Morphology Evolution and Adsorption Behavior of Ionomers from Solution to Pt/C Substrates

Deep Learning to Reveal the Distribution and Diffusion of Water Molecules in Fuel Cell Catalyst Layers

Water management in the catalyst layers (CLs) of proton-exchange membrane fuel cells is crucial for its commercialization and popularization. However, the high experimental or computational cost in obtaining water distribution and diffusion remains a bottleneck in the existing experimental methods and simulation algorithms, and further mechanistic exploration at the nanoscale is necessary. Herein, we integrate, for the first time, molecular dynamics simulation with our customized analysis framework based on a multiattribute point cloud dataset and an advanced deep learning network. This was achieved through our workflow that generates simulated transport data of water molecules in the CLs as the training and test dataset. Deep learning framework models the multibody solid-liquid system of CLs on a molecular scale and completes the mapping from the Pt/C substrate structure and Nafion aggregates to the density distribution and diffusion coefficient of water molecules. The prediction results are comprehensively analyzed and error evaluated, which reveals the highly anisotropic interaction landscape between 50,000 pairs of interacting nanoparticles and explains the structure and water transport property relationship in the hydrated Nafion film on the molecular scale. Compared to the conventional methods, the proposed deep learning framework shows computational cost efficiency, accuracy, and good visual display. Further, it has a generality potential to model macro- and microscopic mass transport in different components of fuel cells. Our framework is expected to make real-time predictions of the distribution and diffusion of water molecules in CLs as well as establish statistical significance in the structural optimization and design of CLs and other components of fuel cells.

Interfacial Distribution of Nafion Ionomer Thin Films on Nitrogen-Modified Carbon Surfaces

Chemically modified carbon supports for the cathode catalyst layers of polymer electrolyte fuel cells (PEFCs) show considerable promise for boosting the oxygen reduction reaction. This study evaluated the ionomer distribution of Nafion ionomer thin films on nitrogen (N)-modified carbon surfaces along their depth direction. Neutron reflectivity (NR) measurements performed using the double-contrast technique with H2O and D2O revealed that the introduction of N functional groups to carbon thin films promoted ionomer adsorption onto the surface under wet conditions (22 °C, 85% relative humidity). Molecular dynamics (MD) simulations conducted to verify the origin of the robust contact between the ionomer and N-modified carbon surface revealed an ionomer adsorption mechanism on the N-modified carbon surfaces, which involved Coulomb interactions between the positively charged carbon surface and the ionomer side chains with negatively charged sulfonic acid groups. The positive surface charge, which was determined using the contents of the N functional groups estimated by X-ray photoelectron spectroscopy, was found to be sufficient as an impetus for ionomer adsorption. This strategy involving NR measurements and MD simulations can provide insights into the solid-ionomer interfacial structures in a cathode catalyst layer and can therefore be extensively employed in studies on PEFCs.