Study on the Synthesis of Ni–Al<sub>2</sub>O<sub>3</sub> Catalysts via Topological Transformation and Their Structure–Activity Relationship
Bibliographic record
Abstract
Nitrohydrogenation is a common process in industry. Here, a highly efficient Ni–Al 2 O 3 catalyst derived from Ni–Al layered double hydroxide synthesized in a rotating packed bed (RPB) was first applied in the hydrogenation of 3,4-dichloronitrobenzene (3,4-DCNB) to 3,4-dichloroaniline (3,4-DCA). Leveraging the enhanced mass transfer and micromixing performances by applying RPB, the phenomenon of particle agglomeration and uneven size distribution occurring during catalyst synthesis has been effectively mitigated. The effects of Ni mass fraction, reduction temperature, and time on the physical properties and catalytic performance of the catalyst were investigated. Results showed that the Ni–Al 2 O 3 catalysts prepared under optimal conditions (Ni mass fraction of 78 wt %, reduction temperature of 500 °C, reduction time of 4 h) have small Ni particle sizes and high dispersion. When applied to 3,4-DCNB hydrogenation under optimized conditions (65 °C, 1.5 MPa of H 2, 60 min, catalyst loading 12.55 mg/g), both conversion and 3,4-DCA selectivity approached 100%. Furthermore, the kinetic study on the 3,4-DCNB hydrogenation revealed that the reaction has an activation energy of 39.91 kJ/mol and a pre-exponential factor of 2.54 × 10 4 min –1 . This finding presents an innovative approach for preparing a highly efficient Ni-based catalyst for the hydrogenation of 3,4-DCNB to 3,4-DCA.
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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".