Thermo-Photonic Detection of THC in Oral Fluid: Transition from Bench-Top System to Handheld Device
Bibliographic record
Abstract
Cannabis is one of the most widely used illicit drug worldwide. Delta-9-tetrahydrocannabinol (THC) is the main intoxicating ingredient in cannabis that impair a person's ability to drive. While operating a motor vehicle while impaired by drug is prohibited, the problem of drug-impaired driving has become more prevalent in recent years [1] . In addition, workplace safety field studies suggest that acute intoxication from cannabis smoking significantly impair employees' performance, leading to increased risks of work-related accidents [2] . Therefore, there is currently an unmet need for rapid, accurate and on-site screening devices for cannabis consumption detection in both workplaces and law enforcement. To address this need, detection of THC in oral fluids using affinity chromatography technologies such as lateral flow immunoassays (LFAs) has been recently explored. While LFA technologies offer simple, portable and non-invasive detection of THC from oral fluid, their limit of detection is normally limited to greater than 25 ng/ml which is insufficient for proper enforcement of per se regulations. In this work, we present an alternative method of detecting THC in LFAs which enhances the limit of detection by over an order of magnitude. Unlike conventional LFA detection/reader technologies that are based on the scattering of light by immobilized gold nanoparticles (GNPs), our innovation explores the thermal signatures of GNPs in response to modulated laser illumination [3] - [4] . Our results (n=240) suggest that the demodulation of localized surface plasmon resonance responses of GNPs captured by infrared cameras allows for detection of THC concentrations as low as 2 ng/ml with an accuracy of 95%. In order to demonstrate the viability of our technology for commercialization, we have also constructed a portable prototype using low-cost ($250) cellphone attachment infrared cameras with which detection cut off of 2ng/ml has been achieved.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".