Step-by-Step Synthesis of Non-Noble Metal Electrocatalysts for O<sub>2</sub> Reduction under Proton Exchange Membrane Fuel Cell Conditions
Bibliographic record
Abstract
Fe-based catalysts for O 2 reduction under proton exchange membrane fuel cell conditions were prepared on a commercial N234 carbon black support using both a “classical” and a step-by-step procedure to determine if parameters other than microporosity and nitrogen loading of the carbon support are important in the synthesis of Fe/N/C electrocatalysts. The “classical” procedure for obtaining Fe/N/C electrocatalysts is to use a single-step synthesis, in which a carbon support loaded with a metal precursor is heat treated at high temperatures (900−950 °C) in pure NH 3 . In the step-by-step procedure, microporosity is first etched into the carbon support followed, if necessary, by the addition of N-bearing functionalities and, last, the loading of the metal precursor. Similar maximum microporous contents can be etched into N234, using either NH 3 or O 2 (air). However, unlike O 2 (air), etching with NH 3 has the added benefit of creating N-bearing functionalities on the carbon surface. For carbon supports etched in O 2 (air), it is possible to add N-bearing functionalities either by N 2 plasma treatment or by a subsequent, short pyrolysis in NH 3 . In the case of the multistep procedure, a second heat treatment is essential for activating the catalytic sites. This demonstrates the importance of a third factor controlling the activity of the catalysts. The duration and temperature of the activation step depend on the ambient gas used. This activation step, during which a C−N x −Fe complex is transformed into a catalytic site, is unapparent in the “classical” procedure. Both “classical” and step-by-step syntheses yield the same maximum catalytic activity when measured using either the rotating disk electrode method or by fuel cell testing. The similarity in microporous specific area of catalysts made using these two synthesis methods explains this finding.
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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) | 0.001 | 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 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".