Application of a Microfluidic Gas-to-Liquid Interface for Extraction of Target Amphetamines and Precursors from Air Samples
Bibliographic record
Abstract
The investigation of clandestine laboratories poses serious hazards for first responders, emergency services, investigators and the surrounding public due to the risk of exposure to volatile organic compounds (VOCs) used in the manufacture of illicit substances. A novel gas sampling interface using open microfluidic channels that enables the extraction of VOCs out of the gas phase and into a liquid, where it can be analysed by conventional detection systems, has recently been developed. This paper investigates the efficiency and effectiveness of such a gas-to-liquid (GTL) extraction system for the extraction of amphetamine-type substances (ATS) and their precursors from the vapour phase. The GTL interface was evaluated across a range of different ATS and their precursors (methamphetamine, dimethylamphetamine, N-formylmethamphetamine, benzaldehyde, phenyl-2-propanone, ephedrine and pseudoephedrine) at concentrations ranging between 10 and 32 mg m−3. These gas samples were produced by a gas generation system directly in Tedlar® bags and gas canisters for controlled volume sampling. When using gas sampled from Tedlar® bags, four of the seven compounds were able to be extracted by the GTL interface, with the majority of the VOCs having extraction yields between 0.005% and 4.5%, in line with the results from an initial study. When samples were taken from gas canisters, only benzaldehyde was able to be detected, with extraction efficiencies between 0.2% and 0.4%. A custom-built mount for the GTL interface helped to automate the extraction process, with the aim of increasing extraction efficiency or reducing variability. However, the extraction efficiency did not improve when using this accessory, but the procedure did become more efficient. The results from the study indicated that the GTL interface could be employed for the collection of gaseous ATS and incorporated into mobile detection systems for onsite collection and analysis of volatile compounds related to ATS manufacture.
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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 | 0.000 |
| 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) | 0.000 | 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 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".