Density functional theory study on direct dehydrogenation of propane catalyzed by N-, O-, and P-doped graphene catalysts
Bibliographic record
Abstract
The density functional theory was used to calculate the reaction mechanism and selectivity of nonmetallic single-atom catalysts, such as N, O, and P, doped on graphene in the direct dehydrogenation of propane (PDH) . Our results show that the rate-controlling step in PDH varies with the doping atom. We also found that N, O, and P nonmetallic single-atom-doped graphene catalysts showed relatively low adsorption performance for propane and the active site was the C atom adjacent to N, O, and P, rather than the doped atom itself. Interestingly, for the O-doped graphene catalysts, which can reduce the reaction energy barrier by searching for multiple transition states, and the more transition states in the reaction path, the lower the energy barrier for the reaction rate-controlling step. Finally, the results show that the energy barrier of P-doped propane direct dehydrogenation reflecting the speed control step is the lowest, which is 44.32 kcal·mol−1, and the energy barrier of deep dehydrogenation is 53.08 kcal·mol−1; so it has good selectivity. Therefore, the P-doped graphene catalyst has a promising application as a nonmetallic catalyst for the direct PDH, which provides the possibility for the design of cheap and environmentally friendly catalysts.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".