Unique Self-Phosphorylating Polybenzimidazole of the 6F Family for HT-PEM Fuel Cell Application.

High-temperature polymer-electrolyte membrane fuel cells (HT-PEMFCs) are a very important type of fuel cells since they operate at 150-200 °C, making it possible to use hydrogen contaminated with CO. However, the need to improve the stability and other properties of gas-diffusion electrodes still impedes their distribution. Self-supporting anodes based on carbon nanofibers (CNF) are prepared using the electrospinning method from a polyacrylonitrile solution containing zirconium salt, followed by pyrolysis. After the deposition of Pt nanoparticles on the CNF surface, the composite anodes are obtained. A new self-phosphorylating polybenzimidazole of the 6F family is applied to the Pt/CNF surface to improve the triple-phase boundary, gas transport, and proton conductivity of the anode. This polymer coating ensures a continuous interface between the anode and proton-conducting membrane. The polymer is investigated using CO2 adsorption, TGA, DTA, FTIR, GPC, and gas permeability measurements. The anodes are studied using SEM, HAADF STEM, and CV. The operation of the membrane-electrode assembly in the H2/air HT-PEMFC shows that the application of the new PBI of the 6F family with good gas permeability as a coating for the CNF anodes results in an enhancement of HT-PEMFC performance, reaching 500 mW/cm2 at 1.3 A/cm2 (at 180 °C), compared with the previously studied PBI-O-PhT-P polymer.

Poly(ionic liquid)/OPBI Composite Membrane with Excellent Chemical Stability for High-Temperature Proton Exchange Membrane.

Despite the outstanding proton conductivity of phosphoric acid (PA)-doped polybenzimidazole (PBI) membranes as high-temperature proton exchange membranes (HT-PEMs), chemical stability is a critical issue for the operation life of PEM fuel cells (PEMFCs). Herein, we introduced polymerized [HVIM]H2PO4 ionic liquids (PIL) into an OPBI membrane to accelerate proton transfer and enhance the chemical stability of the membrane. Based on the regulation of the intrinsic viscosity of PIL, the entanglement between PIL chains and OPBI chains is enhanced to prevent the loss of PIL and the oxidative degradation of membrane materials. The PIL/OPBI membrane with the intrinsic viscosity of 2.34 dL·g-1 (2.34-PIL/OPBI) exhibited the highest proton conductivity of 113.9 mS·cm-1 at 180 °C, which is 3.5 times that of the original OPBI membrane. The 2.34-PIL/OPBI membrane exhibited the highest remaining weight of 92.1% under harsh conditions (3 wt% H2O2; 4 ppm Fe2+ at 80 °C) for 96 h, and a much lower attenuation amplitude than the OPBI did in mechanical strength and proton conductivity performance. Our present work demonstrates a simple and effective method for blending PIL with OPBI to enhance the chemical durability of the PA-PBI membranes as HT-PEMs.

Study on water and oxygen transfer characteristics of HT-PEM fuel cells.

In this study, a steady-state model is developed by combining mechanical, Navier-Stokes, Maxwell-Stefan, and Butler-Volmer equations. This model is then used to investigate the influences of diffusion layer thickness deformation under a specific assembly force on the porosity distribution as an indicator of fuel cell performance. The HT-PEM (high temperature proton exchange membrane) fuel cell model is built using COMSOL Multiphysics software, simulating the changes in diffusion layer porosity under different thicknesses of the diffusion layer, thus analyzing the trends in variation of water and oxygen concentration in the cathode diffusion layer. The battery has different current densities at different operating potentials. The influence of the working potential on the mass transfer concentration and the variation in the mass transfer concentration of the diffusion layer under the different areas of flow channel and flow ridge is discussed. The simulation results have a certain reference value for the optimization of mass transfer in a diffusion layer. The results reveal the combined effect of the assembly force and flow field, which makes the porosity distribution uneven and results in remarkable lateral current in the gas diffusion layer (GDL). The thicker the diffusion layer, the less oxygen consumed, and a large amount of oxygen is retained in the gaseous diffusion layer. It can be concluded that thicker diffusion layer is conducive to more uniform mass transfer and diffusion. These results can potentially be used to promote the performance and application of HT-PEMFC.

Self-Phosphorylated Polybenzimidazole: An Environmentally Friendly and Economical Approach for Hydrogen/Air High-Temperature Polymer-Electrolyte Membrane Fuel Cells.

The development of phosphorylated polybenzimidazoles (PBI) for high-temperature polymer-electrolyte membrane (HT-PEM) fuel cells is a challenge and can lead to a significant increase in the efficiency and long-term operability of fuel cells of this type. In this work, high molecular weight film-forming pre-polymers based on N1,N5-bis(3-methoxyphenyl)-1,2,4,5-benzenetetramine and [1,1'-biphenyl]-4,4'-dicarbonyl dichloride were obtained by polyamidation at room temperature for the first time. During thermal cyclization at 330-370 °C, such polyamides form N-methoxyphenyl substituted polybenzimidazoles for use as a proton-conducting membrane after doping by phosphoric acid for H2/air HT-PEM fuel cells. During operation in a membrane electrode assembly at 160-180 °C, PBI self-phosphorylation occurs due to the substitution of methoxy-groups. As a result, proton conductivity increases sharply, reaching 100 mS/cm. At the same time, the current-voltage characteristics of the fuel cell significantly exceed the power indicators of the commercial BASF Celtec® P1000 MEA. The achieved peak power is 680 mW/cm2 at 180 °C. The developed approach to the creation of effective self-phosphorylating PBI membranes can significantly reduce their cost and ensure the environmental friendliness of their production.

Machine learning for guiding high-temperature PEM fuel cells with greater power density.

High-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) are enticing energy conversion technologies because they use low-cost hydrogen generated from methane and have simple water and heat management. However, proliferation of this technology requires improvement in power density. Here, we show that Machine Learning (ML) tools can help guide activities for improving HT-PEMFC power density because these tools quickly and efficiently explore large search spaces. The ML scheme relied on a 0-D, semi-empirical model of HT-PEMFC polarization behavior and a data analysis framework. Existing datasets underwent support vector regression analysis using a radial basis function kernel. In addition, the 0-D, semi-empirical HT-PEMFC model was substantiated by polarization data, and synthetic data generated from this model was subject to dimension reduction and density-based clustering. From these analyses, pathways were revealed to surpass 1 W cm-2 in HT-PEMFCs with oxygen as the oxidant and CO containing hydrogen.