Tracking Neutral Nitrogen Compounds in Subfractions of Crude Oil Obtained by Liquid Chromatography Separation Using Negative-Ion Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry
Bibliographic record
Abstract
Neutral nitrogen compounds have been used as molecular markers for tracking secondary oil migration in geochemistry. However, the distribution of neutral nitrogen compounds in the separation process is not well-characterized because the conventional analytical technique, such as gas chromatography−mass spectrometry (GC−MS), is non-selective for neutral nitrogen and not capable of detecting non-volatile high-molecular-weight compounds. In this paper, a crude oil was subjected to the traditional two-step open-column liquid chromatography (LC) technique to prepare subfractions, which were characterized for their molecular composition of neutral nitrogen compounds by negative-ion electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI FT-ICR MS). The results showed that the two-step LC technique produced a low yield of carbazole in the neutral nitrogen fraction. The neutral nitrogen fraction was enriched with low-molecular-weight neutral nitrogen compounds. Most N 1 class species with relatively low double-bond equivalent (DBE) values and high carbon numbers were eluted into the aromatic fraction, and a portion of neutral nitrogen compounds was eluted into the amino fraction, which was not expected. Because the neutral nitrogen compounds detected by GC−MS analysis only account for a fraction of total neutral nitrogen compounds, caution should be exercised in interpreting the analytical data obtained from the two-step LC technique. The analytical bias of the separation technique could lead to erroneous geochemical interpretations when a low yield of carbazole in the neutral nitrogen fraction was derived. Negative-ion ESI FT-ICR MS was an effective tool to monitor and evaluate the separation technique for neutral nitrogen compounds in crude oil.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".