Determination of benzene polycarboxylic acids in atmospheric aerosols and vehicular emissions by liquid chromatography-mass spectrometry
Bibliographic record
Abstract
A simple, sensitive and reliable LC-ESI(−)/MS method was developed for determination of selected aromatic dicarboxylic acids (e.g. phthalic acid isomers) and tricarboxylic acids (e.g. trimellitic, trimesic) in atmospheric aerosols and vehicular emissions without complex sample pre-treatment. Gradient liquid chromatographic separation was performed on a Zorbax SB-Aq (150 × 2.1 mm i.d., 3.5 µm) column using a mobile phase consisting of 0.1% formic acid (eluent A) and methanol (eluent B). The Zorbax SB-Aq column was chosen as it was specifically designed to retain highly polar compounds, allowing the use of highly aqueous mobile phases. The method was demonstrated to be sensitive and precise. In the SIM mode, LODs for all target acids dissolved in double deionized water were in the range of 0.03–0.1 µg/L. The method showed a good inter-day precision of retention time (RSD <0.3%) and peak area (RSD <3%). Satisfactory recoveries, ranging from 89 to 107% for the spiked blanks and extracts of real samples, were obtained. The method has been successfully applied to the quantitative analysis of selected benzene polycarboxylic acids in urban atmospheric aerosols and particulate matter emitted from diesel- and gasoline-powered motors. Preliminary results suggest that 3-hydroxyphthalic acid may be used as a potential tracer for diesel engine emissions.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".