Direct Determination of Metals in Soils and Sediments by Induction Heating-Electrothermal Vaporization (IH-ETV) Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES)
Bibliographic record
Abstract
The application of an induction heating (IH) electrothermal vaporization (ETV) sample introduction arrangement for the determination of As, Cd, Cu, Mn, Pb, and Zn in soils and sediments by inductively coupled plasma-optical emission spectrometry (ICP-OES) is presented. Samples were deposited either directly as a solid or by means of slurry sampling into graphite cups that were then positioned in a radio-frequency (RF)-field and vaporized in a carrier flow of 15% (v/v) SF 6 -Ar. Four certified reference materials (CRMs) were examined: two soil samples—SRM 2710 and SRM 2711 (NIST); and two marine sediments—MESS-2 and PACS-2 (NRC Canada). In general, sample delivery was simpler and observed signal precision was better with slurry sampling when compared to the analysis of the solid directly, with peak area RSDs ranging from 4–16% ( n = 6). Plots of intensity vs. certified concentration for the four CRMs were linear with log-log slopes of 0.98–1.02 and r 2 values ≥ 0.995 for As, Cu, Pb, and Zn. Recoveries of 80–105% were achieved for the above elements in SRM 2711 by using an external standards curve constructed from the 3 remaining CRMs. Aqueous standard solutions were used for the analysis of all 4 CRMs by standard additions, resulting in recoveries ranging from 54–139% (average recovery of (101 ± 15)%) across all six determined elements in all four 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".