A comparison of alkyl derivatization methods for speciation of mercury based on solid phase microextraction gas chromatography with furnace atomization plasma emission spectrometry detection
Bibliographic record
Abstract
Several derivatizing agents were evaluated for use in speciating mercury in biological samples using solid phase microextraction in conjunction with tandem gas chromatography-furnace atomization plasma emission spectrometry (SPME-GC-FAPES). Following digestion with methanolic potassium hydroxide, the pH of the samples was adjusted and NaCl added when necessary. The mercury species were then derivatized with sodium tetraphenylborate or sodium tetrapropylborate and extracted by SPME using a 100 µm PDMS coated fiber. The derivatized species were then separated by GC and detected by FAPES. All experimental parameters were optimized for best separation and analytical response. Propylation proved to be more sensitive, robust and faster than ethylation or phenylation, leading to procedural detection limits of 0.55 ng g−1 for methylmercury, 0.34 ng g−1 for ethylmercury and 0.23 ng g−1 for inorganic mercury. An intra-day and intra-fiber precision of typically 2.2% was achieved whereas long-term (4 months) and inter-fiber reproducibility precision was typically 4.4%. The accuracy of the method was validated by the analysis of Certified Reference Materials (DORM-2, DOLT-2 and TORT-2) from the National Research Council of Canada.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".