Determination of zinc in marine/lacustrine sediments by graphite furnace atomic absorption spectrometry using Pd/Mg chemical modifier and slurry sampling
Bibliographic record
Abstract
Effectiveness of Pd/Mg chemical modifier for the accurate direct determination of zinc in marine/lacustrine sediments by graphite furnace atomic absorption spectrometry (GF-AAS) using slurry samples was evaluated. A calibration curve prepared by aqueous zinc standard solution with addition of Pd/Mg chemical modifier is used to determine the zinc concentration in the sediment. The accuracy of the proposed method was confirmed using Certified Reference Materials, NMIJ CRM 7303-a (lacustrine sediment) from National Metrology Institute of Japan, National Institute of Advanced Industrial Science and Technology, Japan, and MESS-3 (marine sediment) and PACS-2 (marine sediment) from National Research Council, Canada. The analytical results obtained by employing Pd/Mg modifier are in good agreement with the certified values of all the reference sediment materials. Although for NRC MESS-3 an accurate determination of zinc is achieved even without the chemical modifier, the use of Pd/Mg chemical modifier is recommended as it leads to establishment of a reliable and accurate direct analytical method. One quantitative analysis takes less than 15 minutes after we obtain dried sediment samples, which is several tens of times faster than conventional analytical methods using acid digested sample solutions. The detection limits are 0.13 µg g−1 (213.9 nm) and 16 µg g−1 (307.6 nm), respectively, in sediment samples, when 40 mg of dried powdered samples are suspended in 20 mL of 0.1 mol L−1 nitric acid and a 10 µl portion of the slurry sample is measured. The precision of the proposed method is 8–15% (RSD).
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".