Uncovering Activity-Stability Relationships in Mixed Ir-Based Catalysts Toward Improved Water Electrolysis
Bibliographic record
Abstract
The role of proton exchange membrane water electrolysers (PEMWE) in providing clean and reliable green hydrogen is undeniable in a fully decarbonized energy system. Indeed, the ambitious “Hydrogen Shot” initiative by the U.S. DoE seeks to decrease in a decade green hydrogen pricing down to 1$/Kg H2.(1) Such endeavour will require a mass-scale implementation of PEMWEs, which is currently bottlenecked by the use of scarce precious metal catalysts, particularly at the anode where loadings of the state-of-the-art iridium (Ir) catalyst typically range between 1-3mgIr cm-2.(2) Thus, recent strategies in the literature have aimed to reduce Ir loading with several approaches such as higher catalyst dispersion and preparation of mixed metal oxides (IrxM1-xOy). The latter is particularly promising if the cation substituent M is an earth-abundant metal highly active to the oxygen evolution reaction (OER), which could lead to drastic catalyst cost reductions retaining high activities. The activity-stability relationships of various Ir phases are well-reported,(3) but hardly studied for IrxM1-xOy where any preferential dissolution of M under OER operation could result in misleading conclusions. The work presented here aims to bridge this knowledge gap by online quantification of catalyst dissolution products with our electrochemical flow cell coupled to inductively-coupled plasma mass spectrometry setup. First, we evaluated these relationships in cationic-substituted IrxM1-xO2 (100) epitaxial thin films, model systems which will allow us to clearly uncover any cation-dependent trends. It was observed that the Ir stability in such model systems directly correlated with the OER enhancement effect of the cation substituent: higher OER activities induced by the cation yielded lower Ir stabilities and vice versa. In addition, the IrO2 matrix does not seem to affect the thermodynamic dissolution trends of the cation M. In conjunction with ex-situ XPS characterization, we successfully identified two promising cation substituents considering the experimental activity-stability trends: Mo and W. Next, we moved to the industrially-promising IrxRu1-xO2 nanoparticle (NP) systems, with different relative compositions and degrees of crystallinity. Although Ru generally presents high dissolution rates under OER potentials, preliminary studies on sputtered thin films revealed a drastic stabilization of the RuO2 matrix upon incorporation of less than 20 at. % Ir.(4) The one-step synthetic approach used here, flame spray pyrolysis, allowed to easily tune the NPs Ir:Ru ratio by the relative content of the metal precursors in the pyrolized solution. Additionally, a post-calcination step enabled to convert the NPs phase from hydrous to rutile-type oxide. Using as start-up/shut-down OER protocol, we observed at 1000-fold lower stability for hydrous vs. rutile-type IrxRu1-xO2 regardless of the composition after selective Ru leaching. For rutile-type IrxRu1-xO2 nanoparticles, the sequential on/off OER operation revealed a ca. 10-fold decrease in Ru dissolution upon Ir incorporation, as well as the key role of surface Ru in OER activity.(5) Minimal Ru losses (<1 at. %) led to the formation of an Ir-rich protective shell, which for Ir0.2Ru0.8O2 resulted in an OER performance shift equivalent to that of pristine Ir0.8Ru0.2O2. These results showcase, contrary to previous reports, that Ru-rich compositions can indeed be implemented in PEMWE devices. References: Hydrogen Shot -Hydrogen and Fuel Cell Technologies Office, in, https://www.energy.gov/eere/fuelcells/hydrogen-shot . C. V. Pham, D. Escalera‐López, K. Mayrhofer, S. Cherevko and S. Thiele, Advanced Energy Materials, 11 (2021). S. Geiger, O. Kasian, M. Ledendecker, E. Pizzutilo, A. M. Mingers, W. T. Fu, O. Diaz-Morales, Z. Li, T. Oellers, L. Fruchter, A. Ludwig, K. J. J. Mayrhofer, M. T. M. Koper and S. Cherevko, Nature Catalysis, 1, 508 (2018). O. Kasian, S. Geiger, P. Stock, G. Polymeros, B. Breitbach, A. Savan, A. Ludwig, S. Cherevko and K. J. J. Mayrhofer, Journal of The Electrochemical Society, 163, F3099 (2016). D. Escalera-López, S. Czioska, J. Geppert, A. Boubnov, P. Röse, E. Saraçi, U. Krewer, J.-D. Grunwaldt and S. Cherevko, ACS Catalysis, 11, 9300 (2021).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".