Analysis on the Go: Quantitation of Drugs of Abuse in Dried Urine with Digital Microfluidics and Miniature Mass Spectrometry
Bibliographic record
Abstract
We report the development of a method coupling microfluidics and a miniature mass spectrometer, applied to quantitation of drugs of abuse in urine. A custom digital microfluidic system was designed to deliver droplets of solvent to dried urine samples and then transport extracted analytes to an array of nanoelectrospray emitters for analysis. Tandem mass spectrometry (MS/MS) detection was performed using a fully autonomous 25 kg instrument. Using the new method, cocaine, benzoylecgonine, and codeine can be quantified from four samples in less than 15 min from (dried) sample to analysis. The figures of merit for the new method suggest that it is suitable for on-site screening; for example, the limit of quantitation (LOQ) for cocaine is 40 ng/mL, which is compatible with the performance criteria for laboratory analyses established by the United Nations Office on Drugs and Crime. More importantly, the LOQ of the new method is superior to the 300 ng/mL cutoff values used by the only other portable analysis systems we are aware of (relying on immunoassays). This work serves as a proof-of-concept for integration of microfluidics with miniature mass spectrometry. The system is attractive for the quantitation of drugs of abuse from urine and, more generally, may be useful for a wide range of applications that would benefit from portable, quantitative, on-site analysis.
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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) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".