Development of a Graphene-Oxide-Deposited Carbon Electrode for the Rapid and Low-Level Detection of Fentanyl and Derivatives
Bibliographic record
Abstract
The opioid overdose crisis in North America worsened during the COVID-19 pandemic, with multiple jurisdictions reporting more deaths per day due to the fentanyl-contaminated drug supply than COVID-19. The rapid quantitative detection of fentanyl in the illicit opioid drug supply or in bodily fluids at biologically relevant concentrations (i.e., <80 nM) remains a significant challenge. Electroanalytical techniques are inexpensive and can be used to rapidly detect fentanyl, but detection limits need to be improved. Herein, we detail the development of an electrochemical-based fentanyl analytical detection strategy that used a glassy carbon electrode modified with electrochemically reduced graphene oxide (ERGO) via electrophoretic deposition. The resulting surface was further electrochemically reduced in the presence of fentanyl to enhance the sensitivity. Multiple ERGO thicknesses were prepared in order to prove the versatility and ability to fine-tune the layer to the desired response. Fentanyl was detected at <10 ppb (<30 nM) with a limit of detection of 2 ppb and a calibration curve that covered 4 orders of concentration (from 1 ppb to 10 ppm). This method was sensitive to fentanyl analogues such as carfentanil. Interference from the presence of 100-fold excess of other opioids (heroin, cocaine) or substances typically found in illicit drug samples (e.g. caffeine and sucrose) was not significant.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".