High-temperature PEM fuel cell electrode catalyst layers part 1: Microstructure reconstructed using FIB-SEM tomography and its calculated effective transport properties

Catalyst Development for High-Temperature Polymer Electrolyte Membrane Fuel Cell (HT-PEMFC) Applications

A constant increase in global emission standard is causing fuel cell (FC) technology to gain importance. Over the last two decades, a great deal of research has been focused on developing more active catalysts to boost the performance of high-temperature polymer electrolyte membrane fuel cells (HT-PEMFC), as well as their durability. Due to material degradation at high-temperature conditions, catalyst design becomes challenging. Two main approaches are suggested: (i) alloying platinum (Pt) with low-cost transition metals to reduce Pt usage, and (ii) developing novel catalyst support that anchor metal particles more efficiently while inhibiting corrosion phenomena. In this comprehensive review, the most recent platinum group metal (PGM) and platinum group metal free (PGM-free) catalyst development is detailed, as well as the development of alternative carbon (C) supports for HT-PEMFCs.

Advances in Focused Ion Beam Tomography for Three-Dimensional Characterization in Materials Science

Over the years, FIB-SEM tomography has become an extremely important technique for the three-dimensional reconstruction of microscopic structures with nanometric resolution. This paper describes in detail the steps required to perform this analysis, from the experimental setup to the data analysis and final reconstruction. To demonstrate the versatility of the technique, a comprehensive list of applications is also summarized, ranging from batteries to shale rocks and even some types of soft materials. Moreover, the continuous technological development, such as the introduction of the latest models of plasma and cryo-FIB, can open the way towards the analysis with this technique of a large class of soft materials, while the introduction of new machine learning and deep learning systems will not only improve the resolution and the quality of the final data, but also expand the degree of automation and efficiency in the dataset handling. These future developments, combined with a technique that is already reliable and widely used in various fields of research, are certain to become a routine tool in electron microscopy and material characterization.