Study of IrxMny(CO)n(DMF)z as Electrocatalyst for Oxygen Reduction and Hydrogen Oxidation in the Presence of Fuel Cell Contaminants
Bibliographic record
Abstract
The oxygen reduction reaction (ORR) and the hydrogen oxidation reaction (HOR) have been studied on a wide range of elec-trocatalysts, including bimetallic materials which are based solely on platinum group metals and their alloys. This work reports the synthe-sis and characterization of a novel bimetallic electrocatalyst, IrxMny(CO)n(DMF)z, for the ORR and HOR in acid media. The material was synthesized by reacting Ir4(CO)12 and MnCl2·4H2O in DMF. It was characterized structurally by FT-IR and micro-Raman spectroscopy, X-ray diffraction, SEM and energy-dispersive X-ray spectroscopy; the electrochemical characterization was made by the rotating disk elec-trode technique, at room temperature. The electrocatalytic activity of the new material for the ORR and HOR does not show appreciable variations due to the presence of methanol or carbon monoxide, respectively, even at high concentrations of these contaminants (2 mol L-1 methanol and 0.5% CO). This tolerance is a very important property with respect to platinum-based catalysts, which are poisoned by low concentrations of such contaminants. The kinetic parameters of the novel catalyst, such as Tafel slope (b), exchange current density (jo) and charge transfer coefficient (α), are reported as well. The results show that the novel electrocatalyst is attractive for evaluation as cath-ode/anode in PEM 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 | 0.000 |
| 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".