State-of-the-Art Iridium-Based Catalysts for Acidic Water Electrolysis: A Minireview of Wet-Chemistry Synthesis Methods
Bibliographic record
Abstract
With the increasing demand for clean hydrogen production, both as a fuel and an indispensable reagent for chemical industries, acidic water electrolysis has attracted considerable attention in academic and industrial research. Iridium is a well-accepted active and corrosion-resistant component of catalysts for oxygen evolution reaction (OER). However, its scarcity demands breakthroughs in catalyst preparation technologies to ensure its most efficient utilisation. This minireview focusses on the wet-chemistry synthetic methods of the most active and (potentially) durable iridium catalysts for acidic OER, selected from the recent publications in the open literature. The catalysts are classified by their synthesis methods, with authors’ opinion on their practicality. The review may also guide the selection of the state-of-the-art iridium catalysts for benchmarking purposes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".