TCF selenium preconcentration in geological materials for \ndetermination at sub-μg g−1 with INAA (Se/TCF-INAA)
Bibliographic record
Abstract
In geological samples, Se concentration ranges from 1 × 10−9 g g−1 up to 1 × 10−3 g g−1. The analytical difficulty at low concentration (<1 μg g−1), is one of the main reasons why the geological cycle of Se is poorly known. The analytical method that consisted of preconcentration of Se with thiol cotton fiber (TCF) followed by graphite furnace atomic absorption spectrometry (GFAAS) has been modified by finishing with instrumental neutron activation analysis (INAA). The modified technique involves sample dissolution (HF–HNO3–H2O2) and evaporation to dryness at low temperature (55–60 °C) to avoid selenium volatilization. SeVI is converted to SeIV by adding 6 M HCl to the dry residuum and the solution is then heated in a covered boiling bath (95–100 °C). The solution is diluted to obtain 0.6 M HCl and then collected on TCF. The TCF is placed in a polyethylene vial for irradiation in the SLOWPOKE II reactor (Montréal) for 30 s at a neutron flux of 1015 m−2 s−1. The 162 keV peak of 77mSe (half-life 17.36 s) is read for 20 s after a decay of 7 s. The amount of sample to be dissolved is controlled by two competing effects. To obtain low detection limits, a larger amount of sample should be dissolved. On the other hand, the TCF could become saturated with chalcophile elements when large sample is used. Sulfur is a good indicator of the amount of Se and chalcophile elements present. In S poor sample (<100 μg g−1) 3.0 g of sample was used and the LD was ∼2 ng g−1. In S high samples (>1.5% S) 0.05 g of sample was used and the LD was ∼120 ng g−1. The present work also includes suggested Se concentration for eight international geological reference materials (IGRM) that compare favorably with literature values.
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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 | 0.000 |
| 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) | 0.002 | 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".