Application of CE with novel dynamic coatings and field‐amplified sample injection to the sensitive determination of isomeric benzoic acids in atmospheric aerosols and vehicular emission
Bibliographic record
Abstract
A simple and reliable CE method with direct UV detection has been developed to separate eight isomeric benzoic acids in atmospheric aerosols and vehicular emission without complex sample pretreatment. Optimal electrophoretic conditions, with migration times under 5 min, were obtained by using a 50 mM acetate buffer (pH 4.7) containing a dynamic surface coating EOTrol LN (0.005% w/v). The separations were carried out in a cathode to anode direction (-30 kV) allowing the low cathodal EOF ( approximately 1 x 10(-9) m(2)V(-1)s(-1)) to extend the effective separation by slowing the movement of the studied aromatic acids. Moreover, the sensitivity of the method at 200 nm was enhanced by using a field-amplified sample injection (FASI) with electrokinetic (EK) sample injection (-2 kV, 60 s). Prior to sample injection, a short water plug (3 s at 0.5 psi) was introduced. Under these conditions, the method was capable of detecting the analytes in deionized water with LODs (S/N = 3) as low as 0.1 microg/L for most of the studied acids. In the presence of 10 mg/L of sulphate (added to simulate a sample matrix), LODs ranged from 0.26 to 0.62 microg/L. The validation of the method has proven an excellent separation performance and accuracy for the determination of isomeric benzoic acids in the studied matrices.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".