Synergistic flame retardancy and electrical conductivity in di-glycidyl ether of bisphenol-A epoxy composites with polyaniline and aluminum Tri-hydroxide
Bibliographic record
Abstract
Abstract This study focuses on developing and characterizing multifunctional composites based on the diglycidyl ether of bisphenol-A (DGEBA) epoxy matrix. The aim is to enhance fire resistance and electrical conductivity properties for applications in various fields. To achieve this, aluminum tri-hydroxide (ATH) was incorporated as a flame retardant (FR) agent, while polyaniline (PANI) was added to impart electrical conductivity. The composites were categorized into three groups: the first containing flame retardant (FR), the second containing PANI for conductivity, and the third containing both PANI and FR for combined effects. E 60-FP emerged as the optimal multifunctional composite, exhibiting superior mechanical properties among the tested formulations. Thermogravimetric analysis (TGA) results provided valuable insights into the thermal stability of E 60-FP, revealing that it retained 42% of its initial mass at a temperature of 600 °C. Additionally, the composite achieved a V-0 rating in the UL 94 test, confirming its excellent fire resistance. Notably, E 60-FP displayed impressive mechanical strength, with a tensile strength of 7.2 MPa and a tensile modulus of 1117.6 MPa. Its flexural strength and modulus were measured at 31.2 MPa and 2800.2 MPa, respectively. Furthermore, the composite E 60-FP exhibited remarkable electrical conductivity, measuring 6.1 × 10–6 S cm−1. These findings highlight the potential of DGEBA epoxy composites containing PANI and ATH as promising materials for applications requiring fire resistance and electrical conductivity properties.
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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".