A Methodology for Investigating New Nonprecious Metal Catalysts for PEM Fuel Cells
Bibliographic record
Abstract
This paper reports an approach to investigate metal-chalcogen materials as catalysts for the oxygen reduction reaction (ORR) in proton exchange membrane (PEM) fuel cells. The methodology is illustrated with reference to Co-Se thin films prepared by magnetron sputtering onto a glassy-carbon substrate. Scanning Auger microscopy (SAM), X-ray photoelectron spectroscopy (XPS), energy-dispersive X-ray spectroscopy (EDX), and X-ray diffraction (XRD) have been used, in parallel with electrochemical activity and stability measurements, to assess how the electrochemical performance relates to chemical composition. It is shown that Co-Se thin films with varying Se are active for oxygen reduction, although the open circuit potential (OCP) is lower than for Pt. A kinetically controlled process is observed in the potential range 0.5-0.7 V (vs reversible hydrogen electrode) for the thin-film catalysts studied. An initial exposure of the thin-film samples to an acid environment served as a pretreatment, which modified surface composition prior to activity measurements with the rotating disk electrode (RDE) method. Based on the SAM characterization before and after electrochemical tests, all surfaces demonstrating activity are dominated by chalcogen. XRD shows that the thin films have nanocrystalline character that is based on a Co(1-x)Se phase. Parallel studies on Co-Se powder supported on XC72R carbon show comparable OCP, Tafel region, and structural phase as for the thin-film model catalysts. A comparison for ORR activity has also been made between this Co-Se powder and a commercial Pt catalyst.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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 teacher head, 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".