Electrocatalytic Activity of Nanoporous Pt–Ir Materials toward Methanol Oxidation and Oxygen Reduction
Bibliographic record
Abstract
Nanoporous bimetallic electrocatalysts with different contents of iridium (Ir%: 15, 28, 40, and 50) were prepared using a one-step facile hydrothermal method. Formaldehyde was used as the reduction agent to simultaneously reduce and , resulting in the formation of bimetallic nanoporous structures. Scanning electron microscopy and energy dispersive X-ray spectroscopy were employed to characterize the surface morphology and composition of the as-synthesized samples. A number of electrochemical methods were used to study the electrochemical activity of the different nanoporous electrodes toward methanol oxidation and oxygen reduction. Our electrochemical studies show that the synthesized nanoporous electrodes possess extraordinarily high electroactive surface areas and that the presence of Ir significantly improves the electrocatalytic activity of Pt toward the electrochemical oxidation of methanol and the electrochemical reduction of oxygen. Of the synthesized nanoporous electrodes, the electrode exhibits the highest electrocatalytic activity. The steady-state current density of the nanoporous electrode for methanol oxidation at is 345 times higher than that of a polycrystalline Pt electrode and over four times higher than that of a nanoporous Pt electrode
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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 | 0.000 |
| 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".