Analysis of Asphaltenes and Asphaltene Model Compounds by Laser-Induced Acoustic Desorption/Fourier Transform Ion Cyclotron Resonance Mass Spectrometry
Bibliographic record
Abstract
Laser-induced acoustic desorption (LIAD)/electron ionization (EI) was used to study asphaltene model compounds and asphaltenes derived from North American crude oil in a Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometer (MS). Successful desorption by LIAD of all model compounds (including a polyphenylated vanadoyl porphyrin) as intact neutral molecules into the mass spectrometer indicates that this method allows the evaporation of most if not all components of asphaltenes into mass spectrometers for further characterization. Electron ionization is a universal ionization method that ionizes all organic compounds. Hence, it is not surprising that all the model compounds studied were successfully ionized by using this method. Furthermore, this method yielded stable molecular ions for all model compounds studied. Because LIAD/EIMS provides MW information for these model compounds, this is almost certainly also true for all components of asphaltenes. Examination of asphaltene samples derived from North American crude oil by using this technique yielded a MW distribution of about 350−1050 Da and provided structural information for asphaltene components.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".