Determination of Copper and Cadmium in Petroleum Produced Formation Water by Electrothermal Atomic Absorption Spectrometry after Cloud Point Extraction
Bibliographic record
Abstract
Cloud point extraction (CPE) in association with graphite furnace atomic absorption spectrometry (GF AAS) was proposed for preconcentration and quantification of trace amounts of copper and cadmium in samples of saline produced formation water from petroleum exploration. The procedure was based on the formation of hydrophobic complexes of the analytes with 1,5-diphenylthiocarbazone (dithizone) in a micellar media of the surfactant (1,1,3,3-tetramethyl butyl) phenyl-polyethylene glycol (Triton X-114). Constrained mixture design was performed for the optimization of the proportions of the three solutions employed in the CPE: Triton X-114, dithizone, and buffer solution. Under the recommended conditions, the CPE GF AAS procedure allowed to obtain enrichment factors of 18 and 11 times, limits of quantification of 0.030 and 0.12 µg L-1 and precision, expressed as relative standard deviation (RSD, n = 8, 2.0 µg L-1), of 1.1 and 4.3% for copper and cadmium, respectively. The CPE GF AAS method was applied to the determination of copper and cadmium in samples of saline produced formation water from petroleum exploration, and its accuracy was accessed by analyzing certified reference material CASS-5 (Nearshore Seawater Reference Material for Trace Metals) from National Research Council (Canada).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".