Gas Sensing With Polymeric Materials: Improved Sensitivity and Selectivity for Acetaldehyde and Formaldehyde
Bibliographic record
Abstract
ABSTRACT Selective detection of volatile organic compounds (VOCs) remains a critical challenge in environmental monitoring and industrial safety. This study investigates the sensitivity and selectivity of four pristine polymeric backbones—polyaniline (PANI), polypyrrole (PPy), polyvinylpyrrolidone (PVP), and polythiophene (PTh)—for the detection of acetaldehyde (Ac) and formaldehyde (F). Among these, PANI demonstrated significant sensitivity to both gases, making it a promising candidate for detecting F. Conversely, PPy and PVP exhibited pronounced sensitivity and selectivity for Ac, making them suitable for applications requiring selective Ac sensing. PTh, with negligible sorption of both analytes, can serve as a material to indicate a sensor baseline. Modifications to the PANI backbone, including poly(o‐anisidine) (POANI), poly(N‐methyl aniline) (PNMA), and poly(2,5‐dimethyl aniline) (P25DMA), were also evaluated. While these derivatives improved Ac sensitivity, they reduced F sensitivity due to altered electrostatic interactions. Among these, P25DMA displayed a relatively higher selectivity for F, although it still needs further refinements. Binary and ternary gas mixture analyses were conducted to simulate real‐world scenarios with multiple VOCs, revealing PPy and PVP as optimal materials for Ac detection, and P25DMA as a good material for detection of F. Mechanistic insights indicate that electrostatic interactions and polymer morphology significantly influence sorption behavior. This study underscores the potential of tailored polymeric materials for specific gas sensing applications and reports notable selectivity achievements for gas sensing polymers for detecting structurally and functionally diverse analytes.
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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) | 0.001 | 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 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".