Regioselective nitration of toluene to <scp><i>para</i></scp>‐nitrotoluene with NO<sub>2</sub> over dealumination Hβ zeolite: Comparison of organic and inorganic acids
Bibliographic record
Abstract
Abstract This work presents a novel heterogeneous catalytic system for the preparation of para‐nitrotoluene (p‐NT) with nitrogen dioxide (NO2) as a nitrating agent in the presence of oxygen (O2). By applying dealuminated Hβ zeolite treated with oxalic acid (Hβ‐Ox) as a catalyst, p‐NT can be obtained with high conversion (90.5%) and selectivity (72.6%). In addition, it can be recycled at least five times without a significant decrease in catalytic activity. This result demonstrates that the catalyst has high efficiency, good stability, and regenerability. Meanwhile, the characteristics of pristine Hβ and different dealuminated Hβ zeolites modified with organic and inorganic acids were systematic contrastive studied using X‐ray diffraction (XRD), nitrogen (N2) adsorption‐desorption, Fourier‐transform infrared (FT‐IR), scanning electron microscope (SEM), temperature‐programed desorption of ammonia (NH3‐TPD), pyridine adsorption infrared (Py‐FT‐IR), and solid state 27Al nuclear magnetic resonance (27Al MAS‐NMR). Finally, the higher conversion and para‐selectivity over Hβ‐Ox also give a reasonable explanation in this paper.
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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".