Molecularly Imprinted Electrochemical Sensor for Naloxone Detection
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».