Delta-9-tetrahydrocannabinol (Δ<sup>9</sup>-THC) sensing using an aerosol jet printed organic electrochemical transistor (OECT)
Bibliographic record
Abstract
Recreational use of marijuana/cannabis was legalized in Canada in 2018 and has been decriminalized in several other countries; however, the detection of impairment has remained elusive for law enforcement. The psychoactive ingredient in cannabis, delta-9-tetrahydrocannabinol (Δ9-THC), can be detected in saliva and be correlated well with the intake of cannabis. Organic electrochemical transistors (OECTs) have been used for a variety of biosensing applications like glucose, pH, ions, etc. In this work, we demonstrate the use of unfunctionalized OECTs for the detection of Δ9-THC down to 0.1 nM and 1 nM diluted in DI water and synthetic saliva buffer, respectively. These OECTs have been aerosol jet printed entirely with PEDOT:PSS as the channel material. Using a platinum gate coupled with an aerosol jet printed OECT, Δ9-THC concentration can be detected due to its oxidation reaction at the gate. These results were consistent with cyclic voltammetry measurements of Δ9-THC using Pt as the working and counter electrode. Utilizing these OECT based sensors, we have achieved high sensitivity of detection of Δ9-THC in the range from 0.1 nM to 5 μM. These OECT based Δ9-THC sensors demonstrate less than 3% error indicating good repeatability which is averaged over 15 measurements on multiple devices.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".