Evaluation of polymeric nanocomposites for the detection of toxic gas analytes
Bibliographic record
Abstract
Four different metal oxide nanoparticles, copper oxide (CuO), aluminum oxide (Al2O3), nickel oxide (NiO), and titanium dioxide (TiO2), were added to poly (2,5-dimethyl aniline) (P25DMA) during synthesis to create different polymer nanocomposites. These polymer nanocomposites were evaluated as potential sensing materials for six different gas analytes (acetaldehyde, acetone, benzene, ethanol, formaldehyde, and methanol). It was found that CuO did not incorporate into the P25DMA and only a small percentage of Al2O3 was incorporated. However, both NiO and TiO2 were incorporated into the P25DMA at the same concentration as during the synthesis step. Overall, the type of metal oxide significantly affected the morphology of the sensing material and the amount of each analyte sorbed. For example, P25DMA doped with 5 wt% Al2O3 had high selectivity towards ethanol, whereas P25DMA doped with 20 wt% TiO2 sorbed the most ethanol. However, P25DMA doped with 20 wt% TiO2 also sorbed a high amount of formaldehyde, making P25DMA doped with 20 wt% TiO2 less selective than P25DMA doped with 5 wt% Al2O3 towards ethanol with respect to formaldehyde.
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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".