Organic Thin Film Transistor-Based Cannabinoid Sensors
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
The legalization of cannabis for medical and recreational use has created global legal markets, resulting in economic growth and job opportunities. Δ 9 -Tetrahydrocannabinol (THC) and the nonpsychoactive cannabidiol (CBD) are the primary bioactive compounds from the plant Cannabis sativa and sensors for their detection are vital for monitoring the effects on patients, understanding strain effects, and ensuring accurate potency information. Current detection methods require specialized facilities, making low-cost hand-held sensors desirable for public safety, regulatory compliance, and industry efficiency. Electrical sensors, such as organic thin-film transistors (OTFTs), offer advantages over optical sensors, and metal phthalocyanines (MPcs) show promise as an active semiconducting sensing material. Through both molecular interactions and thin film reorganization, MPc-based OTFTs have been demonstrated to enable the detection and differentiation of THC and CBD both in the vapor and solution. This spotlight article discusses recent advances in the discovery and optimization of MPc based THC and CBD OTFT sensors and highlights their promising future.
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) | 0.002 | 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