Hydrothermal Conversion of Cattle Manure to Biooil: Biooil Definitions
Bibliographic record
Abstract
A number of researchers have reported that biooil was produced through hydrothermal conversion of different types of biomass. However, it is difficult to evaluate and compare these biooils in terms of yields and chemical properties. They applied different organic solvents to extract biooil from products after hydrothermal conversion of biomass. The purpose of this study is to assess the impact of extraction solvents on the quantity and chemical structure of biooil. Cattle manure was used as one type of biomass feedstock for biooil production. And dichloromethane (CH2Cl2), chloroform (CHCl3) and diethyl ether (C4H10O) were used for biooil extraction. Results showed that extraction solvents influenced biooil yields. The highest biooil yield of 48.78 wt% of volatile content of cattle manure was obtained when using CH2Cl2 solvent. The main components of biooil extracted by CH2Cl2 and CHCl3 were ketones and carboxylic acids, while those extracted by C4H10O were aromatic chemicals. In terms of elemental compositions and high heating values of biooil, no statistically apparent differences were caused by different solvents. The mean elemental compositions (by weight) of biooils were carbon of 73.79%, hydrogen of 8.18%, nitrogen of 4.38% and oxygen of 13.65%. And the mean high heating value of biooil was 36.74 MJ/kg.
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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 | 0.001 |
| 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) | 0.001 | 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".