Evaluation of Zinc Borate and Melamine Polyphosphate Synergy on the Rheological, Thermal, Mechanical, Dimensional, and Flame Retardant Performance of <scp>rLLDPE</scp> / <scp>CaCO</scp> <sub>3</sub> Composites for Pipes Applications
Bibliographic record
Abstract
ABSTRACT The use of recycled thermoplastics is crucial for addressing environmental challenges such as waste management and sustainability. Recent upcycling trends have focused on enhancing recycled materials for advanced applications, such as flame‐retardant pipes used in underground mining. To improve the flame‐retardant properties of recycled LLDPE (rLLDPE) and expand its potential applications, flame‐resistant rLLDPE/CaCO 3 composites containing melamine polyphosphate (MPP) and zinc borate (ZB) were developed using a melt‐mixing method. The contents of MPP and ZB in the polymer were varied at 5 and 10 wt% respectively, to evaluate their individual and combined effects. The results showed improvements in flame retardancy, with higher char content, leading to a considerable improvement in the Limiting Oxygen Index (LOI) from 18.2% for neat rLLDPE to 23.7% for the MPP/ZB combination, achieving a UL‐94 V‐1 rating. The rLLDPE/CaCO 3 /MPP5/ZB10 composite exhibited a 52% increase in elastic modulus and a 10% increase in tensile strength, along with low water absorption (< 0.07%) and good dimensional stability. The synergistic effect of MPP and ZB improved the flame‐retardant performance by forming a dense and compact physical barrier that stopped heat transmission and suppressed flame spread. This research contributes to the development of upcycled materials with enhanced flame‐retardant properties for industrial applications including pipe fabrication.
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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".