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 (R2= 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".