Effect of sulphation on the catalytic activity of MIL‐101 and the activation energy of an esterification reaction
Bibliographic record
Abstract
ABSTRACT Metal organic frameworks have been recently highlighted as promising materials for heterogeneous catalysts. In this study, MIL‐101, a chromium‐based metal organic framework was synthesized using the solvothermal method and functionalized through sulphation. The catalytic activities of MIL‐101 and sulphated MIL‐101 (MIL‐101s) were tested in the esterification of acetic acid. The conversion of acetic acid increased from 73.7 to 86.2 % after sulphation at 363 K. These results prove that the sulphation of MIL‐101 is an effective way to enhance the acidity and catalytic activity of MIL‐101. The characterization of MIL‐101 and MIL‐101s was performed using FTIR, BET, XRD, TG‐DTA, SEM/EDX, XPS, and potentiometric titration methods. A BET analysis showed that MIL‐101 maintained its porosity after sulphation. The sulphation reduced the surface area and pore volume but increased the acidity.
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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) | 0.002 | 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".