Piston-cylinder based micro liquid–liquid extraction with GC–qMS for trace analysis of targeted chlorinated organic compounds in water
Bibliographic record
Abstract
A fast and reliable approach for the measurement of sub parts-per-billion levels of targeted chlorinated compounds like tetrachloroethane, hexachloroethane, hexachlorobutadiene, pentachlorobenzene, and hexachlorobenzene in various water matrices such as waste water is described. The method employed a novel piston-cylinder-based micro liquid–liquid extraction technique using hexane as an extraction solvent. The device, known commercially as the MIXXOR, substantially accelerates extraction time by a factor of more than 100 times and reduces solvent consumption by a factor of 25 times when compared with the solvent extraction technique using wrist-action mechanical agitation. A recently introduced 6% cyanopropylphenyl –94% dimethylpolysiloxane capillary column offering a high degree of inertness was used for the separation of the analytes. A quadrupole mass spectrometer equipped with a triple-axis detector was also employed to enhance the instrument detection limit. With this technique, a complete separation for the analytes in water can be conducted in less than 10 min using a three-port SilFlow planar microfluidic device for back-flushing. Repeatability of retention times for all compounds were found to be less than 0.04% (n = 10). The compounds cited can be analyzed from 1 ng/mL to 10 μg/mL, with a detection limit and correlation coefficient of at least 0.5 ng/mL and 0.999, respectively. A relative precision of less than 1.2% relative standard deviation (RSD) (n = 20) at the 50 ng/mL level, with analyte recovery of greater than 99% (n = 3) from 10 ng/mL to 10 μg/mL, was obtained.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".