Fly ash utilization as support of nano zinc oxide composite catalyst for methanolysis of kapok (Ceiba Pentandra) seed oil
Bibliographic record
Abstract
This study focuses on developing a nano zinc oxide (ZnO) catalyst with fly ash (FA) as a support material for converting kapok seed oil (KSO) into biodiesel. This research aims to study the preparation of nano ZnO/FA solid catalysts and the catalyst's reactivity towards kapok seed oil biodiesel (KSOB) products. The catalysts were synthesized using a modification of the Stober process, which is the co-precipitation, impregnation, and precipitation step co-occurred. The catalyst is prepared on base condition using sodium hydroxide with a solvent of methanol and zinc chloride as a raw material. FA waste was effectively modified with zinc oxide particles to create a high-performance ZnO/FA composite catalyst. Under optimal stoichiometric NaOH and 60% ZnO, the resulting material achieved a remarkable specific surface area of 14.8 m²/gram, indicating its potential for enhanced catalytic activity. The prepared catalyst of nano ZnO/FA achieved successful methanolysis of KSO, with a maximum FAME yield of 61.09% attained at 65°C after 5 hours of reaction time, using a 3% catalyst dose and a KSO: methanol molar ratio of 1:15. The initial success of nano ZnO/FA with kapok seed oil paves the way for further development towards robust catalysts specifically tailored for low-grade oil conversion.
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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".