Abstract
Titanium-based porous transport layers (PTLs) and iridium-based catalyst layers (CLs) are two main components of proton exchange membrane water electrolyzers (PEMWEs). PTLs are typically coated with platinum to minimize interfacial losses and to support long-term operation. Optimizing coatings and the PTL-CL interface requires comprehensive characterization. This study establishes time-of-flight secondary ion mass spectrometry (ToF-SIMS) as a valuable technique for PTL characterization, addressing capabilities and limitations related to PTL morphology. A methodology was developed that uses a Cs+ sputter beam for dynamic depth profiling, with data collected in both positive-ion (MCs+) and negative-ion modes to generate depth profiles, 2D ion maps, and 3D ion reconstructions. ToF-SIMS detected relative differences in platinum-layer thickness between samples; these trends were validated by cross-sectional scanning transmission electron microscope (STEM) measurements and flat-titanium substrate controls. Interfacial oxide layers are identified in both ion modes, with enhanced oxide sensitivity in negative mode. The technique’s high sensitivity enables detection of nanometer-scale coatings and trace impurities within the bulk PTL structure. These results provide a methodological framework for analyzing Pt-coated PTLs, with the potential to extend to other components in PEMWEs and other electrolyzer systems.
| Original language | English |
|---|---|
| Pages (from-to) | 10224-10237 |
| Number of pages | 14 |
| Journal | ACS Applied Nano Materials |
| Volume | 9 |
| Issue number | 22 |
| DOIs | |
| State | Published - Jun 5 2026 |
Funding
This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Hydrogen and Fuel Cell Technologies Office’s FY2020 H2@Scale New Markets FOA, Award Number DE-EE0009236. This work was authored, in part, by the National Laboratory of the Rockies for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Hydrogen and Fuel Cells Technologies Office. This material makes use of the ToF-SIMS system at the Colorado School of Mines, which was supported by the National Science Foundation under Grant No. 1726898, as well as the E-XPS system at the Colorado School of Mines which was supported by the National Science Foundation under Grant No. 1626619. We also acknowledge Mines’ Shared Instrumentation Facility for access and support of various instruments used in this work. STEM research was conducted as part of a user project at the Center for Nanophase Materials Sciences (CNMS), which is a US Department of Energy, Office of Science User Facility at Oak Ridge National Laboratory. This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Hydrogen and Fuel Cell Technologies Office’s FY2020 H2@Scale New Markets FOA, Award Number DE-EE0009236. This work was authored, in part, by the National Laboratory of the Rockies for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Hydrogen and Fuel Cells Technologies Office. This material makes use of the ToF-SIMS system developed at the Colorado School of Mines, which was supported by the National Science Foundation under Grant No. 1726898, as well as the E-XPS system at the Colorado School of Mines which was supported by the National Science Foundation under Grant No. 1626619. We also acknowledge Mines’ Shared Instrumentation Facility for access and support of various instruments used in this work. STEM research was conducted as part of a user project at the Center for Nanophase Materials Sciences (CNMS), which is a US Department of Energy, Office of Science User Facility at Oak Ridge National Laboratory. This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes. Artificial intelligence (AI) was used in the generation of the schematic inFigure 1using Microsoft Copilot. This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Hydrogen and Fuel Cell Technologies Office’s FY2020 H2@Scale New Markets FOA, Award Number DE-EE0009236. This work was authored, in part, by the National Laboratory of the Rockies for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Hydrogen and Fuel Cells Technologies Office. This material makes use of the ToF-SIMS system at the Colorado School of Mines, which was supported by the National Science Foundation under Grant No. 1726898, as well as the E-XPS system at the Colorado School of Mines which was supported by the National Science Foundation under Grant No. 1626619. We also acknowledge Mines’ Shared Instrumentation Facility for access and support of various instruments used in this work. STEM research was conducted as part of a user project at the Center for Nanophase Materials Sciences (CNMS), which is a US Department of Energy, Office of Science User Facility at Oak Ridge National Laboratory. This material is based upon work supported by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) under the Hydrogen and Fuel Cell Technologies Office’s FY2020 H2@Scale New Markets FOA, Award Number DE-EE0009236. This work was authored, in part, by the National Laboratory of the Rockies for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Hydrogen and Fuel Cells Technologies Office. This material makes use of the ToF-SIMS system developed at the Colorado School of Mines, which was supported by the National Science Foundation under Grant No. 1726898, as well as the E-XPS system at the Colorado School of Mines which was supported by the National Science Foundation under Grant No. 1626619. We also acknowledge Mines’ Shared Instrumentation Facility for access and support of various instruments used in this work. STEM research was conducted as part of a user project at the Center for Nanophase Materials Sciences (CNMS), which is a US Department of Energy, Office of Science User Facility at Oak Ridge National Laboratory. This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes. Artificial intelligence (AI) was used in the generation of the schematic in using Microsoft Copilot.
Keywords
- characterization
- method development
- porous transport layers
- protective coatings
- time-of-flight secondary ion mass spectrometry
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