Enhanced recovery and recycling of catalyst by post‐impregnation of γ‐Al<sub>2</sub>O<sub>3</sub> with 12‐tungstophosphoric acid for esterification reaction
Bibliographic record
Abstract
12‐tungstophosphoric acid, polyoxometalate with Keggin structure, and γ‐Al2O3 were synthesized. Loading of 12‐tungstophosphoric acid (HPW) on γ‐Al2O3 was performed by the post‐impregnation method. The catalysts were characterized by XRD, SEM, FTIR, BET, and DRS techniques. According to the pertinent observation, using the post‐impregnation method promises the stability of polyoxometalate Keggin structure after loading on γ‐Al2O3 support. The stability of 20 %HPW/Al2O3 was also confirmed by a leaching test. Textural characterization demonstrates that HPW on the γ‐Al2O3 had a much larger surface area as compared with the pure HPW. The 20 %HPW/Al2O3 exhibited roughly high activity toward an esterification reaction. The influence of various reaction parameters (% loading HPW on support, reaction temperature, reaction time, catalyst weight, and acid/alcohol molar ratio) on the catalytic performance was studied. The efficiency of catalytic activity for six cycles remains approximately constant, which confirms the enhanced recovery and recycling of the catalyst. The kinetics of the esterification reaction and the kinetic parameters such as rate constants and activation energy were determined (25.37 kJ/mol).
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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".