Ligandless, Task-Specific Ionic Liquid-Based Ultrasound-Assisted Dispersive Liquid–Liquid Microextraction for the Determination of Cobalt Ions by Electrothermal Atomic Absorption Spectrometry
Bibliographic record
Abstract
Ligandless, task-specific ionic liquid based ultrasound-assisted dispersive liquid–liquid microextraction (TSIL-USA-DLLME) was used for preconcentration of cobalt ions in food and water samples and in vitamin supplements before analysis by electrothermal atomic absorption spectrometry. The reported method is free of toxic volatile organic solvents and does not require the use of a back-extraction step. The dispersion of extractant was achieved with the use of ultrasound. A TSIL, trioctylmethylammonium thiosalicylate (TOMATS), was served as both the extraction and complexing agent. After microextraction, the TOMATS phase was separated by centrifugation and dissolved in ethanol before analysis. Selected parameters affecting the microextraction including the pH of the sample, the volume of the ionic liquid, the ultrasonication time, centrifugation parameters, and the influence of ionic strength were optimized. The limit of detection was 0.011 ng mL−1 for cobalt ions. The achieved preconcentration factor was 24. The relative standard deviations for the determination of analyte in the real samples were 3–24%. The accuracy of this method was evaluated by the extraction and determination of the analyte in several certified reference materials including INCT-SBF-4 (soya bean flour), INCT-TL-1 (tea leaves), ERM-CAO11b (hard drinking water), INCT-MPH-2 (mixed Polish herbs), TMDA-54.5 (Lake Ontario Water), and NIST 1643e. The measured cobalt contents were in satisfactory agreement with the certified concentrations based on Student’s t-test at the 95% confidence level. The presented method has been successfully applied for the determination of analyte in real samples that include tea, lake water, and vitamin supplements.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".