Transformation of Nitrogen Compounds in Deasphalted Oil Hydrotreating: Characterized by Electrospray Ionization Fourier Transform-Ion Cyclotron Resonance Mass Spectrometry
Bibliographic record
Abstract
Asphaltenes-free vacuum resid derived deasphalted oils (DAOs) from Chinese Liaohe (LDAO) and Venezuela Orinoco (VDAO) were subjected to catalytic hydrotreating. Electrospray ionization (ESI) Fourier transform-ion cyclotron resonance mass spectrometry (FT-ICR MS) analyses were performed on LDAO and VDAO before and after hydrotreating to determine the structural composition transformation of nitrogen-containing compounds as a result of hydrotreating. The results showed that the basic nitrogen contents of the two DAO before and after hydrotreating were relatively constant. However, the neutral nitrogen contents of hydrotreated LDAO and VDAO were dramatically reduced. The LDAO had a higher neutral nitrogen conversion than VDAO, even though LDAO had a higher neutral nitrogen content than VDAO prior to hydrotreating. The significant difference in nitrogen removal for the DAOs was due to the structural variation of the neutral nitrogen compounds. By plotting the double bond equivalent (DBE) value as a function of carbon number for N 1 class species, the hydrodenitrogenation reactivities of nitrogen compounds could be classified as easy- and hard-to-convert nitrogen compounds. The easy-to-convert nitrogen compounds have more unsaturated cores and have less and/or shorter alkyl side chains than the hard-to-convert nitrogen compounds which have long alkyl side chains.
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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 | 0.000 | 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".