Selectivity engineering in the O‐ versus C‐alkylation of <i>p</i>‐cresol with cyclohexene over sulfated zirconia
Bibliographic record
Abstract
Abstract Solid acids are more widely used as heterogeneous catalysts, because they are eco‐friendly. This paper reports the results for the Friedel‐Crafts alkylation of p‐cresol with cyclohexene using solid acids sulfated zirconia, 20% w/w dodecatungstophosphoric acid (DTP) supported on K10 clay and ZnCl2/K10 (Clayzic). This reaction gave substantial amount of 1‐cyclohexyloxy‐4‐methyl benzene (O‐alkylated product) and 4‐cyclo‐hexyl‐4‐methyl phenol (C‐alkylated product). Both products are of commercial importance as perfume and insecticide respectively. Sulfated zirconia catalyst was shown to be better than others studied in terms of activity and selectivity to the O‐alkylated product. The kinetics were studied with sulfated zirconia as catalyst where the rate determining step was the surface reaction between chemisorbed cyclohexene and p‐cresol from the liquid phase within pores according to the Eley‐Rideal mechanism. The production of O‐alkylated p‐cresol is favoured at lower temperatures and C‐alkylated product at higher temperatures. The best operating temperature is 353 K. The activation energies for O‐ and C‐alkylation are 72.68 and 118.28 kj/mol, respectively.
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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".