Synthesis of acidic modified <scp>UiO</scp> ‐66 by concentrated sulphation form and its application in the production of fatty acids ethyl esters
Bibliographic record
Abstract
Abstract In this article, UiO‐66 metal–organic frameworks containing structural modifications were synthesized and characterized to increase acidic surface behaviour for application in heterogeneous acid catalysis to produce fatty acid ethyl esters (FAEE) from degummed soybean oil. The solvothermal route synthesized UiO‐66, and the post‐synthesis acid modifications were done by sulphation reaction in strong acidic medium. The synthesized materials were characterized by physicochemical techniques such as X‐ray diffraction (XRD), C‐ray fluorescence (XRF), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), Fourier transform infrared spectrometer (FTIR), and N 2 physisorption, observing a strong structural modification of the original UiO‐66, but no signal of sulphated ZrO 2 was presented. However, the acidic characteristics on the material's surface were increased. A central compound centred on face 2 3 experiments design was carried out to evaluate three essential reaction variables: temperature, time, and alcohol/oil ratio, obtaining an optimized fit for the UiO‐66‐S35 of temperature at 140°C, time of 3 h, and molar ratio of 16. The optimized statistical adjustment was applied in the reuse cycles assays in three catalyst cycles, reaching the maximum yield of 95% in FAEE, keeping the catalyst active at the end of 3 cycles.
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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".