Towards the Electrochemical Detection of Cannabis Drugs in Human Saliva
Notice bibliographique
Résumé
With the recent legalization of cannabis in Canada, the sensitive and selective detection of Δ 9 -tetrahydrocannabinol (THC) in human saliva in a point-of-contact sensor is of great interest, with electrochemical detection having the potential to provide rapid, reliable, and low cost road-side testing capabilities. THC contains a phenol group and is believed to undergo electro-oxidation by a similar mechanism as has been reported for phenols ( 1 – 3 ). Other groups have developed methods to analyze THC electrochemically. Balbino et al . ( 1 , 2 , 4 ), have analyzed the drug in organic electrolyte solutions to overcome the challenges of the poor solubility of THC, while the Compton group developed a carbon paste electrode system that adsorbs the drug from sample solution, followed by square wave voltammetry measurements in aqueous solutions ( 3 ). In our recent work, we have developed a novel, reproducible, and sensitive electrochemical method for the detection of minute amounts of THC and/or its metabolites that were aliquot-deposited onto carbon paper electrodes, followed by drying and electrochemical oxidation in a pH 10 buffer solution ( 5 ). We showed that the electrochemistry was consistent with the oxidation of a surface-confined species, based on the charges passed and the sweep rate dependence of the currents, with the charge passed equating to 0.2 electrons/THC molecule for a wide range of drug quantities deposited on the carbon surface. The most sensitive detection limits of THC as well as the metabolites, 11-hydroxy-tetrahydrocannabinol (OH-THC) and the non-psychoactive 11-nor-9-carboxy-tetrahydrocannabinol (COOH-THC)) were obtained using cyclic voltammetry (CV) and square wave voltammetry (SWV), while the relatively high background current observed during chronoamperometry tended to interfere with detection. Using cyclic voltammetry, we were able to detect THC and OH-THC at levels as low as 2.5 pmol, while with square wave voltammetry (SWV), the detection limit was 1 pmol of drug. However, the COOH-THC metabolite was found to be more difficult to detect, giving a detection limit of 5 pmol by CV and 2 pmol by SWV. These differences may be related to varying drug solubilities, different reaction mechanisms or products, different modes of surface adsorption and/or surface orientations of the deposited drugs, or from lateral interactions between neighboring molecules on the carbon surface ( 6 – 8 ). In this presentation, we will report on the electrochemistry of THC, its metabolites and cannabidiol (CBD) in solutions of varying pH in order to better understand their different responses and also to attempt to selectively detect each of these molecules. A pH range of 2 to 12 was examined, using a Britton-Robinson buffer in 100 mM KCl. In all cases, we observed a slope of ~60 mV/pH point, indicating a 1:1 ratio of protons:electrons in the oxidation reaction, similar to what has been reported previously for phenol ( 9 ). However, COOH-THC produced a significantly higher Faradaic efficiency at low vs. high pH solutions. This may indicate that the different functional groups within these molecules alter how they adsorb to the carbon electrode surface, which may also influence the intermolecular interactions under different pH conditions. To evaluate our sensor performance in a real-world environment, recent work has been carried out with drug spiked into artificial saliva. Instead of depositing the drug (dissolved in methanol) directly onto the carbon paper, we have compared electrochemical oxidation by either submerging the electrode in artificial saliva followed by analysis in a buffered electrolyte in a separate cell (electrode transfer), to electrochemical testing for cannabis materials in a saliva solution combined with buffering agents ( in situ ). The results have been very promising, showing that we can obtain reproducible and sensitive detection of THC and its metabolites in artificial saliva, taking us one step closer to a practically realizable road-side THC sensor. References: 1. M. A. Balbino et al. , Forensic Sci. Int. 221 , 29–32 (2012). 2. M. A. Balbino et al. , J. Forensic Sci. 61 , 1067–1073 (2016). 3. R. Nissim, R. G. Compton, Chem. Cent. J. 9 (2015), doi:10.1186/s13065-015-0117-0. 4. M. A. Balbino et al. , J. Solid State Electrochem. 20 , 2435–2443 (2016). 5. M. Renaud-Young et al. , Electrochimica Acta . 307 , 351–359 (2019). 6. O. Alévêque et al. , Electrochem. Commun. 11 , 1776–1780 (2009). 7. O. Alévêque et al. , Electrochem. Commun. 12 , 1462–1466 (2010). 8. O. Alévêque, C. Gautier, M. Dias, T. Breton, E. Levillain, Phys. Chem. Chem. Phys. 12 , 12584 (2010). 9. C. Costentin, C. Louault, M. Robert, J.-M. Saveant, Proc. Natl. Acad. Sci. 106 , 18143–18148 (2009).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».