Biodiesel Production from Jatropha curcas Oil Using Potassium Carbonate as an Unsupported CatalystThe authors wish to express their gratitude to Mary Jane Walzak, Senior Research Scientist of Surface Science Western, University of Western Ontario, London, Ontario, Canada for analyzing the results using FTIR-ATR. The research is funded by an Individual Discovery Grant awarded to Dr. E.K. Yanful by the Natural Sciences and Engineering Research Council of Canada (NSERC).
Bibliographic record
Abstract
Jatropha curcas (JTC) oil, an inedible vegetable oil, can be a substitute feedstock for traditional food crops in the production of environment friendly and renewable fuel (biodiesel). In the present study, unsupported potassium carbonate was used as a catalyst to provide an understanding of the catalytic activity in the transesterification reaction. Researching the potential and the behavior of potassium carbonate is very important because every biomass ash contains this compound in a significant amount. It can be extracted by using classical extraction or leaching technologies. During the biodiesel production reaction, the formation of soap as a byproduct was also monitored using the FTIR-ATR method. From this study it was observed that the transesterification of JTC oil to JTC biodiesel appeared to be complete within 15 minutes when a 5 wt% (based on the wt. of the oil) potassium carbonate, 6:1 methanol to oil molar ratio, 60°C or a 4 wt% potassium carbonate, 9:1 methanol to oil molar ratio and 60°C reaction temperature were used.
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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".