Determination of Aromatic Acids and Nitrophenols in Atmospheric Aerosols by Capillary Electrophoresis
Bibliographic record
Abstract
A capillary zone electrophoresis method is developed for the determination of aromatic organic acids and nitrophenols in atmospheric aerosols. The procedure is based on sampling atmospheric particulate matter on quartz fiber filters and the extraction and analysis of the extracts by capillary electrophoresis. Separation conditions are optimized by varying the pH and acetonitrile content of the electrolyte buffer. Separations in a 20% acetonitrile-20 mM borate mixture (pH 9.9) are able to resolve all of the geometric isomers of hydroxybenzoic acid, phthalic acid, benzenetricarboxylic acid, and nitrophenol as well as 1,2,4,5-benzenetricarboxylic acid, m-toluic acid, and sulfosalicylic acid. A buffer consisting of 11% acetonitrile-20 mM borate (pH 9.9) is found to be most suitable for the analysis of atmospheric aerosol samples. Detection limits are in the order of 40 to 130 ng/mL. Intersample migration time reproducibility is generally better than 1.5%, with day-to-day variations under 3%. A general extraction scheme using diethyl ether-HCl in combination with a preconcentration step is developed. Recoveries of spiked standards range from 59% to 102%, with relative standard deviations ranging from 2% to 17% for five determinations. The method is applied towards the analysis of ambient aerosol samples as well as vehicle emission studies with promising results, thus showing it to be a potential complement to already existing methodology for the analysis of organic acids and nitrophenols in atmospheric aerosols.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".