Molecular characterization of petroporphyrins in crude oil by electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry
Bibliographic record
Abstract
Petroporphyrin compositional analysis of a heavy crude oil has been realized by isolation and subsequent ESI-FT-ICR mass spectrometric analysis of the porphyrin-containing fractions. Vanadium octaethyl (V=O(II)OEP) and nickel octaethyl (Ni(II)OEP) porphyrin standards were analyzed to determine favorable electrospray ionization conditions and provide insight as to the molecular species present (e.g., adducts, multimers). Standard V=O(II)OEP and Ni(II)OEP solutions revealed the presence of both monomer and dimer species with a greater relative abundance of monomers. In contrast, mass spectral analysis of a porphyrin fraction from Cerro Negro crude oil was dominated by dimeric species. MS3 analysis identified a dioctylphthalate (DOP) contaminant, likely introduced during fractionation of the crude oil. DOP-porphyrin complexes and porphyrin-porphyrin dimers were then identified. Infrared multiphoton dissociation (IRMPD) of dimeric species produced the corresponding monomers with minimal fragmentation. The monomeric petroporphyrins were analyzed to reveal the metal (Ni(II) or V=O(II)), porphyrin type (e.g., etio vs. DPEP), and distribution of alkylation.Key words: petroporphyrin, porphyrin, petroleum, electrospray ionization, mass spectrometry, Fourier transform, ion cyclotron resonance, ICR, FT-ICR, FTMS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".