Computational Approaches to Alkaline Anion-Exchange Membranes for Fuel Cell Applications

Abstract

Anion-exchange membranes (AEMs) are key components in relatively novel technologies such as alkaline exchange-based membrane fuel cells and AEM-based water electrolyzers. The application of AEMs in these processes is made possible in an alkaline environment, where hydroxide ions (OH^-) play the role of charge carriers in the presence of an electrocatalyst and an AEM acts as an electrical insulator blocking the transport of electrons, thereby preventing circuit break. Thus, a good AEM would allow the selective transport of OH^- while preventing fuel (e.g., hydrogen, alcohol) crossover. These issues are the subjects of in-depth studies of AEMs-both experimental and theoretical studies-with particular emphasis on the ionic conductivity, ion exchange capacity, fuel crossover, durability, stability, and cell performance properties of AEMs. In this review article, the computational approaches used to investigate the properties of AEMs are discussed. The different modeling length scales are microscopic, mesoscopic, and macroscopic. The microscopic scale entails the ab initio and quantum mechanical modeling of alkaline AEMs. The mesoscopic scale entails using molecular dynamics simulations and other techniques to assess the alkaline electrolyte diffusion in AEMs, OH^- transport and chemical degradation in AEMs, ion exchange capacity of an AEM, as well as morphological microstructures. This review shows that computational approaches can be used to investigate different properties of AEMs and sheds light on how the different computational domains can be deployed to investigate AEM properties.

Keywords: alkaline anion-exchange membranes; computational approaches; macroscopic; mesoscopic; microscopic.

Figure 1

Schematic comparison between an AAEMMFC and a PEMFC.

Figure 2

Number of AAEM publications per year (2013–2022, to date) with and without computational studies included.

Figure 3

Computational modeling scales.

Figure 4

Schematic of degradation reaction process of a nucleophilic attack. Reprinted with permission from [24]. Copyright 2020 Elsevier.

Figure 5

Molecular structures of imidazolium cations with different substitution positions. Reprinted with permission from [27]. Copyright 2014 American Chemical Society.

Figure 6

Isosurface and LUMO energy of the synthesized imidazolium cations. The color of atoms: red (O), blue (N), white (H), and grey (C). The black arrows indicate the nucleophilic attacking direction of OH⁻. Reprinted with permission from [27]. Copyright 2014 American Chemical Society.

Figure 7

Molecules considered for 3-butyl-1-methylimidium (BMI) and 3-butyl-1-methylbenzimidium (BMBI). Reprinted with permission from [22]. Copyright 2017 Elsevier.

Figure 8

DFT-calculated LUMO energy and isosurfaces of model BMI and BMBI. (The grey, blue, and white balls represent C, N, and H atoms, respectively). Reprinted with permission from [22]. Copyright 2017 Elsevier.

Figure 9

Molecular structures of cationic compounds investigated by Sun and coworkers. Reprinted with permission from [13]. licensed under CC BY-ND 4.0.

Figure 10

Frontier molecular orbital energy of various cations in water. Reprinted with permission from [13]. licensed under CC BY-ND 4.0.

Figure 11

Hydroxide transport mechanisms in AAEMs. Reprinted with permission from [38]. Copyright 2017 Elsevier.

Figure 12

The QPE monomer used for classical molecular dynamics and first-principles molecular dynamics. Reprinted with permission from [38]. Copyright 2017 Elsevier.

Figure 13

Images of (a) a unit cell wherein QPE comprises 10 repeating units, (b) a unit cell wherein QPE comprises 15 repeating units, and (c) a unit cell wherein QPE comprises 20 repeating units. Reprinted with permission from [39]. Copyright 2017 Elsevier.

Figure 14

OH⁻ transport in hydrated AAEMs. Reprinted with permission from [41]. Copyright 2015 American Chemical Society.

Figure 15

Optimized DFT structure used to predict the diffusion coefficients. Reprinted with permission from [29]. Copyright 2020 Elsevier.

Figure 16

AAEM morphology simulated at different hydration levels. Reprinted with permission from [19]. Copyright 2017 American Chemical Society.

Figure 17

Machine learning methodology used to predict OH⁻ conductivity. Reprinted with permission from [55]. Copyright 2022 Elsevier.

Figure 18

Classification model for identifying the type of functional cationic groups. Reprinted with permission from [55]. Copyright 2022 Elsevier.