Profiling Alkyl Phosphates in Industrial Petroleum Samples by Comprehensive Two-Dimensional Gas Chromatography with Nitrogen Phosphorus Detection (GC × GC–NPD), Post-column Deans Switching, and Concurrent Backflushing
Bibliographic record
Abstract
Several refinery fouling incidents in North America have been due to the presence of alkyl phosphates in the crude oil feed. These phosphates originate in some cases from their use as gellants (viscosity builders) for fracturing fluids used in the process of hydraulic fracturing in water-sensitive geologies. Industry responded with an inductively coupled plasma–optical emission spectroscopy (ICP–OES) method for the analysis of total volatile phosphorus. Applied to distillate fractions of crude oil, this method is plagued with limited precision and a high limit of detection (0.5 ± 1 μg of phosphorus mL –1 ). This approach provides only total P with no speciation information; thus, it cannot be used to develop an understanding of alkyl phosphate fouling at a molecular level. Our group previously presented an approach using comprehensive two-dimensional gas chromatography with nitrogen phosphorus detection (GC × GC–NPD) and post-column Deans switching that provided qualitative and quantitative profiles of alkyl phosphates in industrial petroleum samples with increased precision and at levels comparable to or below those achievable by ICP–OES. Here, we present a refinement to this method that incorporates splitless injection and concurrent backflushing. Using this technique, it is possible to quantify alkyl phosphates to levels 2 orders of magnitude lower than those achieved with our previous approach and 2–3 orders of magnitude lower than what is possible by ICP–OES while still maintaining an increased precision over ICP–OES. The addition of concurrent backflushing provided column protection, reducing instrument maintenance and improving the reproducibility of retention times when analyzing heavier industrial petroleum fractions. A recovery study performed in two different industrial petroleum samples demonstrated the reliability of calibrations performed in solvent when used for quantification of alkyl phosphates in real samples. Finally, a profiling study of alkyl phosphates in 14 different industrial petroleum samples (crude oil and mixtures of crude oil and fracture fluid) is also presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".