CO2-Neutral Fuels and Lubricants Based on Second Generation Oils such as Jatropha
Bibliographic record
Abstract
For the production of raw materials, the generation of energy and for their mobility, the industrialized countries strongly depend on the import of fossil resources. Second generation resources, such as Jatropha, grow on arid soils and produce non-edible oils. Thus, their cultivation does not compete with food production for farmland. Oils of such plants are therefore suggested as sustainable, CO2-neutral and regenerative alternatives to fossil fuels and lubricants. The present work establishes one of the first functional profiles of second generation oils with properties relevant for the use as fuel or lubricants in order to validate the potential for substituting fossil resources. It concentrates on the characterization of oil gained from the Jatropha Curcas plant to be used as fuel and lubricant. Properties were determined such as pour point, flash point, lubricity of diesel-like fuel in high frequency reciprocating rig, high-temperature/high-shear viscosity, and viscosities as function of temperature, extreme pressure behavior, oxidation resistance as well as its toxicity and bio-degradability. These properties are compared to those of currently used plant oils and fossil oils. The fatty acid chain length distribution of Jatropha oil was determined and found to be close to that of palm oil. This qualifies Jatropha oil as a substitute, thus releasing the pressure on the prices of food based oils. First tribological characterizations were carried out and are presented here, showing impressive performance of the as pressed filtered pure Jatropha plant oil. In oil dilution tests carried out on piston-ring/cylinder-liner test rigs, the performance of oils processed from plants such as Jatropha are compared to ester-based, polyalkylene glycol based, and hydrocarbon-based oils. Finally, functional properties of Jatropha were compared to further possible second generation bio-oils and aspects of availability and costs are discussed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".