Thermographic detection and quantification of THC in oral fluid at unprecedented low concentrations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With recent changes in the legalization of cannabis around the world, there is an urgent need for rapid, yet sensitive, screening devices for testing drivers and employees under the influence of cannabis at the roadside and at the workplace, respectively. Oral fluid lateral flow immunoassays (LFAs) have recently been explored for such applications. While LFAs offer on-site, low-cost and rapid detection of tetrahydrocannabinol (THC), their nominal detection threshold is about 25 ng/ml, which is well above the 1-5 ng/ml per se limits set by regulations. In this paper, we report on the development of a thermo-photonic imaging system that utilizes the commercially available low-cost LFAs but offers detection of THC at unprecedented low concentrations. Our reader technology examines photothermal responses of gold nanoparticles (GNPs) in LFA through lock-in thermography (LIT). Our results (n = 300) 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 96% accuracy. Quantification of THC concentration is also achievable with our technology through calibration.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it