Upgrading Bio-oil through Emulsification with Biodiesel: Thermal Stability
Bibliographic record
Abstract
Storage properties and thermal stability of fuels are important background information when dealing with a new potential fuel. Following the first paper on the preparation and characterization of the fuel mixture produced through emulsification of bio-oil and biodiesel, the second part of this investigation reports on the storage and thermal stability of the bio-oil/biodiesel mixture. The physicochemical properties of the samples stored at different temperatures (60 and 80 °C) for up to 180 h are measured. Fuel properties, such as viscosity, water content, acid number, and average molecular weight of the bio-oil/biodiesel mixture, are measured before and after aging. In contrast to the aging properties of bio-oil alone, very little changes in water content and viscosity are shown for the mixtures aged at 80 °C for 180 h. Overall, a slight decrease in acid numbers is observed for the aged mixtures. Chemical changes are characterized using gel permeation chromatography (GPC), showing a slight increase in the molecular weight over time, possibly because of some polymerization and condensation reactions during storage. Further confirmation of the changes is shown through a Fourier transform infrared spectrometer (FTIR), thermal decomposition analysis using a thermogravimetric analyzer (TGA), and proton assignment using proton nuclear magnetic residence ( 1 H NMR) spectroscopy. Finally, the study indicates that the bio-oil/biodiesel mixture is stable within the conditions tested as a fuel.
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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 | 0.001 |
| 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".