Surface engineering of zinc phthalocyanine organic thin-film transistors results in part-per-billion sensitivity towards cannabinoid vapor
Bibliographic record
Abstract
Abstract Phthalocyanine-based organic thin-film transistors (OTFTs) have been demonstrated as sensors for a range of analytes, including cannabinoids, in both liquid and gas phases. Detection of the primary cannabinoids, Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD), is necessary for quality control and regulation, however, current techniques are often not readily available for consumers, industry, and law-enforcement. The OTFT characteristics, X-ray diffraction (XRD) spectra, and grazing incident wide angle x-ray scattering (GIWAXS) spectra of two copper and three zinc phthalocyanines, with varying degrees of peripheral fluorination, were screened to determine sensitivity to THC vapor. Unsubstituted ZnPc was found to be the most sensitive material and, by tuning thin-film morphology, crystal polymorphs, and thickness through altered physical vapor deposition conditions, we increased the sensitivity to THC by 100x. Here we demonstrate that deposition conditions, and the resulting physical film characteristics, play a significant role in device sensitization.
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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 | 0.000 |
| 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) | 0.001 | 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".