A density functional theory study on the performance of graphene and N-doped graphene supported Au<sub>3</sub> cluster catalyst for acetylene hydrochlorination
Bibliographic record
Abstract
Density functional theory (DFT) calculation was used to investigate the mechanism of Au3 clusters, separately supported on pure graphene (Au3/graphene) and one graphitic N-doped graphene (Au3/N-graphene). These supported Au3 clusters were used to catalyze acetylene hydrochlorination. Results show that the graphene supporter could obviously enhance the adsorption of reactants. Also, N-atom doping could broaden the energy gap between the HOMO of graphene and the LUMO of Au3, leading to the significantly attenuated interaction between the Au3 cluster and graphene by more than 19 kcal/mol (1 cal = 4.184 J). The two catalysts possessed extremely similar reaction mechanisms with activation energy values of 23.26 and 23.89 kcal/mol, respectively. The calculated activation barrier declined in the order of Au3 < Au3/N-graphene < Au3/graphene, suggesting that Au3/N-graphene could be a potential catalyst for acetylene hydrochlorination.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".