Lessons Learned from a Decade-Long Assessment of Asphaltenes by Ultrahigh-Resolution Mass Spectrometry and Implications for Complex Mixture Analysis
Bibliographic record
Abstract
Recent advances in instrumentation for high-field Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) have enabled access to ∼70 000 unique molecular formulas in broadband mass spectral characterization of unfractionated/whole asphaltenes. The results accumulated over a decade highlight the need for an asphaltene molecular model that acknowledges the coexistence of (1) monofunctional and polyfunctional species; (2) island and archipelago structural motifs; and (3) heteroatom-depleted/highly aromatic compounds, as well as atypical species with low aromaticity but increased heteroatom content. Collectively, results from FT-ICR MS, preparatory-scale separations (extrography/interfacial material), gel permeation chromatography, precipitation behavior in heptane:toluene, thermal decomposition, and aggregate microstructure by atomic force microscopy (among other techniques), suggest that the strong aggregation of asphaltenes results from the synergy between several intermolecular forces: π-stacking, hydrogen bonding, London forces, and acid/base interactions. This review presents general features of asphaltene molecular composition reported over the past five decades. We focus on mass spectrometry characterization and expose the reasons why early results supported the dominance of single-core motifs. Then, the discussion shifts to recent advances in instrumentation for high-field FT-ICR MS, which have enabled the detection of thousands of species in asphaltene samples, whose molecular composition and fragmentation behavior in ultrahigh vacuum agree with the coexistence of single-core and multicore structural motifs. Furthermore, evidence that highlights the limitations of commercially available/custom-built ion sources and selective ionization effects is presented. Consequently, the limitations require separations (e.g., chromatography, extrography) to gain more-comprehensive molecular-level insights into the composition of these complex organic mixtures. The final sections present evidence for the role of aggregation in selective ionization and suggest that advanced characterization by both thermal desorption/decomposition and liquid chromatography with online FT-ICR MS detection can be employed to mitigate the effects of aggregation and provide unique insights in molecular composition/structure.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".