Kinetic study on alkylation of hydroquinone with methanol over <scp> SO <sub>3</sub> H </scp> functionalized <scp>Brønsted</scp> acidic ionic liquids
Bibliographic record
Abstract
Abstract O ‐alkylation of a dihydric phenol (i.e., hydroquinone) with methanol in presence of benzoquinone catalyzed by double SO 3 H functionalized Brønsted acidic ionic liquids (i.e., 1,3‐disulphonic acid imidazolium hydrogen sulphate, 1,3‐disulphonic acid benzimidazolium hydrogen sulphate, and sulphuric acid) is studied in a batch reactor. The sensitivity of activity and selectivity with reaction time, temperature, speed of agitation, and catalyst loading was examined. The plausible reaction pathways proposed based on the experimental observations and detailed kinetic investigation are performed by assuming a homogeneous reaction phase. The kinetic parameters, such as pre‐exponential factor and activation energy, are estimated for both ionic liquids and sulphuric acid by considering all competitive reactions, and comparative results were presented. An extended form of the Arrhenius equation is used to estimate the kinetic parameters for the reaction which showed curvature in against a plot. The model prediction with the estimated kinetic parameters is in good agreement with the experimental data, which confirmed the model validity in the experimental operating range. It was found that ionic liquid has a potential application in the synthesis of a selective monoalkylated product of hydroquinone. The kinetic analysis performed is found to be useful in the understanding of process behaviour.
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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".