Analysis of the Nitrogen Content of Distillate Cut Gas Oils and Treated Heavy Gas Oils Using Normal Phase HPLC, Fraction Collection and Petroleomic FT-ICR MS Data
Bibliographic record
Abstract
The determination of the nitrogen content of petroleum products is important because nitrogen compounds decrease product quality and can be difficult to remove through processes such as hydrotreating. In recent years, Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) has been used to study the nitrogen species present in petroleum samples (petroleomics), with the goal of identifying hydrotreatment resistant species. While open column separations of petroleum samples are common, there has been little work done that employs a high-performance liquid chromatography (HPLC) separation prior to FT-ICR MS analysis. In this work, distillate cut and treated gas oils are separated on a commercially available dinitrophenyl “DNAP” column and the fractions are collected offline and analyzed by FT-ICR MS. HPLC separations on the “DNAP” column are shown, along with separations on a custom synthesized HPLC column (HC-Tol). Four peak regions are identified on the “DNAP” chromatograms, and the nitrogen species identified in each fraction using positive and negative electrospray ionization are discussed. It was found that pyridines are present in the first three fractions, while pyrroles are present in fractions 2 and 3. Acids are isolated in fraction 4. Alkylation of nitrogen species decreases their level of retention on the “DNAP” column, and the HPLC chromatograms can be used to qualitatively compare nitrogen content between samples. A comparison of HPLC fraction data to MS analysis of unfractionated gas oils found that fraction data are not adequate for quantitative comparisons of carbon number and double bond equivalents between samples.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".