Improved gas chromatography methods for micro-volume analysis of haloacetic acids in water and biological matrices
Bibliographic record
Abstract
A fast headspace solid-phase microextraction gas chromatography method for micro-volume (0.1 mL) samples was optimized for the analysis of haloacetic acids (HAAs) in aqueous and biological samples. It includes liquid-liquid microextraction (LLME), derivatization of the acids to their methyl esters using sulfuric acid and methanol after evaporation, followed by headspace solid-phase microextraction with gas chromatography and electron capture detection (SPME-GC-ECD). The derivatization procedure was optimized to achieve maximum sensitivity using the following conditions: esterification for 20 min at 80 degrees C in 10 microL methanol, 10 microL sulfuric acid and 0.1 g anhydrous sodium sulfate. Multi-point standard addition method was used to determine the effect of the sample matrix by comparing with internal standard method. It was shown that the effect of the matrix for urine and blood samples in this method is insignificant. The method detection limits are in the range of 1 microg L(-1) for most of the HAAs, except for monobromoacetic acid (MBAA) (3 microg L(-1)) and for monochloroacetic acid (MCAA) (16 microg L(-1)). The optimized procedure was applied to the analysis of HAAs in water, urine and blood samples. All nine HAAs can be separated in < 13 min for biological samples and < 7 min for drinking water samples, with total sample preparation and analysis time < 50 min. Analytical uncertainty can increase dramatically as the sample volume decreases; however, similar precision was observed with our method using 0.1 mL samples as with a standard method using 40 mL samples.
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How this classification was reachedexpand
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 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".