Characterization of Heteroatoms in Residue Fluid Catalytic Cracking (RFCC) Diesel by Gas Chromatography and Mass Spectrometry
Bibliographic record
Abstract
Nitrogen-, sulfur-, and oxygen-containing hydrocarbons in a residue fluid catalytic cracking (RFCC)-derived diesel were characterized by a gas chromatograph equipped with a pulsed flame photometric detector and an electron impact, ammonia chemical ionization mass spectrometer. Caustic and acid extractions on RFCC diesel were performed to isolate the phenolic and basic nitrogen compounds, respectively. The fraction of RFCC diesel that contained the basic nitrogen compounds was reacted with acetic anhydride to allow for chromatographic separation of amino-aromatic compounds from the rest of the non-reactive basic nitrogen compounds. The majority of basic nitrogen compounds were anilines, as expected. Non-basic nitrogen compounds in RFCC diesel were alkyl indoles and alkyl carbazoles. Double-ring and poly-aromatic amines were identified in RFCC diesel by the acetylation reaction. The majority of sulfur compounds in RFCC diesel were alkyl benzothiophenes and dibenzothiophenes, after being isolated by Pd 2+ ligand-exchange chromatography. Dihydrobenzohiophenes and dihydronaphthanthiophenes, despite a low concentration, were also identified in the subfractions. The majority of oxygen compounds in RFCC diesel were phenolic compounds. In addition to alkylphenols, bicyclic and polycyclic phenols were identified and characterized in the acidic fraction.
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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 | 0.000 |
| 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) | 0.001 | 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".