Activity performance and kinetics for glycerol carbonylation with urea over <scp>Zn‐Co</scp> mixed metal oxide catalyst
Bibliographic record
Abstract
Abstract Efficient carbonylation of glycerol using urea with Zn‐Co mixed metal oxide (MMO) catalyst has been achieved. Various methods of catalyst preparation were explored for glycerol carbonate (GC) synthesis. The optimized method of catalyst preparation was found to be co‐precipitation (CP) with a Zn:Co ratio of 70:30, achieving 81% glycerol conversion with 97% GC selectivity. X‐ray diffraction (XRD) studies revealed the formation of ZnO, Co 3 O 4 , and spinel ZnCo 2 O 4 phases. Thermal treatment given to the catalyst allows insertion of Zn cations into Co 3 O 4 lattice forming ZnCo 2 O 4 phase which was also evidenced in X‐ray photoelectron spectroscopy (XPS) and Raman spectroscopy. Herein, for the first time, reaction kinetics was studied to propose the rate equation, based on which a plausible reaction pathway is proposed involving two‐site adsorption of glycerol (basic site) and urea (acidic site), which undergo carbonylation followed by cyclization into GC. A recycle study and hot filtration test have proven the reusability of the catalyst.
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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".