Synthesis of solketalacetin as a green fuel additive via ketalization of monoacetin with acetone using silica benzyl sulfonic acid as catalyst
Bibliographic record
Abstract
Silica benzyl sulfonic acid (SBSA) was prepared as a catalyst for reacting monoacetin with acetone to synthesize solketalacetin as a green fuel additive. To synthesize SBSA, commercially available silica gel was functionalized with benzyl alcohol in the presence of sulfuric acid as catalyst and was then sulfonated with chlorosulfonic acid. The catalyst was characterized by FT-IR, XRD, and TGA. The catalytic activity of SBSA was compared with those of Amberlyst 36 and Purolite PD 206 as two sulfonated acidic catalysts, in a continuous flow system. The effect of different operation conditions such as acetone to monoacetin molar ratio, reaction temperature, and feed flow rate were investigated. Increasing acetone to monoacetin molar ratio increased the solketalacetin yield for the three catalysts but SBSA demonstrated the highest solketalacetin yield. Solketalacetin yield was reduced with temperature increase for all the catalysts and the maximum solketalacetin yields were recorded with Amberlyst 36 and SBSA catalyst at 20 °C and 40 °C, respectively. The catalytic activity was examined by keeping the catalysts on–stream for 25 h while the reusability tests were performed in four consecutive runs and showed that SBSA was stable up to 25 h and had the highest stability in 4 runs.
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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".