Selective hydrogenation of halogenated arenes using porous manganese oxide (OMS-2) and platinum supported OMS-2 catalysts
Bibliographic record
Abstract
Porous manganese oxide (OMS-2) and platinum supported on OMS-2 catalysts have been shown to facilitate the hydrogenation of the nitro group in chloronitrobenzene to give chloroaniline with no dehalogenation. Complete conversion was obtained within 2 h at 25 °C and, although the rate of reaction increased with increasing temperature up to 100 °C, the selectivity to chloroaniline remained at 99.0%. Use of Pd/OMS-2 or Pt/Al2O3 resulted in significant dechlorination even at 25 °C and 2 bar hydrogen pressure giving a selectivity to chloroaniline of 34.5% and 77.8%, respectively, at complete conversion. This demonstrates the potential of using platinum group metal free catalysts for the selective hydrogenation of halogenated aromatics. Two pathways were observed for the analogous nitrobenzene hydrogenation depending on the catalyst used. The hydrogenation of nitrobenzene was found to follow a direct pathway to aniline and nitrosobenzene over Pd/OMS-2 in contrast to the OMS and Pt/OMS-2 catalysts which resulted in formation of nitrosobenzene, azoxybenzene and azobenzene/hydrazobenzene intermediates before complete conversion to aniline. These results indicate that for Pt/OMS-2 the hydrogenation proceeds predominantly over the support with the metal acting to dissociate hydrogen. In the case of Pd/OMS-2 both the hydrogenation and hydrogen adsorption occur on the metal sites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".