Headspace In-Tube Microextraction and GC-ICP-MS Determination of Mercury Species in Petroleum Hydrocarbons
Bibliographic record
Abstract
Characterization of mercury contamination in petroleum hydrocarbons (PHs) is necessary in order to assess the risk of corrosion of the processing infrastructure and to assess the level of human exposure to Hg-containing substances. Here we present an accurate and sensitive method for determination of Hg species in PHs by headspace sampling with a possibility of on-line pre-concentration using in-tube extraction (ITEX) combined with gas chromatography–inductively coupled plasma mass spectrometry (GC-ICP-MS) analysis. Mercury species were first extracted from the PHs matrix into an aqueous phase via dithizone chelation and subsequently converted with sodium tetrapropyl borate into volatile derivatives which could be sampled from the headspace prior to GC-ICP-MS analysis. For concentrations in the ng kg –1 range, the on-line ITEX method was applied, whereas the μg kg –1 range was accessible by static headspace. Quantitation of Hg species was carried out by a double isotope dilution method, with quantitative recoveries of methylmercury (MeHg, average 101 ± 5%) and inorganic mercury (InHg, average 97 ± 7%) by direct headspace injection. Average recoveries of Hg spikes after on-line ITEX pre-concentration were 95 ± 3% for MeHg and 98 ± 8% for InHg. The detection limits for MeHg and InHg were 428 and 46 ng kg –1 when measured by static headspace, and 2.4 ng kg –1 and 1.7 ng kg –1 by on-line ITEX pre-concentration. The accuracy of the pre-concentration method was demonstrated by analysis of a crude oil standard reference material (NIST 2722) certified for InHg.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".