Evaluations of Weathering of Polar and Nonpolar Petroleum Components in a Simulated Freshwater–Oil Spill by Orbitrap and Fourier Transform Ion Cyclotron Resonance Mass Spectrometry
Bibliographic record
Abstract
The comprehensive chemical characterization of crude oil is important for the evaluation of the transformation and fate of components in the environment. Molecular-level speciation of naphthenic acid fraction compounds (NAFCs) was investigated in a mesoscale spill tank using both negative-ion electrospray ionization (ESI) Orbitrap mass spectrometry (MS) and positive-ion atmospheric pressure photoionization Fourier transform ion cyclotron resonance mass spectrometry (APPI-FT-ICR-MS). Both ionization techniques are coupled to high-resolution mass spectrometric detectors (ESI: Orbitrap MS; APPI: FT-ICR-MS at 9.4 T), enabling insight into the behavior and fate of petrogenic compounds during a simulated freshwater crude oil spill. Negative-ion ESI Orbitrap-MS reveals that oxygen-containing (O x ) classes are detected early in the spill, whereby species with more oxygen per molecule evolve later in the simulated spill. The O 2 -containing species gradually decreased in relative abundance, while O 3 and O 4 species increased in relative abundance throughout the simulated spill, which could correspond to a relative degree of oxygen incorporation. Nonpolar speciation by positive-ion APPI 9.4 T FT-ICR-MS allowed for the identification of water-soluble nonpolar and less polar acidic species. Molecular-level graphical representation of elemental compositions derived from simulated spill water-soluble and oil-soluble species suggest that biological activity is the primary degradation mechanism and that biodegradation was the dominant mechanism based on the negative-ion ESI Orbitrap-MS results.
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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 | 0.000 | 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".