Determination of Zinc in Aerosol Samples by Discrete Nebulization Flame Atomic Absorption Spectrometry
Bibliographic record
Abstract
A well-known analytical method, ``discrete nebulization'', which is suitable for samples having a limited amount like body fluids, high solid content or salt solutions, and less volatile metals, has been further developed for zinc determination in aerosol samples. The effects of injection volume and nebulizer aspiration rate on absorbance and precision were studied. A calibration graph was obtained linearly up to 1.75 mg/L Zn with a 0.9988 correlation coefficient and 3.09% RSD using 50 m L injection volume, values comparable to conventional continuous nebulization. The detection limit (3S) achieved by the method was calculated to be 1.09 ng or 0.020 mg/L for 50 m L injection volume. A hydrophobic PTFE micro sampling cup was used. Aerosol samples were collected between January 1996 and December 1998 in Mersin, Turkey, using polycarbonate filters. Collected samples were digested in the presence of HNO3 and HF. Digested samples were analyzed for zinc using discrete nebulization flame atomic absorption spectrometry (FAAS). The validity of the proposed method was established by evaluating the accuracy of analyses of BCSS-1 (Marine Sediment Reference Materials for Trace Metals and Other Constituents) CANADA. For the given true value of 119.0 ± 12 mg/Kg zinc, a value of 118.98 ± 0.26 mg/Kg zinc was found with a precision of 0.22% RSD. As a further check on accuracy, recoveries from aerosol samples were examined and found to be 94-102% , again testifying to the reliability of the proposed method.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".