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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".