Evaluation of Doped Polyaniline as Potential Sensing Materials and/or Absorbents For Styrene and Phthalates in Aqueous Solutions
Bibliographic record
Abstract
Abstract The removal and detection of toxic analytes, including monomers and plasticizers, is important for both health and the environment. Potential absorbents and/or sensing materials are successfully made from polyaniline (PANI) and PANI doped with 10 wt.% of aluminum oxide (PANI‐Al 2 O 3 ) and titanium dioxide (PANI‐TiO 2 ). The amount of metal oxide incorporated into the surface layers of the polymer and the total amount incorporated are confirmed by energy dispersive X‐rays (EDX) and microwave plasma‐ atomic emission spectroscopy (MP‐AES), respectively. All three materials are evaluated as potential sensing materials and/or absorbents for styrene, monobutyl phthalate (MBP), and dibutyl phthalate (DBP) at 100 ppm in aqueous solutions. It is found that incorporating TiO 2 into PANI improved both the sensitivity and selectivity to styrene, making PANI‐TiO 2 a good absorbent and potential sensing material for styrene. Additionally, incorporating Al 2 O 3 into PANI improved the sorption to MBP, but reduced the selectivity. As a proof‐of‐concept, the responses of the polymeric nanocomposites are combined and analyzed using principal component analysis (PCA) as such an algorithm. The output reveals good separation of the responses to each analyte, thereby demonstrating how sensing materials with poorer selectivity can be used together to overcome limitations in selectivity.
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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".