A density functional theory study of catalytic sites for oxygen reduction in Fe/N/C catalysts used in H2/O2 fuel cells
Bibliographic record
Abstract
The oxygen reduction catalytic activity of carbon-supported FeN4 moieties bridging micropores between two graphene sheets was investigated by density functional theory (DFT). Based on the FeN(2+2)/C structure proposed earlier by our group, two types of FeN(2+2)/C structures were considered: one mostly planar and one in which the Fe ion is significantly displaced out of the graphitic plane. A structure in which the FeN4 moiety is embedded in an extended graphene sheet (FeN/C) was also considered. In addition, we have investigated the influence of an axial pyridine group approaching the Fe centre. The formation energy is lowest for the planar FeN(2+2)/C structure. The overall downhill behaviour of the relative free energy vs. the reaction step suggests that most structures have catalytic activity near zero potential. This conclusion is further supported by calculations of the binding energies of adsorbed O2 and H2O and of the O-O bond lengths of adsorbed O2 and OOH. The side-on interaction of adsorbed O2 is preferred over the end-on interaction for the three basic structures without the axial pyridine. The pyridine coordination produces a stronger binding of O2 for the planar FeN(2+2)/C and the FeN/C structures as well as a dominant end-on interaction of O2. The energy levels of the planar FeN(2+2)/C structure with and without the pyridine ligand are nearly equal for iron spin states S = 1 and S = 2, suggesting that both configurations are formed with similar concentration during the preparation process, as also previously found for two of the iron sites by Mössbauer spectroscopy experiments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".