Hydrocarbon Compound Type Analysis by Mass Spectrometry: On the Replacement of the All-Glass Heated Inlet System with a Gas Chromatograph
Bibliographic record
Abstract
Hydrocarbon compound type analysis by mass spectrometry is a simple and powerful method that provides extensive compositional information about complex petroleum fractions. Sample introduction into the mass spectrometer has been successfully done for over 30 years using the all-glass heated inlet system (AGHIS). However, the limited transportability of the AGHIS has considerably restricted the wider usage of mass spectrometry for hydrocarbon compound type analysis. In this work, a gas chromatograph (GC) has been examined as a system equivalent to the AGHIS for the introduction of petroleum fractions into the mass spectrometer (MS). It is demonstrated that the GC is a versatile system that offers many advantages over the AGHIS, including simplicity of operation, ease of maintenance, automation, and transportability. Most importantly, the use of the GC allows for the detection of individual compounds, a feature not available with the AGHIS. The capabilities of the GC/MS system have been examined by the analysis of several representative petroleum fractions. The differences in the results obtained by the two sample introduction methods are of the same order of magnitude as the repeatability of the two methods. The ability of the GC/MS system to determine individual compounds in low-boiling-range samples has been illustrated by the use of selective-ion monitoring (SIM) experiments. Individual compounds in heavier samples have been determined by using the automated deconvolution program AMDIS to separate overlapping 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".