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 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 | 0.000 |
| 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".