Optimization of a new cloud point extraction procedure for the selective determination of trace amounts of total iron in some environmental samples
Bibliographic record
Abstract
A new, simple, and rapid cloud point extraction (CPE) procedure system, combined with flame atomic absorption spectrometry (FAAS), was developed for selective separation, preconcentration, and determination of trace amounts of total iron in some environmental samples. The complexing agent, N,N'-(2,2'-(ethane-1,2-diylbis(oxy)) bis(ethane-2,1-diyl))bis(2-chloroacetamide) (EDBOCA), is selective at pH 5.0 for only Fe(II) and Fe(III) ions in the presence of Cu(II), Pb(II), Cd(II), Mn(II), Co(II), Cr(III), Cr(VI), Ni(II), Zn(II), Al(III), Mo(VI), Pd(II), Pt(IV), Au(III), and V(V) ions. The procedure is based on the complexation of Fe(III) ions with EDBOCA reagent in the presence of Triton X-114 (TX-114) as a non-ionic surfactant. The optimum conditions for the CPE of Fe(III) ions were investigated with respect to several experimental parameters such as pH of the solution, TX-114 and EDBOCA concentrations, incubation time and temperature, and centrifugation rate and time. The detection limit for Fe(III) ions based on the 3 times the standard deviation of the blanks (N:10) was found to be 1.22 \mu g L^{-1}, while the relative standard deviation (RSD) was 4.2%. Environment Canada TM-25.3 and CRM-SA-C Sandy Soil C, as certified reference materials, were used, and spike tests were applied to validate the method. The method was applied to some real environmental samples to evaluate their total iron levels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".