Tracking Nanoparticle Degradation across Fuel Cell Electrodes by Automated Analytical Electron Microscopy

Haoran Yu, Michael J. Zachman, Kimberly S. Reeves, Jae Hyung Park, Nancy N. Kariuki, Leiming Hu, Rangachary Mukundan, Kenneth C. Neyerlin, Deborah J. Myers, David A. Cullen

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Nanoparticles are an important class of materials that exhibit special properties arising from their high surface area-to-volume ratio. Scanning transmission electron microscopy (STEM) has played an important role in nanoparticle characterization, owing to its high spatial resolution, which allows direct visualization of composition and morphology with atomic precision. This typically comes at the cost of sample size, potentially limiting the accuracy and relevance of STEM results, as well as the ability to meaningfully track changes in properties that vary spatially. In this work, automated STEM data acquisition and analysis techniques are employed that enable physical and compositional properties of nanoparticles to be obtained at high resolution over length scales on the order of microns. This is demonstrated by studying the localized effects of potential cycling on electrocatalyst degradation across proton exchange membrane fuel cell cathodes. In contrast to conventional, manual STEM measurements, which produce particle size distributions representing hundreds of particles, these high-throughput automated methods capture tens of thousands of particles and enable nanoparticle size, number density, and composition to be measured as a function of position within the cathode. Comparing the properties of pristine and degraded fuel cells provides statistically robust evidence for the inhomogeneous nature of catalyst degradation across electrodes. These results demonstrate how high-throughput automated STEM techniques can be utilized to investigate local phenomena occurring in nanoparticle systems employed in practical devices.

Original languageEnglish
Pages (from-to)12083-12094
Number of pages12
JournalACS Nano
Volume16
Issue number8
DOIs
StatePublished - Aug 23 2022

Funding

This material is based on work performed by the Million Mile Fuel Cell Truck (M2FCT) Consortium, technology managers Greg Kleen and Dimitrios Papageorgopoulus, which is supported by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Hydrogen and Fuel Cell Technologies Office. For more information, visit https://millionmilefuelcelltruck.org . Electron microscopy research was supported by the Center for Nanophase Materials Sciences (CNMS), which is a U.S. Department of Energy, Office of Science User Facility at Oak Ridge National Laboratory. The Talos F200X S/TEM tool was provided by U.S. DOE, Office of Nuclear Energy, Fuel Cycle R&D Program, and the Nuclear Science User Facilities. The X-ray scattering experiments were performed at beamline 9-ID-C at the Advanced Photon Source (APS) at Argonne National Laboratory (ANL). Use of the APS, an Office of Science user facility operated by ANL, is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AS02-06CH11357. This work was authored in part by Alliance for Sustainable Energy, LLC, the manager and operator of the National Renewable Energy Laboratory for the U.S. Department of Energy under Contract No. DE-AC36-08GO28308, Los Alamos National Laboratory under Contract No. 89233218CNA000001 operated by Triad National Security, LLC, for the National Nuclear Security Administration of U.S. Department of Energy, and UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy.

Keywords

  • Python
  • automation
  • nanoparticles
  • proton exchange membrane fuel cell
  • scanning transmission electron microscopy

Fingerprint

Dive into the research topics of 'Tracking Nanoparticle Degradation across Fuel Cell Electrodes by Automated Analytical Electron Microscopy'. Together they form a unique fingerprint.

Cite this