Ordered intermetallic nanoparticles are promising electrocatalysts with enhanced activity and durability for the oxygen-reduction reaction (ORR) in proton-exchange membrane fuel cells (PEMFCs). The ordered phase is generally identified based on the existence of superlattice ordering peaks in powder X-ray diffraction (PXRD). However, after employing a widely used postsynthesis annealing treatment, we have found that claims of “ordered” catalysts were possibly/likely mixed phases of ordered intermetallics and disordered solid solutions. Here, we employed in situ heating, synchrotron-based, X-ray diffraction to quantitatively investigate the impact of a variety of annealing conditions on the degree of ordering of large ensembles of Pt3Co nanoparticles. Monte Carlo simulations suggest that Pt3Co nanoparticles have a lower order–disorder phase transition (ODPT) temperature relative to the bulk counterpart. Furthermore, we employed microscopic-level in situ heating electron microscopy to directly visualize the morphological changes and the formation of both fully and partially ordered nanoparticles at the atomic scale. In general, a higher degree of ordering leads to more active and durable electrocatalysts. The annealed Pt3Co/C with an optimal degree of ordering exhibited significantly enhanced durability, relative to the disordered counterpart, in practical membrane electrode assembly (MEA) measurements. The results highlight the importance of understanding the annealing process to maximize the degree of ordering in intermetallics to optimize electrocatalytic activity.
On the basis of a
set of machine learning predictions of glass formation in the Ni–Ti–Al
system, we have undertaken a high-throughput experimental study of
that system. We utilized rapid synthesis followed by high-throughput
structural and electrochemical characterization. Using this dual-modality
approach, we are able to better classify the amorphous portion of
the library, which we found to be the portion with a full width at
half maximum (fwhm) of >0.42 Å–1 for the
first sharp X-ray diffraction peak. Proper phase labeling is important
for future machine learning efforts. We demonstrate that the fwhm
and corrosion resistance are correlated but that, while chemistry
still plays a role in corrosion resistance, a large fwhm, attributed
to a glassy phase, is necessary for the highest corrosion resistance.
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