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Record W3025727821 · doi:10.1149/ma2020-01271959mtgabs

Towards the Electrochemical Detection of Cannabis Drugs in Human Saliva

2020· article· en· W3025727821 on OpenAlexaffabout
Margaret Renaud‐Young, April Woods, Vajihe Salehi, M. Golędzinowski, Felix J. E. Comeau, Justin L. MacCallum, Viola Birss

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsAlcohol Countermeasure Systems (Canada)University of Calgary
Fundersnot available
KeywordsChemistryElectrochemistrySupporting electrolyteDetection limitDrug detectionGlassy carbonElectrodeVoltammetryCyclic voltammetryChromatography

Abstract

fetched live from OpenAlex

With the recent legalization of cannabis in Canada, the sensitive and selective detection of Δ9-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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0020.002

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.

Opus teacher head0.011
GPT teacher head0.224
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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