Green Fuel Innovation: Enhancing Biodiesel Production with MCM-41 Mesoporous Silica Catalysis
Bibliographic record
Abstract
In this study, mesoporous silica nanoparticles (MSNs) with a hexagonal structure and large surface area were synthesized via a sol-gel method. The properties of the synthesized MCM-41 catalyst were characterized using BET, EDX, XRD, and FTIR analyses. The results showed that the MCM-41 had a high surface area of 966 m2/g and large pore volume of about 0.91 cm3/g. Sunflower oil was converted to biodiesel in a batch reactor at different temperatures (40, 50, 60 °C), methanol-to-oil molar ratios (6:1, 9:1, 12:1), catalyst loadings (0.7, 0.9, 1.25 wt%), and reaction times (up to 80 min) using the prepared catalyst under atmospheric pressure. The biodiesel yield was found to reduce when the reaction time exceeded 1 hour despite maintaining the catalyst. The maximum biodiesel yield of 45% was obtained under optimal conditions of a 9:1 methanol-to-oil ratio, 1.25 wt% catalyst loading, 60 °C temperature, and 60 min reaction time. GC-MS analysis characterized the biodiesel composition and properties. The synthesized biodiesel showed improved properties compared to conventional fuels, with linoleic acid methyl ester (C17H34O2, 25.93%) as the main component. The MCM-41 catalyst exhibited remarkable catalytic activity and could be recovered, regenerated, and reused, reducing reaction costs. This makes it a potential alternative to homogeneous catalysts that complicate product separation.
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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".