Thermal, mechanical, flammability properties, and morphologies of polypropylene/calcium carbonate‐based on carbon nanomaterials and layered silicates composites: A future perspective of new sheet material for flooring application
Bibliographic record
Abstract
Abstract Polyvinyl chloride/calcium carbonate (PVC/CaCO 3 ) composites are widely used in the construction industry to fabricate flooring materials. However, PVC materials have raised concern due to the large quantity of plasticizers introduced during PVC product processing. Studies have reported that volatile organic chemicals (VOCs) are toxic with side effects on human health and the environment. Therefore, polypropylene (PP) is proposed as an alternative polymer for developing calcium carbonate‐based polymer composite sheets for flooring applications with improved thermal, mechanical, and flammability properties. This work reviews the scientific and technological advances in the field of polypropylene/calcium carbonate–carbon nanomaterials or clay nanocomposites to develop a better understanding of how to develop superior nanocomposites for use in flooring applications. This work explores various studies about the synergistic effect of combining nanofillers (i.e., carbon nanomaterials or layered clays) with intumescent flame retardants and the synergist effect of nanofillers (i.e., multi‐walled carbon nanotubes/montmorillonite) without intumescent flame retardants on thermal, mechanical, flammability properties, and morphologies of polypropylene. The generated information will facilitate the design of flooring materials based on PP with improved thermal, mechanical, and flammability properties. Therefore, it was confirmed that there is a relationship between the morphology (dispersion state of nanofillers in the PP), and mechanical properties, as well as the flammability of PP composites. Highlights New advances in carbon nanomaterials and clays‐based PP/CaCO 3 composites The relationship between preparation, structure, property, and mechanisms The synergism effect of inorganic flame retardants, multifunctionality, and flooring application Challenges and future outlook on the development of PP/CaCO 3 /inorganic FR
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".