Formation of Core‐Shell Ir@TiO<sub>2</sub> Nanoparticles through Hydrogen Treatment as Acidic Oxygen Evolution Reaction Catalysts
Bibliographic record
Abstract
Abstract The transition to a sustainable energy economy requires the availability of renewably produced hydrogen through proton exchange membrane water electrolysis. The techno‐economic viability of this technology requires addressing materials challenges regarding the lack of active and stable catalysts for the electrochemical oxygen evolution reaction (OER) in acidic conditions. Herein, core‐shell iridium/titanium dioxide (Core‐shell Ir@TiO 2 ) catalysts for acidic OER are synthesized through a polyol method to create TiO 2 nanoparticles, followed by urea reduction with Ir, and subsequent annealing in hydrogen. The formation process of the core‐shell structure is observed through in situ environmental transmission electron microscopy under annealing conditions. Ir segregation occurred from an initially blended mixed metal oxide structure to a core‐shell configuration at 500 °C. Core‐shell Ir@TiO 2 showed a three‐fold higher stability number (i.e., S‐number) than commercial IrO x (3.34 × 10 6 versus 1.02 × 10 6 ). Furthermore, an Ir‐mass normalized activity of 1,880 A g Ir −1 at 1.7 V versus RHE is measured for Core‐shell Ir@TiO 2 , compared to 624 A g Ir −1 for commercial IrO x . The developed synthetic route to prepare a composite structure with a TiO 2 core and Ir‐based shell has enabled an Ir content reduction without a compromise in activity and stability, thus offering a promising avenue for developing next‐generation catalysts tailored for acidic water electrolysis.
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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".