Characterisation of the mixture product of ether‐soluble fraction of bio‐oil (ES) and bio‐diesel
Bibliographic record
Abstract
Abstract A method of upgrading the properties of bio‐oil with bio‐diesel has been taken in this article. Firstly, the unpopular pyrolytic lignin fraction is extracted from bio‐oil using ether, the rest ether‐soluble fraction of bio‐oil, named ES is mixed with bio‐diesel according to emulsification. The optimal conditions for obtaining a stable ES/bio‐diesel mixture are with octanol surfactant dosage of 3% by volume; initial ES to bio‐diesel ratio of 4:6 by volume; stirring intensity of 1200 rpm; mixing time of 15 min and mixing temperature at 30°C. Additionally, selected fuel properties such as viscosity, water content and acid number are measured for characterising the ES/bio‐diesel mixture. Thermogravimetric analysis (TGA) has been used to further evaluate the thermal properties. Data from the TGA and Fourier transform infrared spectroscopy (FTIR) analyses confirm the presence or absence of certain group of chemical compounds in the mixture. Proton and carbon atoms assignments are further confirmed by 1H NMR (nuclear magnetic resonance) and 13C NMR analysis, respectively. © 2011 Canadian Society for Chemical Engineering
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
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".