Oil fingerprinting analysis using commercial solid phase extraction (SPE) cartridge and gas chromatography-mass spectrometry (GC-MS)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study used solid phase extraction (SPE) cartridges for rapid cleanup and fractionation of oil samples in oil fingerprinting analysis. A series of commercially available florisil cartridges, normal phase SPE cartridges, and silica gel/cyanopropyl (SiO2/C3-CN) SPE cartridges was selected for the fractionation of oil into aliphatic and aromatic hydrocarbons. The florisil cartridges and normal phase SPE cartridges can clean up the oil samples but are unable to separate them into two fractions. The SiO2/C3-CN (1 g/0.5 g) SPE cartridge successfully separated oil samples into aliphatic and aromatic fractions by eluting with 4 mL of hexane and 4 mL of dichloromethylene (DCM)/hexane (3 : 1, v:v), respectively. No cross-elution was observed between aliphatic and aromatic fractions when oil loading mass was less than 40 mg on the SiO2/C3-CN SPE cartridge. The relative standard deviation (RSD) of five replicates of SPE-GC-MS analysis of 5 mg of reference oil is 2.8%, 1.2%, and 6.9% for total n-alkanes, polycyclic aromatic hydrocarbons (PAHs), and biomarkers, respectively. The recoveries of six spiked deuterated surrogates were all above 95%. This SPE-GC-MS method was used for the fingerprinting analysis of various crude oils, refined petroleum products, and environmental sediment samples. The characterized target hydrocarbons included n-alkanes, unsubstituted priority PAHs and alkylated homologues, and biomarker terpanes and steranes. The concentration profiles and diagnostic ratios of target compounds are both comparable to those obtained by the conventional silica gel column-GC-MS method.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.000 | 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 it