Three-dimensional nanoimaging of fuel cell catalyst layers

Area of Science:

• Materials Science
• Electrochemistry
• Nanotechnology

Background:

• Proton exchange membrane fuel cells (PEMFCs) rely on platinum-group-metal (PGM) nanocatalysts on carbon supports.
• The porous structure of catalyst layers, including ionomer networks, critically impacts mass transport and fuel cell performance.
• Understanding local structural morphology is key to reducing performance losses.

Purpose of the Study:

• To develop and apply a 3D visualization technique for detailed catalyst layer morphology.
• To quantitatively analyze ionomer distribution, platinum location, and accessibility within the catalyst layer.
• To establish a link between catalyst layer architecture and transport properties.

Main Methods:

• Implementation of deep-learning-aided cryogenic transmission electron tomography (cryo-TEMT) for image restoration.
• Quantitative analysis of catalyst layer morphology at the local-reaction-site scale.
• Validation of computed metrics against experimental measurements.

Main Results:

• Detailed 3D visualization of catalyst layer structure was achieved.
• Metrics such as ionomer morphology, coverage, homogeneity, and platinum accessibility were computed.
• Results were directly compared and validated with experimental data.

Conclusions:

• The developed deep-learning cryo-TEMT methodology enables quantitative investigation of catalyst layer architecture.
• This approach facilitates understanding the relationship between morphology, transport properties, and fuel cell performance.
• Findings contribute to optimizing catalyst layer design for improved PEMFC efficiency.