Effect of Fe/Zn ratio in composite catalyst for synthesizing polyoxymethylene dimethyl ethers
Bibliographic record
Abstract
Abstract Polyoxymethylene dimethyl ethers (PODEn) are extremely effective diesel additives to reduce soot formation during combustion. We introduce a series of Fe‐Zn composite solid acid catalysts (SO42−/xFe2O3‐yZnO), for the condensation reaction of methanol and paraformaldehyde (PF) with a cheap and feasible route to efficiently synthesize PODEn. These catalysts were characterized by different characterization techniques, namely BET, XRD, SEM, EDS, FTIR, and NH3‐TPD and the results showed that Fe/Zn molar ratios strongly influenced the physicochemical characteristics of catalysts, thus affecting the methanol conversion and PODE1‐6 and PODE3‐6 selectivity. Accordingly, the methanol conversion was decreased and the selectivity of PODE3‐6 was increased after increasing the Zn molar content. Comparatively, SO42−/Fe2O3‐2ZnO 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 CH2O to methanol, achieving a high selectivity of 99.09% PODE1‐6 and 28.23% PODE3‐6 with 55.16% methanol conversion during the reaction.
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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".