Effect of Fe/Zn ratio in composite catalyst for synthesizing polyoxymethylene dimethyl ethers
Bibliographic record
Abstract
Abstract Polyoxymethylene dimethyl ethers (PODE n ) are extremely effective diesel additives to reduce soot formation during combustion. We introduce a series of Fe‐Zn composite solid acid catalysts (SO 4 2− /xFe 2 O 3 ‐yZnO), for the condensation reaction of methanol and paraformaldehyde (PF) with a cheap and feasible route to efficiently synthesize PODE n . These catalysts were characterized by different characterization techniques, namely BET, XRD, SEM, EDS, FTIR, and NH 3 ‐TPD and the results showed that Fe/Zn molar ratios strongly influenced the physicochemical characteristics of catalysts, thus affecting the methanol conversion and PODE 1‐6 and PODE 3‐6 selectivity. Accordingly, the methanol conversion was decreased and the selectivity of PODE 3‐6 was increased after increasing the Zn molar content. Comparatively, SO 4 2− /Fe 2 O 3 ‐2ZnO exhibited superior catalytic activity among the various investigated catalysts due to the high acid density of strong acid sites. The optimal reaction conditions were observed to be at a 3.0 wt% catalyst loading (catalyst/reactant mass ratio), 2.5 hours ours of reaction time, a reaction temperature of 403 K, and a molar ratio of 3:1 of CH 2 O to methanol, achieving a high selectivity of 99.09% PODE 1‐6 and 28.23% PODE 3‐6 with 55.16% methanol conversion during the reaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".