Oxygen Reduction Catalysts for Polymer Electrolyte Fuel Cells from the Pyrolysis of Iron Acetate Adsorbed on Various Carbon Supports
Bibliographic record
Abstract
Nonnoble metal catalysts for the electrochemical reduction of oxygen in acidic medium have been produced by adsorbing iron(II) acetate on 19 carbon supports. These materials were then pyrolyzed in an atmosphere containing NH 3 . The 19 carbon supports are (i) six as-received commercial supports (Printex XE-2, Norit SX Ultra, Ketjenblack EC-600JD, acetylene black, Vulcan XC-72R, and Black Pearls 2000), (ii) three as-received developmental supports (Lonza HS300 and Sid Richardson RC1 and RC2), (iii) the same nine previous supports prepyrolyzed at 900 °C in an atmosphere containing NH 3 to increase their N content, and (iv) a synthetic carbon made by pyrolyzing perylene tetracarboxylic dianhydride at 900 °C in an atmosphere containing NH 3. The goal of this study is to determine the effect of the carbon support on the catalytic activity of the catalysts. The specific surface area, the pore size distribution, the N and O contents, and the electrocatalytic activities of the 19 types of catalysts were measured. It was found that the activity of the catalysts varies greatly from one carbon support to another, but neither the specific surface area of the catalysts nor the distribution of their macro- or mesopores is a determining factor for the catalytic activity. The most important factor is the N content of the materials; the higher it is, the higher is the density of the catalytic sites on their surface and the better is the electrocatalyst. Carbon supports that are devoid of N, however, display some lower catalytic activity, which is attributed to an iron oxide. The latter catalytic site occurs also in the other N-containing catalysts. In these materials there are, therefore, three catalytic sites at work: an iron oxide site and two N-containing sites labeled FeN 4 /C and FeN 2 /C, with the last site being the most active for oxygen electroreduction.
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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 | 0.001 | 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".