AQUEOUS SOLUBILITY, DISPERSIBILITY AND TOXICITY OF BIODIESELS
Bibliographic record
Abstract
ABSTRACT With increasing interest in the use of plant and animal oils as potential petroleum fuel replacements, there is the potential for accidental release of these biodiesels to the environment. While the behaviours and effects of petroleum diesels have been extensively studied, little is known about either the fate of biofuels in water or their potential effects on aquatic ecosystems. The most important mechanism for exposure of aquatic ecosystems to biodiesels and petroleum diesels is the transfer of material from the non-aqueous phase liquid (NAPL) into the aqueous phase, as both soluble and dispersed components. The equilibrium levels of fuel components in the water, the water-accommodated fractions (WAF) have been measured in fresh water for biodiesels from feedstocks of soy oil, canola oil, waste fry tallow and fish wastes. Biodiesel and petroleum diesels blends of 5% (B5) and 20% (B20) are also measured. Aqueous toxicities are also reported for pure biodiesels and petroleum diesel. Acute toxicities were assessed by 96-hour LC5os of Daphnia magna and rainbow trout and by IC5os of bioluminescent bacteria. The correlations between acute toxicity, WAF concentrations and fuel property data are examined. Natural and chemically-enhanced dispersion of biodiesel is examined in both low- and high-energy conditions. Biodiesels are found to have significant differences with petroleum diesels in water chemistries and in potential ecological impacts. All organisms tested show that biodiesels have less acute toxicity than petroleum diesels. Biodiesels and biodiesel-rich blends were found to be very much more dispersible in high-energy conditions than petroleum diesel.
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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) | 0.001 | 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".