Determination of Hydrocarbon Group-Type of Diesel Fuels by Gas Chromatography with Vacuum Ultraviolet Detection
Bibliographic record
Abstract
A GC-vacuum ultraviolet (UV) method to perform group-type separations of diesel range fuels was developed. The method relies on an ionic liquid column to separate diesel samples into saturates, mono-, di-, and polyaromatics by gas chromatography, with selective detection via vacuum UV absorption spectroscopy. Vacuum UV detection was necessary to solve a coelution between saturates and monoaromatics. The method was used to measure group-type composition of 10 oilsands-derived Synfuel light diesel samples, 3 Syncrude light gas oils, and 1 quality control sample. The gas chromatography (GC)-vacuum UV results for the Synfuel samples were similar (absolute % error of 0.8) to historical results from the supercritical fluid chromatography (SFC) analysis. For the light gas oils, discrepancies were noted between SFC results and GC-vacuum UV results; however, these samples are known to be challenging to quantify by SFC-flame ionization detector (FID) due to incomplete resolution between the saturate/monoaromatic and/or monoaromatic/diaromatic group types when applied to samples heavier than diesel (i.e., having a larger fraction of higher molecular weight species). The quality control sample also performed well when comparing both methods (absolute % error of 0.2) and the results agreed within error for saturates, mono- and polyaromatics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".