Catalytic Hydrogenation of <i>p</i>-Chloronitrobenzene to <i>p</i>-Chloroaniline Mediated by γ-Mo<sub>2</sub>N
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Promoting the production of industrially important aromatic chloroamines over transition-metal nitrides catalysts has emerged as a prominent theme in catalysis. This contribution provides an insight into the reduction mechanism of p -chloronitrobenzene ( p -CNB) to p -chloroaniline ( p -CAN) over the γ-Mo 2 N(111) surface by means of density functional theory calculations. The adsorption energies of various molecularly adsorbed modes of p -CNB were computed. Our findings display that, p -CNB prefers to be adsorbed over two distinct adsorption sites, namely, Mo-hollow face-centered cubic (fcc) and N-hollow hexagonal close-packed (hcp) sites with adsorption energies of −32.1 and −38.5 kcal/mol, respectively. We establish that the activation of nitro group proceeds through direct pathway along with formation of several reaction intermediates. Most of these intermediaries reside in a significant well-depth in reference to the entrance channel. Central to the constructed mechanism is H-transfer steps from fcc and hcp hollow sites to the NO/–NH groups through modest reaction barriers. Our computed rate constant for the conversion of p -CNB correlates very well with the experimental finding (0.018 versus 0.033 s –1 at ∼500 K). Plotted species profiles via a simplified kinetics model confirms the experimentally reported high selectivity toward the formation of p -CAN at relatively low temperatures. It is hoped that thermokinetics parameters and mechanistic pathways provided herein will afford a molecular level understanding for γ-Mo 2 N-mediated conversion of halogenated nitrobenzenes into their corresponding nitroanilines; a process that entails significant industrial applications.
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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".