Water Oxidation over Au-Pd/TiO2 as a Substitute for Iridium-Based Catalysts
Bibliographic record
Abstract
Water oxidation is one of the most important reactions needed for a transition to a green economy. The reaction relies on extracting electrons from oxygen anions and is commonly studied using homogenous catalysts based on Ru or Ir metals. Because of Ir scarcity and its relative instability in acidic environments, metals to replace it are sought after. In this study, we have synthesized Au-Pd-based catalysts deposited on TiO2 with different ratios in order to mimic IrO2 valence orbitals (Ir5d) by the hybrid valence orbitals of Au5d and Pd4d and compared their heterogeneous catalytic activity for the evolution of O2 from water in the presence of cerium ammonium nitrate (CAN). Au-Pd-based catalysts were found to be active at a particular nominal atomic ratio. At an atomic ratio of 1 Au to 2 Pd and 1 Au to 3 Pd, the catalysts were active and stable for oxygen production from water. Long-term runs up to 20,000 min still showed the expected stoichiometry between O2 production and CAN consumption (1 to 4). However, catalysts with a reverse ratio were not active. Also, the monometallic catalysts were found to be not active for the reaction. We link the reason for the activity of Au-Pd with this specific ratio to the shape and energy position of their valence band that might be similar to those of IrO2 particles. While the turnover numbers of the Au-Pd-based catalysts were found to be lower than those of IrO2-based catalysts, on the same support in a heterogenous system, there is considerable potential upon further optimization for these two metals to replace IrO2 for a water oxidation reaction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".