Characterization of chemical fingerprints of ultralow sulfur fuel oils using gas chromatography-quadrupole time-of-flight mass spectrometry
Bibliographic record
Abstract
With the implementation of stringent environmental regulations, high sulfur fuel oils (HSFO) are shifted to very low (VLSFOs) and ultralow sulfur fuel oils (ULSFOs). The current understanding of these fuels is far from sufficient. The chemical fingerprints of these oils are significantly altered by desulfurization processes, and sulfur-containing compounds present in these oils are in low or extremely low concentrations. These changes pose challenges for petroleum analysis. The ULSFOs studied were limited to distillates. Like conventional fuel oils, ULSFOs are diverse. ULSFOs do not just include distillates but can be a mixture of multiple oil products. The present work measured and compared the physical and chemical properties of ULSFOs with conventional fuels. Gas chromatography-quadrupole time-of-flight mass spectrometry (GC-QTOF-MS) was applied to characterize the chemical fingerprints of ULSFOs. Polycyclic aromatic sulfur heterocycles (PASHs) and their alkylated homologues were determined in considerable abundance in the oils with ≤ 1,000 ppm and ≤ 500 ppm of total sulfur. The chromatographic profiles of ULSFOs were obviously different from that of crude oil and high sulfur fuel oil. Most of PASHs are barely detectable in ≤ 10 ppm S ultralow sulfur diesel fuels (ULSDs), which were subject to deep desulfurization. Some dibenzothiophene isomers such as 4-methyldibenzothiophene,4,6-dimethyldibenzothiophene and 2,4,6-trimethyldibenzothiophene naturally occur in relatively high abundance and are the most refractory to the refinery process due to the methyl steric hindrance. These refractory species were clearly detected in ≤ 10 ppm S ULSDs while other PASHs are barely detectable. Certain compounds with special chemical fingerprints in ULSFOs and ULSDs have potential suitability as diagnostic molecular markers for associated oil spill characterization and identification.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".