Polymer Additives as Cold Flow Improvers for Palm Oil Methyl Esters
Bibliographic record
Abstract
Biodiesel is a fuel that can be originated from different raw materials. However, even a good quality biodiesel has its properties strongly dependent upon its fatty acid profile. For instance, samples with a high content of saturated fatty acids may present poor cold flow properties, meaning that they tend to solidify under low temperatures. In this work, we propose to synthesize polymer additives and apply them as cold flow improvers for palm methyl esters (biodiesel) and their blends with petrodiesel. The synthetic additives were evaluated at different concentrations to assess their effect on the pour point (PP) of the resulting neat or blended biofuel. By using 1000 ppm of poly(dodecyl acrylate‐co‐tetradecyl methacrylate), it was possible to decrease the PP of B5 (5 vol.% biodiesel + 95 vol.% diesel) and B20 (20 vol.% biodiesel + 80 vol.% diesel) to −35 °C and −14 °C, respectively, which allowed the use of these blends in regions where their flow properties would eventually be prohibitive. However, the performance of the additive on the neat fuel (B100) was negligible.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".