Cloud point extraction of Pd(II), Au(III), and Ag(I) prior to their determination by graphite furnace atomic absorption spectrometry
Bibliographic record
Abstract
A simple and rapid cloud point extraction methodology has been developed for the separation and preconcentration of palladium (Pd2+), gold (Au3+), and silver (Ag+) ions. The metal ions in the initial aqueous solution were complexed with 4-allylthiosemicarbazide, and Triton X-114 was added as surfactant. Dilution of the surfactant-rich phase with acidified metanol was performed after phase separation, and the metal ions were determined by graphite furnace atomic absorption spectrometry. The main factors affecting the cloud point extraction procedure, such as pH, concentration of the ligand, amount of Triton X-114, equilibrium temperature, and incubation time, were investigated and optimized. Under the optimum experimental, the calibration graphs were linear upto 100 μg L−1 for Pd2+ and Au3+ and up to 80 μg L−1 for Ag+. The enrichment factors were 48, 53, and 51 for Pd2+, Au3+, and Ag+, respectively. The limits of detection, based on three times the standard deviation of the blank signal by seven replicate measurements, were 0.15, 0.07, and 0.04 μg L−1 for Pd2+, Au3+, and Ag+, respectively. The accuracy of the results was verified by analyzing spiked real samples (water, blood, and urine) as well as by comparison the results of geological samples with those obtained by ICP−MS after solvent extraction using ammonium pyrrolidinedithiocarbamate in methyl isobutyl ketone. The proposed method has been applied for the determination of the metal ions in real samples with satisfactory results.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".