Straightforward Synthesis and Evaluation of Polymeric Sensing Materials for Acetone Detection
Bibliographic record
Abstract
Abstract Three polymeric materials (polyaniline, polypyrrole, and poly(methyl methacrylate)) are selected, prepared, and evaluated for potential use in acetone sensing (for possible diabetes‐related applications). Of the materials studied, polyaniline and polypyrrole show the most promise. Polypyrrole allows for more acetone sorption (i.e., higher concentration of acetone sorbed), but does not distinguish between different target analytes (that is, it is not selective). A material's ability to distinguish between several gas analytes simultaneously (in a gas mixture) is rarely evaluated; selectivity is typically based on a “one‐analyte‐at‐a‐time” investigation. However, comparison of acetone sorption (in one experimental test) and interferent sorption (in a complementary experimental test) does not consider interactions that might occur between gas analytes; this motivates the sorption analysis of gas mixtures that is shown in this work. The most promising results are obtained when polyaniline or polypyrrole is exposed to acetone‐rich gas mixtures with low amounts of acetaldehyde, ethanol, and benzene (interferent gases). Polymer doping using three metal oxides (SnO 2 , WO 3 , and ZnO) is also investigated, but metal oxide addition has a limited effect on the sorption performance. This is true for all three metal oxides, regardless of the amount of doping (over the range studied; up to 20 wt%).
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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".