Hitting the Jackpot – development of gas chromatography–mass spectrometry (GC–MS) and other rapid screening methods for the analysis of 18 fentanyl‐derived synthetic opioids
Bibliographic record
Abstract
Abstract In recent years, the occurrence of synthetic opioid fentanyl and its derivatives has grown significantly in forensic casework. This study presents the synthesis and analysis of 18 fentalogs, selected based on information received from local law enforcement. This study provides colorimetric tests, thin‐layer chromatography (TLC) which can potentially be utilized for presumptive screening of the target compounds, as bulk powders or as trace‐level adulterants. The fully validated confirmatory GC–MS method (employing SIM mode) allows the identification of the 18 derivatives, five commonly encountered controlled substances and four adulterants, within 20 minutes. The cross‐validated method described herein provides a sensitive screening and quantitation method for the illicit (and potentially harmful) components at trace levels (LOD = 0.007–0.822 μg/mL and LOQ = 0.023–2.742 μg/mL respectively). Spectral data [1H‐NMR, 13C‐NMR, 19F‐NMR, FT‐IR, and HRMS] and assignments for the synthesized reference materials are also provided in the Supplementary Information for laboratories engaged in the routine analysis of fentanyl and its derivatives.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".