Catalytic convertion of Al-MCM-41-ceramic on hidrocarbon (C8 – C12) liquid fuel synthesis from polypropylene plastic waste
Bibliographic record
Abstract
Hydrocarbon liquid fuel (C8-C12) from poly propylene plastic waste has been successfully synthesized by catalytic conversion using Al-MCM-41 as catalyst. The Al-MCM-41 was characterized using SAXRD. Morphology of Al-MCM-41 surface was analyzed using TEM. The metals content were measured using XPS. The performance of Al-MCM-41 to adsorb and desorb nitrogen was monitored by GSA using BET model. The acidity of Al-MCM-41 was analyzed by FTIR by pyridine absorption method. Al-MCM-41 catalyst was used to convert polypropylene plastic waste in 200 mL catalytic convertion reactor. The Al-MCM-41 catalyst was used for three times and coded as sample A, B, and C. The results showed that sample A, B, and C produce hydrocarbon fraction (C8-C12) liquid fuel with a composition of 92.76; 91.92; and 90.58%, respectively. The repeated use of the catalyst causes a decrease in the composition of the hydrocarbon fraction (C8-C12) due to decrease of catalyst performance as observed by a TON (Turn Over Number) value which increased from 0.075%/gram to 0.156%/gram. Based on physical test i.e. boiling point, flash point, density, viscosity, and calorific value, it was proved that hydrocarbon fuels A, B and C have characteristics that in accordance with standard, premium gasoline, as specified in SNI 06-3506- 1994 and HIBER11Z international standards issued by Hibernia Petroleum Canada in 2016.
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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) | 0.002 | 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".