Proton‐Conducting, Vacancy‐Rich H <i> <sub>x</sub> </i> IrO <i> <sub>y</sub> </i> Nanosheets for the Fabrication of Low‐Ionomer‐Dependent Anode Catalyst Layer in PEM Water Electrolyzer
Bibliographic record
Abstract
Abstract The anode catalyst layer is composed of catalytically functional IrO x and protonic conducting ionomer and largely dictates catalytic performance of proton exchange membrane water electrolyzer (PEMWE). Here, we report a new type of anode nanocatalyst that possesses both IrO x ’s catalytic function and high proton conductivity that traditional anode catalysts lack and demonstrate its ability to construct high‐performance, low‐ionomer‐dependent anode catalyst layer, the interior of which—about 85% of total catalyst layer—is free of ionomers. The proton‐conducting anode nanocatalyst is prepared via protonation of layered iridate K 0.5 (Na 0.2 Ir 0.8 )O 2 and then exfoliation to produce cation vacancy‐rich, 1 nm‐thick iridium oxide nanosheets (labeled as □‐H x IrO y ). Besides being a proton conductor, the □‐H x IrO y is found to have abundant catalytic active sites for the oxygen evolution reaction due to the optimization of both edge and in‐plane iridium sites by multiple cation vacancies. The dual functionality of □‐H x IrO y allows the fabrication of low‐iridium‐loading, low‐ionomer‐dependent anode catalyst layer with enhanced exposure of catalytic sites and reduced electronic contact resistance, in contrast to common fully mixed catalyst/ionomer layers in PEMWE. This work represents an example of realizing the structural innovation in anode catalyst layer through the bifunctionality of anode catalyst.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".