Thermo‐physical properties of bio‐oil and its fractions: Measurement and analysis
Bibliographic record
Abstract
Thermo‐physical properties including densities and viscosities of bio‐oil itself and its two fractions (aqueous fraction and organic fraction) are important for practical utilization. In this study, the bio‐oil fractions were prepared in various weight ratios of 2:1, 1:1, and 1:2. Density and viscosity of a bio‐oil itself, and its aqueous and organic fractions were accurately measured at various temperatures varying from ambient to 343 K and pressures from ambient to 10 MPa. Increasing temperature decreased the density of bio‐oil itself and the two separated phases (aqueous and organic phases) by almost 2.5 %, and 0.3 to 0.4 %, respectively. Increasing the temperature also decreased the viscosity of the bio‐oil by almost 86 % and its aqueous and organic fractions by up to 68 % and 98 %, respectively. The density of the bio‐oil and its organic and aqueous fractions was enhanced by increasing the pressure, whereas increasing the pressure increased the viscosity of bio‐oil and the organic fraction, but not the viscosity of aqueous phase. The generated experimental density and viscosity data of bio‐oil and its aqueous and organic fractions were evaluated with existing models that account for the effects of temperature and pressure. The impacts of pressure and temperature on the density and viscosity were considered in the correlations and evaluated with the experimental results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".