Hydroxide ions transportation in polynorbornene anion exchange membrane
Graphical abstract
Introduction
Anion exchange membranes (AEMs) have attracted considerable attention as separators in alkaline membrane fuel cells (AMFCs) [1]. Compared to proton exchange membrane fuel cells (PEMFCs), AEMFCs can work under alkaline conditions with much higher oxygen reduction kinetics, and can prompt to use of non-precious metal catalysts, greatly reducing the cost of fuel cell devices. Corrosion issues are less common in an alkaline environment [2]. However, despite great promise, AMFC application has many technical problems that must be resolved before AMFC can be applied [3]. The first is identifying the impact of water content on the microstructure of the membrane and transport limitations of hydroxide ions. During the normal operation of AMFCs, water is generated as a by-product at the anode, as hydroxide ions transport from the cathode, meet and react with hydrogen molecules. The ion conductivity of the membrane can be improved with a sufficient quantity of water. However, this causes the disadvantage of mechanical properties [[4], [5], [6], [7], [8], [9], [10]]. Recognizing the impact of water content on the microstructure of polymer and transport mechanism of hydroxide ions is helpful not only to the design of polymer structure, but also to the efficient operation of fuel cells [11].
Molecular simulation has been applied to analyze the movement of ion and microstructure of polymer backbone in membranes for years. Besides, the functional polymer can be designed and synthesized by molecular simulation. The ion conductivity mechanism and the properties of the various ion exchange membranes have been predicted [3,4,[11], [12], [13], [14], [15], [16], [17], [18]]. With the molecular dynamic (MD) simulation, the reporters [6] have constructed different molecular models of proton exchange membranes for understanding the relationship between the aggregation structures of the polymer membrane, and the transports of the protons amongst membranes through changing the temperatures. The previous works [5] displayed critical factors which influenced the distribution of particles, for example, the H2O around sulfonate sites inside the functional polymer membranes. Kim et al. [4] used MD simulations to study the ion exchange mechanism in the poly(ether ether ketone) (PEEK) ion exchange membrane. The simulation results were a validation for actual results of the cationic conductivity and ion exchange capacity (IEC). Oh and coworkers [17] reported the transported behavior of cation at different temperatures in fully hydrated Nafion membranes through devising cell model. They proposed that the distribution of H2O molecules in Nafion membrane was different from that of pure water clusters greatly, and similar to the distribution in hexagonal ice by using the relation between mean square displacement (MSD) and time.
The transmission mechanism of OH− in AEMs is the same as cation exchange membrane. The factors of the relative humidity, the temperature, and pressure play on the important roles on the conductivity of OH− [[18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29]]. Thus a reference has been provided for studying the OH− conduction mechanisms [[30], [31], [32], [33], [34]]. Various mechanisms have been reported [[24], [25], [26], [27], [28], [29],35,36]. For example, Grotthuss' mechanism occurs attributed to the switching of covalent bonds and hydrogen, en masse diffusion means the OH− around the quaternary ammonium side chains of the respective membrane. The migration and diffusion attributed to the concentration and potential gradient acting on the charged particles [35]. Ion transport is often the result of coordination of the several mechanisms. Unlike proton, the structural diffusion of hydroxide ions is few reported. As a note, the data refer to experimental technique can be not provided for confirming the detailed mechanism of the OH− diffusion.
In the present work, amorphous cells composed of polynorbornene chains, hydroxide ions, and water molecules were created by molecular modeling techniques to analyze that the transportation of hydroxide ions was related to the microstructure of anion exchange membranes. From the results of MD simulations, the calculated values for hydroxide ions were compared to the experimental values, the results from various water contents and temperature were also examined and used to predict the anion diffusivity and the interaction among the polymer chains and water molecules.
Section snippets
Stimulate software and parameters
Material Studio software package (Accelrys Inc., USA) was used for Molecular simulations. Fig. 1 shows the polynorbornene chemical formula (a) and repeat units (b). Both were used to build the unit cell of polymer (c). The degree of polymerization of polymer chains was set to 100. Polymer R2 and R3 including 198 and 150 functional groups were randomly arranged within the chains. The initial cell's density was 1.0 g/cm3. The parameters for building the Model are shown in Table 1.
Stimulation methods
This hydrophobic
Water content
The mean square displacement curves (MSDs) of hydroxide ions in R2-50 and R3-30, which were selected based on the experimental IEC and water content, were analyzed. R2-50 had a higher IEC and water content than R3-30. The water content was increased when the number of functional groups was high [37], because of the interaction between quaternary ammonium groups and water molecules. AEMs, which contain high IEC and water absorption, had high ion conductivity. The curves shown in Fig. 2 suggest
Conclusion
The structures of H2O, OH−, and polymer backbone were built and optimized. Molecular models for anion exchange membranes were designed for understanding the microstructures of the membranes and the transport of the OH− in the membranes at different temperatures and water contents. After MD simulations, the MSDs of OH- and RDFs of N-H2O, N-OH and OH-H2O were calculated to analyze the impact of water content and temperature on the transport of hydroxide ions. According to the simulation results,
Author contribution
Chao Wang and Biming Mo contributed equally to this work.
Acknowledgements
The authors appreciated the financial supports by the National Natural Science Foundation of China (Project No. 51503187, 21504037 and 51603194); the National key R&D Project (Project No. 2016YFE0102700); the Shanxi provincial foundation for science and technology research (Project No. 201601D021058, 201701D221050); and one hundred talented program. Partial support is also from the NIMHD-RCMI grant number 5G12MD007595 from the National Institute of Minority Health, Health Disparities and the
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