Polyaniline as a dual flame retardant and electrostatic dissipative additive in polyethylene nanocomposites
Bibliographic record
Abstract
Abstract Polyolefins, such as polyethylene (PE), are highly flammable and electrically insulative, limiting their applicability. The study explored the flame‐retardancy and electrical conductivity of PE/polyaniline (PE/PANI) nanocomposites containing undoped PANI, PANI doped, and co‐doped with various acids and PANI modified with a double layered hydroxide or ammonium polyphosphate (APP). The nanocomposites were synthesized through in situ chemical oxidative polymerization of aniline and compression molding. Flame retardancy was evaluated using UL 94 tests and cone calorimetry. All nanocomposites, except the de‐doped PANI nanocomposite, attained a UL 94 V2 rating. Cone calorimeter results showed that PANI doped with H 3 PO 4 reduced the peak heat release rate by 20% compared to neat PE, whereas co‐doping PANI with H 3 PO 4 and phytic acid reduced it by 31%. The nanocomposites exhibited volume resistivity for suitable for electrotactic dissipation applications but showed marginally reduced mechanical properties. This study demonstrates the potential to develop electrostatic dissipative and flame‐retardant PE nanocomposites incorporating PANI. Highlights PE/PANI nanocomposites were synthesized. PANI doped with H 3 PO 4 reduced the peak HRR by 20%. Co‐doping PANI with H 3 PO 4 and phytic acid further reduced peak HRR. Nanocomposites attained a UL 94 V2 rating. PE/PANI nanocomposites were electrostatic dissipative.
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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) | 0.000 | 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) | 0.001 | 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".