Iridium-Based Perovskites as Efficient Oxygen Evolution Reaction Catalysts in Acid Media
Bibliographic record
Abstract
A series of perovskite-based catalysts were synthesized for oxygen evolution reactions (OERs), primarily intended for anodic reactions in the zinc electrowinning process. OER represents a significant portion of the energy consumption in the zinc electrowinning process, and our objective is to explore the possibility of using Ir-based perovskite catalysts to reduce this energy consumption. Ba–Ir perovskite was used as the starting point, and it was doped by other cations (M) to achieve BaM x Ir 1– x O 3 perovskites. Solid-state reaction (SSR) was employed to prepare the catalytic compounds. The crystalline structure of materials was investigated using X-ray diffraction (XRD). Potentiodynamic polarization and electrochemical galvanostatic tests were used to assess the performance of the synthesized materials with respect to the OER. Morphology and surface chemical composition of the optimized compound were evaluated, respectively, using scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS) analysis methods. The results reported here show that, compared to the benchmark IrO 2 catalyst, the catalytic performance of Ir in a perovskite structure was significantly improved, while its Ir content was substantially lower. However, the activity of these compounds in sulfuric acid media is reduced over time. We found that the main deactivation mechanism of the catalysts is related to the formation of the Ba sulfate on the catalyst. The deactivation rate is highly dependent on the doped cation (M). BaNb 0.2 Ir 0.8 O 3, with 42% less iridium content, was found to be the best catalyst among the synthesized formulations, satisfying the requirements of catalytic activity and longevity in highly acidic environments.
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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".