A Polydopamine-Based Molecularly Imprinted Electrochemical Sensor for Fentanyl Determination
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide A molecularly imprinted polymer (MIP)-based electrochemical sensor for the rapid detection of fentanyl is reported. The sensor was prepared by electrochemically grafting polydopamine on a carbon nanofiber–Pt nanoparticle composite-modified screen-printed electrode. Dopamine was identified as a suitable functional monomer via in-silico modeling and was electropolymerized via cyclic voltammetry in the presence of fentanyl to form the MIP sensor. The properties and morphology of the sensing material were characterized with spectroscopy, microscopy, and electrochemical techniques. Factors influencing the sensor performance were studied and optimized. Under optimized conditions, the MIP sensor response followed the Langmuir–Freundlich binding isotherm with a dissociation constant ( k d ) of 16.13 μM and a limit of detection of 0.094 μM fentanyl. The sensor displayed good run-to-run repeatability and batch-to-batch performance reproducibility with relative standard deviations of 6.7% ( n = 5) and 9.1% ( n = 3), respectively. Three sensors, prepared and tested in parallel, showed excellent storage stability in a fridge under a humidified environment for 4 weeks with relative standard deviations of ≤10%. The developed MIP sensor presented suitable selectivity when interrogated with solutions composed of equimolar concentrations of fentanyl and glucose, acetaminophen, theophylline, morphine, naloxone, codeine, or norfentanyl. The sensor was also successfully tested in artificial urine samples, indicating that it is a promising candidate as a rapid testing method in fentanyl investigation.
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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) | 0.001 | 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 | 0.001 |
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".