Comparison of sector field- and quadrupole-ICP-MS for the determination of DBT and TBT in sediment following GC separation
Bibliographic record
Abstract
A method is described for the accurate and precise determination of tributyltin (TBT) and dibutyltin (DBT) by species-specific isotope-dilution plasma-source mass spectrometry. Using gas chromatography (GC) for sample introduction and analyte separation, a performance comparison was made between sector field inductively coupled plasma mass spectrometry (SF-ICP-MS) detection and quadrupole ICP-MS (qICP-MS) detection. Samples were extracted with acetic acid using open microwave digestion, derivatized with sodium tetraethylborate and extracted into isooctane. Mass bias correction was implemented based on the expected ratio of 120Sn/117Sn to that of the mean 120Sn/117Sn ratio calculated from the inorganic Sn peaks detected in all chromatograms. A more than 2-fold improvement in precision of calculated 120Sn/117Sn ratios was obtained for both TBT and DBT in standards using GC-SF-ICP-MS as compared to GC-qICP-MS. PACS-2 certified reference material marine sediment (NRCC, Ottawa, Canada) was used for method validation. Concentrations of 0.883 ± 0.013 and 1.126 ± 0.013 µg g−1 (mean and one standard deviation, n = 4) as tin were obtained for TBT and DBT, respectively, using GC-SF-ICP-MS detection, in agreement with the certified values of 0.98 ± 0.13 and 1.09 ± 0.15 µg g−1 (95% confidence interval), respectively. Concentrations of 0.883 ± 0.019 and 1.116 ± 0.014 µg g−1 (mean and one standard deviation, n = 4) as tin were obtained for TBT and DBT, respectively, using GC-qICP-MS detection. Slightly better precisions of 1.59–1.62% RSD for TBT and DBT in a test sediment were obtained using GC-SF-ICP-MS compared with 1.64–3.31% RSD obtained with GC-qICP-MS. Method detection limits (LODs, three times standard deviation) of 0.4 and 0.3 ng g−1 for TBT and DBT, respectively, were obtained using GC-SF-ICP-MS, based on processing a 0.5 g sample. As expected, these are superior to LODs of 0.9 and 1.0 ng g−1 obtained using GC-qICP-MS, arising from the three-fold enhancement in signal-to-background ratio obtained with the sector field machine.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 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".