Qualitative role of heterogeneous catalysts in biodiesel production from Jatropha curcas oil
Bibliographic record
Abstract
Biodiesel properties are in general attributed to the composition and properties of the oil feedstock used, overlooking the possible impacts of the catalyst preparation details. In light of that, the impacts of different catalyst preparation techniques alongside those of different support materials on the yield, composition, and fuel properties of biodiesels produced from the same oil feedstock were investigated. More specifically, tri-metallic (Fe-Co-Ni) catalyst was synthesized through two different techniques (green synthesis and wet impregnation) using MgO or ZnO as support material. The generated catalyst pairs, i.e., Fe-Co-Ni/MgO and Fe-Co-Ni/ZnO prepared by wet impregnation and Fe-Co-Ni-MgO and Fe-Co-Ni-ZnO prepared by green synthesis (using leaf extracts) were used in the transesterification process of Jatropha curcas oil. Detailed morphological properties, composition, thermal stability, crystalline nature, and functional groups characterization of the catalysts were also carried out. Using Box-Behnken Design response surface methodology, it was found that the green-synthesized Fe-Co-Ni-MgO catalyst resulted in the highest biodiesel yield of 97.9%. More importantly, the fatty acid methyl ester (FAME) profiles of the biodiesels produced using the four catalysts as well as their respective fuel properties were different in spite of using the same oil feedstock.
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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".