Innovative Catalysts For Biodiesel Synthesis: Transforming Waste Cooking Oil With Mango And Banana Peel Extracts And Koh
Bibliographic record
Abstract
The global need for energy is increasing steadily.Fossil fuels, namely coal, gas, and crude oil, are the primary energy sources worldwide.Nevertheless, fossil fuels will eventually exhaust, and renewable energy emerges as the most rational substitute, given that fossil fuels contribute to acid rain, the greenhouse effect, and other ecological issues.Renewable biofuels provide the capacity to satisfy the worldwide energy requirement and offer substantial potential.Biodiesel is a significant alternative from biological sources that can replace petroleum.Waste cooking oil was employed as a primary resource for biodiesel production.This study examines the disparities in utilizing three distinct catalysts: a homogeneous catalyst such as KOH and heterogeneous catalysts such as mango and banana peels.Before biodiesel extraction, Mango powder and banana powder were subjected to analysis using FT-IR, SEM, EDS, and XRD techniques.The mango catalyst yielded the most significant amount of synthesized biodiesel (74.97%), followed by the banana catalyst (63.44%) and the KOH catalyst (45.06%).The GC-MS analysis revealed that the biodiesel produced using potassium hydroxide (KOH) exhibited the most significant amounts of all components.This observation supports that potassium hydroxide is a highly efficient and active catalyst, surpassing banana and mngo in catalytic activity.
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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 | 0.000 | 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) | 0.000 | 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".