Molecularly Imprinted Electrochemical Sensor for Naloxone Detection
Bibliographic record
Abstract
Introduction Naloxone, (5α)-4,5-epoxy,3,14-dihydroxy17(2-propenyl) morphinan-6-one, (Figure 1) is a synthetic opioid receptor antagonist mainly used for the treatment of opioid overdose and to reduce constipation caused by orally administered opioid therapy [1]. A number of analytical methods have been reported for the detection of naloxone, mainly by high performance liquid chromatography [2], high performance liquid chromatography coupled with mass spectrometry [3] and chemiluminescence [4]. However, the methods are costly, time consuming, produce large amounts of liquid waste which is not environment friendly, and not appropriate for field use. Electrochemical sensor platforms, however, are attractive for handheld detection and field use due to their low sample volume requirement, simplicity and compactness. In this work, we report on the development of simple, yet sensitive and selective electrochemical sensor for naloxone detection using molecular imprinted polymer (MIP) and screen printing electrodes. Method The MIP preparation was carried out via in situ electropolymerization of a solution composed of the functional monomer, p-phenylenediamine (pPD), and the template (naloxone) in phosphate citrate buffer at pH 6 on a screen printed carbon electrode that was modified with reduced graphene oxide (rGO) and gold nanoparticle (AuNPs) (Figure 2). Several parameters controlling the preparation and performance of the MIP sensor (including pH, the molar ratio between monomer and template molecules, the cycle number of electropolymerization, and incubation time of the modified electrode on the sensing performance) were studied and optimized. After electropolymerization, naloxone molecules were removed from the MIP using methanol/HCl solution to generate binding sites that were complimentary in size, shape and functionality to naloxone molecules for later detection. Non-imprinted polymer (NIP) modified electrodes were prepared using the optimized procedure but in the absence of naloxone to examine the selectivity of the MIP sensor. The electrochemical behavior of naloxone at MIP and NIP sensors was evaluated by differential pulse voltammetry. Results and Conclusions The morphology and properties of the sensing material were characterized with scanning electron microscopy, Raman spectroscopy, and atomic force microscope. Under an optimized condition, the MIP electrochemical sensor responded linearly to naloxone concentration between 0.5 μM to 8 μM, with a detection limit of 0.23 μM. The introduction of rGO and AuNPs hybrid materials significantly improved the sensor’s performance. The selectivity of the MIP sensor towards naloxone was examined using morphine, naltrexone and noroxymorphone as interferents. The result of the selectivity experiment showed that the imprinted electrode has a good response and selectivity towards naloxone. To further demonstrate the potential of the developed MIP-based naloxone sensor for practical applications, the sensor was tested for the detection of naloxone in spiked urine samples. Recoveries of up to 97.0% were recorded, demonstrating the reliability and accuracy of the sensor for naloxone detection in bodily fluids. References [1] M. Liu and E. Wittbrodt, J. Pain Symptoms Manage, 2002, 23, 48-53. [2] D. A. Michels, M. Parker and O. Salas-Solano, Electrophoresis, 2012, 33, 815–826. [3] R. M. Vicente, N. Z. Fernández et al, J. Pharmaceutical and Biomedical Analysis, 2015, 114, 105–112. [4] N. A. Alarfaj, and M. F. El-Tohamy, Chemistry Central Journal, 2015, 9, 1-9. Figure 1
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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) | 0.001 | 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) | 0.002 | 0.002 |
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".