Synthesis of Unsupported Pt-based Electrocatalysts and Evaluation of Their Catalytic Activity for the Ethylene Glycol Oxidation Reaction
Bibliographic record
Abstract
In this work, unsupported Pt, Pt-Ru (1:1 wt. % Pt:Ru ratio) and Pt-CeO2 (1:1 wt. % Pt:CeO2 ratio) electrocatalysts were synthesized and evaluated as anodes for the ethylene glycol oxidation reaction (EGOR) in out in H2SO4 electrolyte. The nanomaterials were prepared by slowly dropping the precursors in a NaBH4 solution, in a reduction process of 10 min. Analysis by XRD showed the formation of polycrystalline electrocatalysts, while the chemical composition characterization indicated a ratio between the different elements in the bimetallic materials close to the stoichiometric value. Selected area electron diffraction patterns evaluation carried out in the TEM apparatus helped in the identification of Pt (1 1 1) in the three anodes, Ru (1 0 0) in Pt-Ru, and CeO2 (1 1 1) in Pt-CeO2, confirming the formation of Ru and CeO2 phases. The results from the electrochemical characterization by Linear Scan Voltammetry (LSV) showed that the Pt-Ru material possess a higher mass catalytic activity for the EGOR, followed Pt-CeO2, compared to Pt-alone. The nano-sized Pt-Ru and Pt-CeO2 anodes demonstrated a high electrochemical stability in accelerated potential cycling tests, with very low surface area losses in the hydrogen adsorption/desorption region after 500 polarization cycles. The results indicated that the bimetallic electrocatalysts are candidate anodes for Direct Ethylene Glycol Fuel Cells.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".