Weakening CO poisoning over size‐ and support‐dependent Pt <sub> <i>n</i> </sub> /X‐graphene catalyst (X = C, B, N, <i>n</i> = 1–6, 13)
Bibliographic record
Abstract
CO poisoning is one of the obstacles for platinum catalysts toward the electro‐catalysis process for proton exchange membrane fuel cell (PEMFC) or direct methanol fuel cell (DMFC). Herein, we aim to weaken the CO poisoning on Pt by varying the cluster sizes and supports via doping graphene with B and N based on DFT + D3 calculations. Energetically, the most favorable Pt n /X‐graphene (X = C, B, N; n = 1–6, 13) structures are obtained, and the calculated binding energies between Pt n and X‐graphene are size‐ and support‐dependent on a sequence: Pt n /B‐g > Pt n /N‐g > Pt n /C‐g. The low‐coordinated and protruded Pt atoms are identified as the active sites. The medium‐sized clusters ( n = 4–6) display CO poisoning‐free properties with an excellent CO oxidation performance, resulting from the moderate locations of d‐band center and electronic transfer via the interface. Furthermore, E‐R mechanism is revealed to dominate the reaction route with a rate‐limiting step of the second CO 2 desorption. The corresponding activation energy barriers are 0.53, 0.61 and 0.56 eV for Pt n /B‐g ( n = 4, 5, 6), respectively. This work provides insights into the theoretical design of CO poisoning‐free catalyst Pt n /X‐g in the applications of DMFC/PEMFC.
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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 | 0.000 | 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".