Oil fingerprinting analysis using commercial solid phase extraction (SPE) cartridge and gas chromatography-mass spectrometry (GC-MS)
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".