Simple Deposition of Metal Salts on Graphene Oxide As Novel Water Oxidation Catalysts
Bibliographic record
Abstract
As populations grow world-wide, there is an ever increasing need for more sustainable and renewable energy sources to keep up with the high demand of energy needed. One promising high energy source is the water splitting reaction where water is split into hydrogen and oxygen gas. However, the materials usually required to facilitate this reaction are often rare and expensive earth metals such as iridium. Thus, the focus has been on using Ir-free materials as a cost-efficient alternative. One such metal is ruthenium, Ru is ideal as it has many oxidation states to help facilitate the water splitting reaction, to aid in reducing the over potentials[1]. Yet, ruthenium-based materials have problems such as being prone to stability issues in acidic media, high over potentials and slow kinetics, so improvements should be made on catalyst design to lower over potentials and improve stability. On the other hand, Ru can also be considered a somewhat rare metal, thus further inexpensive and non-precious metals should be used in the future designs of these materials to lower cost without sacrificing activity. In this work we synthesized graphene oxide electrochemically from a graphite rod source, using a simple, low cost method[2]. Graphene was chosen to stabilize the nanoparticles and enhance the conductivity of the resulting materials. Metal nanoparticles from either a Ru(III) or Ru(VII) salt precursor were deposited on the surface of the graphene oxide using a facile method of preparation to afford a ca. 3% metal loading on the graphene oxide. The materials were examined electrochemically in acidic media for the water oxidation reaction, where we observed low over potentials for the reaction and high stability during prolonged testing[3]. Now, we are comparing the activity of these metals and other non-precious metals for their water oxidation activity in basic electrolyte. These novel metal nanoparticles stabilized on a graphene could aid in the development of cost efficient and sustainable materials for the future of renewable energy technologies. [1] Kamdar, J.M.; Grotjahn, D.B. Molecules 2019, 24, 494. [2] 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 [3] Fruehwald, H. M.; Moghaddam, R. B.; Zekina, O. V.; Easton, E. B., Cat. Sci. Technol. 2019, 9, 6547-6551
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".