Characterization of renewable diesel, petroleum diesel and renewable diesel/biodiesel/petroleum diesel blends
Bibliographic record
Abstract
The physicochemical properties of a renewable diesel (RD) and several petroleum diesel (PD)–dominant diesels were examined to assess the feasibility of identifying and quantifying the presence of RD and biodiesel from PD and their blends. Relative to all PD-dominant diesels, the RD exhibited a lower density, a higher flash point, reduced evaporation loss and a comparable viscosity and water content. The studied RD contained a limited amount of aromatics, with aliphatic hydrocarbons predominantly within the C 15 to C 18 range. Most individual petroleum hydrocarbons were detected in the PD-dominant diesels, whereas the most abundant hydrocarbons for the RD were alkanes in the < n - C 19 carbon range. The typical chemical signature of RD includes clustered peaks within the C 15 to C 18 range on GC/FID chromatograms coupled with a sharp decline in the abundance of n -alkanes from ∼ n - C 19 and heavier. These properties are robust indicators of RD, even in RD/PD blends. However, quantifying the RD/PD blending ratios is challenging because of their overlapping chemical compositions. The quantified blending ratios for the blended biodiesel/PD are close to theoretical biodiesel ratios. Our analyses provide valuable insights into the forensic identification of RD, biodiesel, PD and their blends.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".