A Novel Water Oxidation Catalyst Made By the Facile Deposition of Ruthenium on Graphene Oxide
Bibliographic record
Abstract
As populations grow worldwide, there is an ever-increasing need for sustainable and renewable energy sources to keep up with the high demand of energy. One promising technology is electrochemical water-splitting, which creates hydrogen and oxygen at the cathode and anode, respectively. Developing viable, stable catalysts for water splitting is critical since this reaction is truly a green, sustainable method to generate hydrogen fuel. However, more active electrocatalysts for the water oxidation reaction (WOR) are needed to improve the viability of this technology. Currently, these catalysts are most often composed of rare and expensive earth metals such as iridium. More sustainable materials such as those based on ruthenium have been recently investigated as alternatives to the rare metal systems. Ruthenium-based materials are an ideal choice as Ru offers multiple available oxidation states to help facilitate the water-splitting reaction and possibly lower overpotentials[1]. However, many ruthenium-based materials are prone to stability issues in acidic media at high over potentials. In addition, sluggish kinetics are often observed [2]. In this work we synthesized graphene oxide, via a simple and low-cost method, and utilized it as a support for the Ru oxide nanocatalyst[3]. We used graphene oxide due it high mechanical strength, favorable electronic properties, and the ease at which it can be doped with metals[4]. Ruthenium oxide nanoparticles were deposited on the surface of the graphene oxide at a 5wt% metal loading by a facile method. The materials were examined electrochemically in acidic media for the water oxidation reaction, where we observed low overpotentials and high stability during prolonged testing. This simple preparation method could be used to efficiently synthesize active and highly stable water-oxidation catalysts that are more cost-effective. This novel ruthenium stabilized on graphene catalyst could aid in the future development of more sustainable materials for the future of renewable energy technology. References [1] Kamdar, J.M.; Grotjahn, D.B. Molecules 2019, 24, 494. [2] Haschke, S.; Pankin, D.; Mikhailovskii, V.; Barr, M. K. S.; Both-Engel, A.; Manshina, A.; Bachmann, J. Beilstein J. Nanotechnol. 2019, 10, 157–167 [3] Liu, J.; Poh C. K.; Zhan, D.; Lai, L; Lim, S. H.; Wang, L.; Liu, X.; Sahoo, N. G.; Li, C.; Shen, Z.; Lin, J. Nano Energy. 2013, 3, 377-386 [4] Tu, W.; Lei, J.; Zhang, S.; Ju, H. Chemistry – A European Journal. 2010, 16, 10771-10777. Figure 1
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".