Determination of trace and rare earth elements in marine sediment reference materials by ICP-MS : Comparison of open and closed acid digestion methods
Bibliographic record
Abstract
The ICP-MS determination of REE and several other trace elements in six USGS and Canadian marine sediment reference materials (GSMS-2, GSMS-3, HISS-1, PACS-2, MESS-1, MESS-3) was investigated. The sediment samples were dissolved following open and closed acid digestion procedures, and the methods were evaluated and compared. About 50 mg of the sample powder was used in both digestion techniques with an appropriate dilution, making the solutions suitable for ICP-MS analysis. 103 Rh was used as an internal standard to compensate for the signal drift due to changes in the nebulizer efficiency and other associated matrix interferences. Excellent agreement was observed between the ICP-MS data obtained by both digestion techniques and the certified values, (where available for comparison), for the majority of elements, including all REEs. For a few of the trace elements, such as Cr, Ni, Sr, and Pb, the closed digestion method was found to be very effective due to the controlled temperature and pressure maintained on the sample for many hours. The accuracy and precision achieved was better than 5% RSD in most cases, and the detection limits for most of the elements were in the ppt to sub-ppt range. For the first time, this study provides comprehensive data of trace and REEs by ICP-MS in GSMS-2, GSMS-3, HISS-1, PACS-2, and MESS-3 for which currently only few trace and very few REE-certified values are available in the literature. The analytical data of five marine sediment samples collected from the Bay of Bengal showed that the closed acid digestion technique is a suitable method for the precise ICP-MS determination of trace elements and REEs.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".