Separation of fluorine at trace levels to percentile levels by sulfuric acid-accelerated pyrohydrolysis and determination by ion chromatography: Application to geological and environmental samples
Bibliographic record
Abstract
The quantitative separation of fluorine from geological materials through pyrohydrolysis presents a significant challenge, especially when fluorine is present in the form of CaF2, which exhibits high stability. To overcome this, accelerators (compounds like V2O5 and U3O8) are added to expedite the fluorine recovery. A pyrohydrolysis method using concentrated H2SO4 is proposed for complete fluorine separation from geological materials. The pyrohydrolysis distillates were analyzed by ion chromatography to quantify fluoride. To validate the method, fluorine content was analyzed in six certified reference materials (CRMs): BCR-032 (Merck); USGS-G-2, USGS-AGV-1, USGS-GSP-1, and USGS-GXR-3 (United States Geological Survey); NIST-NBS-1645 (National Bureau of Standards). Additionally, samples and reference materials were analyzed using particle-induced gamma emission (PIGE) for fluorine to validate the developed method. Furthermore, several samples, including IAEA reference materials Soil-1, Soil-5, and Soil-7, with unknown fluorine content, were analyzed. High-purity concentrated H2SO4 was identified as a suitable accelerator for routine sample analysis due to its requirement in smaller quantities, and applicability to variety of geological materials containing trace to percentile-level fluorine. The method exhibited a limit of detection of 4 µg.g−1 for a 50 mg sample, and the uncertainty (±1s) ranged from 3% to 7%.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".