Determination of Ultra Trace Levels of 1,2-Dichloroethane in Air by Sample Enrichment Micromachined Gas Chromatography-Differential Mobility Detection
Bibliographic record
Abstract
A novel analytical procedure has been developed for the analysis of ultra trace levels of 1,2-dichloroethane (EDC) in air using sample enrichment in combination with micromachined gas chromatography (GC) and differential mobility detection (DMD). When compared to other contemporary GC techniques, such as GC-flame ionization detection, GC-electron capture detection, or GC-electrolytic conductivity detection, the employment of a DMD in combination with a preconcentrator provided better sensitivity and markedly improved selectivity. The increase in sensitivity reduces false-negative results, while the improvement in selectivity decreases the potential for false-positive results. Using the technique described, a complete analysis can be conducted in less than 10 min, with a detection limit of 0.7 ppb (v/v) of EDC and a short term precision of less than 6%. A correlation coefficient of 0.9988 was obtained over an EDC concentration range from 0.7 ppb to 36.4 ppb (v/v). The analytical system also has an on-board microTCD in series with the DMD, allowing both detector outputs to be monitored simultaneously. With the pre-concentration technique, the microTCD can detect EDC as low as 15 ppb (v/v) with a substantially enhanced linear dynamic range in addition to providing a confirmation means for the presence of EDC at the level cited.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".