Accurate Determination of Trace Cadmium in Soil Samples with Graphite Furnace Atomic Absorption Spectrometry Using Metal–Organic Frameworks as Matrix Modifiers
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Bibliographic record
Abstract
Cadmium (Cd) is a highly toxic heavy metal that is widely distributed in soils at low concentrations. Its volatilization loss occurs at an ashing temperature higher than 350 °C. Accordingly, the accurate determination of its concentration in soils often requires the addition of chemical modifiers by graphite furnace atomic absorption spectrometry (GFAAS) to improve the thermal stability of Cd. In this work, a metal–organic framework (MOF, UIO-66-NH 2 ) was utilized as a matrix modifier to improve accuracy in determining Cd in soils using GFAAS. The Cd signal, as influenced by drying and pyrolysis temperature and special gas, was also investigated, and the measuring conditions were optimized (2 μg L −1 Cd concentrations, matrix modifier: UIO-66-NH 2 , pyrolysis temperature: 550 °C, atomization temperature: 1650 °C, and special gas: air). The limit of detection (LOD) and the characteristic mass for Cd in soil were 3.84 ng g −1 and 0.80 pg, respectively. Five soil standard reference materials (SRMs) were analyzed for Cd to verify the effectiveness of the optimized measuring conditions. The soil Cd concentrations obtained were in remarkable agreement with the reference values. Moreover, the mechanism of UIO-66-NH 2 on the graphite platform was investigated in the drying and pyrolysis steps by scanning electron microscopy (SEM), Fourier transform infrared (FT-IR), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). During the drying steps, many functional groups of the matrix were activated, and Cd 2+ could be captured on its surface. After the pyrolysis step, the structure matrix collapsed and was partially decomposed into ZrO 2 , and subsequently, an intermediate state of ZrO 2 -(CdO) appeared at high temperatures, which came from the active binding sites on the zirconium oxide octahedron nodes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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 it