On-Line Desalting of Crude Oil in the Source Region of a Fourier Transform Ion Cyclotron Resonance Mass Spectrometer
Bibliographic record
Abstract
The presence of dissolved metal ions in waters associated with crude oils has many negative implications for the transport, processing, and refining of petroleum. In addition, mass spectrometric analysis of sodium containing crude oil samples suffers from ionization suppression, unwanted adduct formation, and an increase in the complexity of data analysis. Here, we describe a method for the reduction/elimination of these adverse effects by modification of the source region gas-inlet system of a 12 T Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometer. Several acids were examined as part of this study, with the most suitable for on-line desalting found to have both high vapor pressure and low pK(a); 12.1 M HCl showed the strongest desalting effect for crude oil samples with a sodium removal index (SRI) of 88%-100% ± 7% for the NaOS compound class. In comparison, a SRI of only 38% ± 9% was observed for a H₂O/toluene solution-phase extraction of oil 1. These results clearly demonstrate the increased efficacy of pseudo-vapor phase desalting with the additional advantages that initial sample solution conditions are preserved and no sample preparation is required prior to analysis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".