Advances in Fire Retardancy of Polymeric Nanocomposites and Applications
Bibliographic record
Abstract
Emerging advancement in nanotechnology have facilitated the embedment of nanomaterials (NMs) such as graphene and derivatives, carbon nanotubes and derivatives, nanowires, and so on, within polymeric matrices to attain enhanced properties, especially fire retardancy, in polymeric nanoarchitectures (PNC) for multifarious applications. In thermal interface materials (TIM) for electronic gadgets, notable fire hazards are often ignored, whereas PNC exhibiting electromagnetic interference (EMI) shielding are frequently subjected to accidental fires. Furthermore, fire warning sensors with capability of rapidly exposing fire dangers in combustible materials plays a key role in mitigating or entirely eliminating fire disasters in most scenarios. Moreover, the escalating evolution of electronic gadgets in the fifth-generation (5G) era has made superlative fire safety, thermal stability and high-performance of PNC highly imperative. Nanowires are one-dimensional (1-D) nanostructures possessing a high length to diameter aspect ratios, unique flame retardant (FR), mechanical, electrical, thermal, and optical properties. The inclusion of different forms of nanowires within polymeric matrices has tremendously enhanced the flame retardancy (F-R) of nanowire@polymeric nanoarchitectures (N-PNC) thereby enlarging their scope of applications. Therefore, this paper presents advances in flame retardancy of nanowire polymeric nanoarchitectures.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".