Silver Cationization for Rapid Speciation of Sulfur-Containing Species in Crude Oils by Positive Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry
Bibliographic record
Abstract
Silver cationization constitutes a complementary approach for analysis of petroleum components with positive-ion electrospray ionization (ESI) mass spectrometry and accesses species that lack a basic nitrogen atom and, hence, are not observed by conventional positive ESI. Four samples of different origin [Canadian bitumen, Canadian bitumen heavy vacuum gas oil (HVGO; 475–500 °C) and South American and Middle East heavy crude oils, all high in sulfur content] were used to study silver cationization by (+) ESI. Cationization with Ag + is essentially instantaneous and accesses hydrocarbons and nonpolar sulfur-containing heteroatom classes (e.g., S s and S s O o ), providing an attractive alternative to time-consuming derivatization by S-methylation to ionize sulfur-containing species. For each sample, we compare Ag + cationization (+) ESI to conventional (+) ESI with formic acid to promote ion formation. Other ionization methods, such as chemical ionization (CI), field desorption (FD), matrix-assisted laser desorption ionization (MALDI) chemical ionization, field desorption ionization, and MALDI, are low in throughput and/or involve thermal processes that may degrade substrate molecules from non-volatile high-boiling petroleum components. Mix-and-spray Ag + cationization avoids tedious separation and time-consuming derivatization and results in the rapid speciation of sulfur-containing compounds in petroleum and its fractions without the need for thermal desorption.
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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 | 0.000 |
| 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) | 0.002 | 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".