Reassessment of Hydrofluoric Acid Desilicification in the Carius Tube Digestion Technique for Re–Os Isotopic Determination in Geological Samples
Bibliographic record
Abstract
In this study, Re and Os isotopes were systematically determined in six geological reference materials (RMs; covering a wide range of lithologies) using the Carius tube (CT) digestion technique with and without hydrofluoric acid desilicification. Our results show that the HF desilicification increased the Re extraction efficiency (by 9–15%) evidenced from basaltic and andesitic rocks (e.g., BHVO‐2, TDB‐1 and AGV‐2). This implies that a small proportion of Re resides in silicate phases. For mafic–ultramafic rocks (e.g., BCR‐2, WGB‐1 and WPR‐1), Re extraction efficiencies obtained by the CT digestion with and without HF desilicification were similar. This may indicate that Re in these rocks may dominantly reside in some phases (e.g., magnetite and sulfides) that could be completely dissolved in aqua regia solutions without the aid of HF desilicification. Our results also show that the HF desilicification increased Os extraction efficiency (by 13–99%) in some RMs (e.g., BHVO‐2, WGB‐1 and AGV‐2). This observation suggests that a portion of Os‐rich trace phases may occur as inclusions in the silicate phases that act as isolators at ~ 200 mesh sizes. This study demonstrates that the HF desilicification step prior to CT digestion is important for complete extraction of Re and Os in geological samples.
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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) | 0.000 | 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 | 0.000 |
| 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 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".