Determination of zinc in marine/lacustrine sediments by graphite furnace atomic absorption spectrometry using Pd/Mg chemical modifier and slurry sampling
Bibliographic record
Abstract
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). Keywords: graphite furnace atomic absorption spectrometry (GF-AAS)slurry samplingmarine and lacustrine sedimentszincchemical modifierpalladiummagnesium Acknowledgement This work was supported by the funding agency in Japan: The Japan Society for the Promotion of Science KAKENHI (No. 19550077).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".