Molecular Composition of Middle Eastern Asphaltenes by Mass Spectrometry: Field <i>vs</i> Dead-Oil-Derived Deposits
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Asphaltenes are complex mixtures of natural compounds that have proven to be notoriously difficult to analyze using routine methods. Here, a mechanically isolated asphaltene field deposit from an oil well obtained using a gauge cutter and dead oil-derived asphaltene from the same well in an offshore Abu Dhabi oilfield were analyzed by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS). Atmospheric Pressure Photon Ionization (APPI), Laser Desorption Ionization (LDI), and Electrospray Ionization (ESI) were applied using a combination of positive (APPI, LDI, and ESI) and negative (ESI) ion modes, and the results were compared to bulk elemental ratios based on independent elemental analysis. Results indicate that the deposit was significantly enriched in sulfur relative to its dead-oil-derived counterpart. The latter was slightly enriched in nitrogenous species and also contained maltenes covering a wider compositional space than that of the field deposit. APPI resulted in marginally better agreement with bulk elemental analysis data for the field deposit and significantly better agreement for its dead-oil-derived counterpart. Whereas LDI generally preferentially ionizes organic nitrogen, the molar S/C ratio of the field deposit is better matched to the LDI-generated molar S/C ratio. We conclude that the optimal ionization method for mass spectrometric analysis is sample-dependent. Most importantly, we demonstrate that significant compositional differences exist between the dead-oil-derived sample and the deposit, raising concerns as to whether dead-oil-derived asphaltenes should be used in asphaltene-inhibitor evaluation studies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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 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".