Organic Thin‐Film Transistors as Cannabinoid Sensors: Effect of Analytes on Phthalocyanine Film Crystallization
Bibliographic record
Abstract
Abstract With a growing international trend of Cannabis legalization, there is a present need for on‐the‐spot, low cost, and rapid detection of cannabinoids. Here, relationships between thin‐films of phthalocyanines (Pcs) with a variety of central, peripheral, and axial substituents and their response to the cannabinoid Δ 9 ‐tetrahydrocannabinol (THC), with and without a cannabinoid‐sensitive chromophore (Fast Blue BB) are investigated through organic thin‐film transistor (OTFT) performance. X‐ray diffraction and UV–vis absorption spectroscopy measurements demonstrate significantly altered film morphologies and the formation of new crystal orientations in response to analytes, which are corroborated by scanning electron microscopy. Electron paramagnetic resonance further corroborates shifting crystal structures in response to THC and also reveals the formation and promotion of Pc radical species through THC‐metal coordination. With exposure to THC, aluminum chloride Pc generates the largest physical film changes as well as the largest changes in OTFT performance. These findings suggest that the semiconductor thin‐film morphologies in Pc‐based OTFT sensors are not static in the presence of analytes and that the sensing response is driven both by strong analyte‐Pc coordination and bulk film restructuring to accommodate these interactions.
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How this classification was reachedexpand
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) | 0.003 | 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 teacher head, 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".