Electrochemical Determination of Fentanyl Using Carbon Nanofiber-Modified Electrodes
Bibliographic record
Abstract
In this work, we report the direct electrochemical oxidation of fentanyl using commercial screen-printed carbon electrodes (SPCEs) modified with carboxyl-functionalized carbon nanofibers (fCNFs). CNFs have surface chemistry and reactivity similar to carbon nanotubes (CNTs), yet they are easier to produce and are of a lower cost than CNTs. By monitoring the current produced during the electrochemical oxidation of fentanyl, variables such as fCNF loading, fentanyl accumulation time, electrolyte pH, and differential pulse voltammetry parameters were optimized. Under an optimized set of conditions, the fCNF/SPCEs responded linearly to fentanyl in the concentration range of 0.125-10 μM, with a limit of detection of 75 nM. The fCNF/SPCEs also demonstrated excellent selectivity against common cutting agents found in illicit drugs (e.g., glucose, sucrose, caffeine, acetaminophen, and theophylline) and interferents found in biological samples (e.g., ascorbic acid, NaCl, urea, creatinine, and uric acid). The performance of the sensor was also successfully tested using fentanyl spiked into an artificial urine sample. The straightforward electrode assembly process, low cost, ease of use, and rapid response make the fCNF/SPCEs prime candidates for the detection of fentanyl in both physiological samples and street drugs.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".