Molecular Analysis of the Unusual Stability of an IrNbO<i><sub>x</sub></i> Catalyst for the Electrochemical Water Oxidation to Molecular Oxygen (OER)
Bibliographic record
Abstract
Adoption of proton exchange membrane (PEM) water electrolysis technology on a global level will demand a significant reduction of today’s iridium loadings in the anode catalyst layers of PEM electrolyzers. However, new catalyst and electrode designs with reduced Ir content have been suffering from limited stability caused by (electro)chemical degradation. This has remained a serious impediment to a wider commercialization of larger-scale PEM electrolysis technology. In this combined DFT computational and experimental study, we investigate a novel family of iridium–niobium mixed metal oxide thin-film catalysts for the oxygen evolution reaction (OER), some of which exhibit greatly enhanced stability, such as minimized voltage degradation and reduced Ir dissolution with respect to the industry benchmark IrO x catalyst. More specifically, we report an unusually durable IrNbO x electrocatalyst with improved catalytic performance compared to an IrO x benchmark catalyst prepared in-house and a commercial benchmark catalyst (Umicore Elyst Ir75 0480) at significantly reduced Ir catalyst cost. Catalyst stability was assessed by conventional and newly developed accelerated degradation tests, and the mechanistic origins were analyzed and are discussed. To achieve this, the IrNbO x mixed metal oxide catalyst and its water splitting kinetics were investigated by a host of techniques such as synchrotron-based NEXAFS analysis and XPS, electrochemistry, and ab initio DFT calculations as well as STEM-EDX cross-sectional analysis. These analyses highlight a number of important structural differences to other recently reported bimetallic OER catalysts in the literature. On the methodological side, we introduce, validate, and utilize a new, nondestructive XRF-based catalyst stability monitoring technique that will benefit future catalyst development. Furthermore, the present study identifies new specific catalysts and experimental strategies for stepwise reducing the Ir demand of PEM water electrolyzers on their long way toward adoption at a larger scale.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".