Determination of fluorine in herbs and water samples by molecular absorption spectrometry after preconcentration on nano-TiO2 using ultrasound-assisted dispersive micro solid phase extraction
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Bibliographic record
Abstract
This work presents ultrasound-assisted dispersive micro solid phase extraction (USA DMSPE) for preconcentration of fluorine (F) in water and herb samples. TiO 2 nanoparticles (NPs) were used as an adsorbent. The determination with slurry sampling was performed via molecular absorption of calcium monofluoride (CaF) at 606.440 nm using a high-resolution continuum source electrothermal absorption spectrometry (HR-CS ET MAS). Several factors influencing the efficiency of the preconcentration technique, such as the amount of TiO 2 , pH of sample solution, ultrasonication and centrifugation time and TiO 2 slurry solution preparation before injection to HR-CS ET MAS, were investigated in detail. The conditions of detection step (wavelength, calcium amount, pyrolysis and molecule-forming temperatures) were also studied. After extraction, adsorbent with the analyte was mixed with 200 μL of H 2 O to prepare a slurry solution. The concentration limit of detection was 0.13 ng mL −1 . The achieved preconcentration factor was 7. The relative standard deviations (RSDs, %) for F in real samples were 3–15%. The accuracy of this method was evaluated by analyses of certified reference materials after spiking: INCT-MPH-2 (Mixed Polish Herbs), INCT-SBF-4 (Soya Bean Flour), ERM-CAO11b (Hard Drinking Water) and TMDA-54.5 (Lake Ontario Water). The measured F contents in reference materials were in satisfactory agreement with the added amounts, and the recoveries were found to be 97–109%. Under the developed extraction conditions, the proposed method has been successfully applied for the determination of F in real water samples (lake, sea, tap water) and herbs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it