Improving Platinum Catalyst Durability with a Doped Graphene Support
Bibliographic record
Abstract
Improving the durability of a platinum catalyst is an important step in increasing its utility when incorporated as the anode or cathode of a proton-exchange membrane fuel cell. Using density functional theory, the binding energy between a platinum atom and five graphene surfaces, one pure, and four others singly doped with beryllium, boron, nitrogen, and oxygen, was calculated. The oxygen-doped surface showed the highest binding energy and was calculated to be 7 times higher than the undoped surface. Each dopant modified the surface bonding arrangement within the graphene lattice, which then affected how the surface bonded to the platinum atom. Using molecular orbitals, natural bond orbitals, and the gradient of the electron density, these interactions were explored to explain the strength of the Pt–surface bond, which, in ascending order by dopant, was found to be undoped, nitrogen, boron, beryllium, and oxygen.
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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 | 0.000 | 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".