenge revolves around the chemical and structural stability of polymers constructing the membrane, which undergo different degradation reactions under cell operating conditions. A widely investigated AEM monomer is Benzyl trimethylammonium (BTMA), a quaternary ammonium (QA) cation, in which the positively charged nitrogen atom enables the selective transfer of hydroxide ions through the membrane2. BTMA degradation occurs in several reaction paths, the main one involving a nucleophilic attack of the hydroxide at the benzylic carbon atom, resulting in complete displacement of the functional group (Fig. 1a). Recently, it has also been shown that low hydration levels of hydroxide, evidently more relevant to the FC’s environment, have an immense impact on the reduced stability of BTMA3,4. Despite the elaborate research done on BTMA degradation, as well as on other QA monomers, complete understanding of the chemical mechanisms affecting the stability of AEM-FCs is still lacking. Modeling these mechanisms can assist in facilitating practical implementation of this promising technology, discovering novel chemically-stable ionomers, while saving time and resources in trial-and-error efforts.The goal of this research is to develop an automated workflow for generating and refining predictive chemical kinetic models describing chemical degradation of AEM monomers, by combining several computational tools, and tailoring them to suit our specific needs. The first is the Reaction Mechanism Generator (RMG) software, which automatically generates chemical kinetic models by utilizing a basic comprehension of chemical reactions5. RMG derives its data from “libraries” of kinetic and thermodynamic parameters based on the literature, and from estimations schemes based on group or decision trees. Since RMG currently does not support ion chemistry, it is required to add this feature to the software, along with solvation corrections, while extending its estimation schemes. To achieve this, accurate kinetic and thermodynamic parameters of the species and reactions of interest are necessary.The present work focuses on describing BTMA degradation. Preliminary results for the temperature-dependent rate coefficient and thermodynamic properties were obtained using the Automated Rate Calculator (ARC) software6. This tool facilitates electronic structure calculations relevant for chemical kinetic modeling, by automatically spawning, tracking, and analyzing relevant computations. We computed the BTMA + OH– reaction rate coefficient at a low hydration level (λ=1) at the ωB97xD/Def2-TZVP level of theory, and compared it to the experimental results obtained at three different temperatures (Fig. 1b). The calculations were performed using the SMD solvation model with DMSO as the solvent, in accordance with the experimental conditions. Our preliminary results show some agreement with experimental measurements, and we expect this agreement to improve by calculating single-point energies using the more accurate coupled-cluster (CC) theory.The methods developed to generate a chemical kinetic model for BTMA, can then be applied for other potential QA monomers. Furthermore, an additional software called The Tandem Tool (T3), can be used to perform model refinement, through a process of iterations between RMG and ARC, creating the desired automated model generation workflow. This process is designed to obtain quantum mechanics-based calculated parameters for significant species and reactions in the model, chosen using sensitivity analysis. Altogether, this work can create a powerful tool, providing insights on known AEM ionomers, as well as the flexibility to investigate additional polymeric materials, with minimal experimental resources.