Thin Film Composite Conductive Polymers Chemiresistive Sensor and Sample Holder for Methanol Detection in Adulterated Beverages
Bibliographic record
Abstract
An inexpensive methanol sensor lid fabricated from a composite conductive polymer comprised of cellulose nanocrystals, carbon nanotubes and polyanilines (PANI@CNC/CNT) has been demonstrated. The sample holder composed of an inexpensive tube with Kanthal resistive heating for preferential methanol headspace sampling. The PANI@CNC/CNT nanoporous aerogel film has inherent ultra-high surface area, with the CNT/PANI free valence electrons affording a high electrical conductive material. The PANI@CNC/CNT methanol sensor performance was tested for its response to methanol, ethanol standards and methanol/ethanol mixtures. The sensor was 46 times more sensitive and selective to methanol compared to ethanol, and afforded a linear response in the 0-7% methanol concentration range, deviating from linearity above 10%. The sensor was also used to test methanol standards spiked in wine, yielding a linear (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.9842) resistance change in response with methanol concentrations in the range, 0-7%. The sensor precision for triplicate standards for different concentrations (0.5-5.0%) averaged ~4.5%, indicating overall reliability of the device. With sample heating and detection time fixed at 10 mins, the sensor demonstrated optimal performance with a limit of detection of 0.30% for methanol. The sensor was tested for over 24 runs over a one-month period without significant loss in performance due to degradation. multiple uses without degradation in performance. The PANI@CNC/CNT has been found effective for as a dosimetric sensor for use in indoor air quality monitoring and beverage adulteration analysis.
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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) | 0.000 | 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 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".