Experimental Comparison of the Effect of Microtalc and Nanoclay on the Structural Properties of Polyethylene Foams Fabricated by Rotational Molding
Bibliographic record
Abstract
Hypothesis: Polymeric foams have fascinating specific properties due to their cellular and porous structure and these properties have attracted much attention in industrial and scientific societies nowadays. The variations in cellular structure of polymeric foams including expansion ratio, cell density, and cell size may affect their final properties. For this reason, it seems necessary to study the effect of process parameters on the cellular structure of polymeric foams. On the other hand, the rotational molding method is developing rapidly compared to other processing methods due to its ability to fabricate large complex hollow parts Different process parameters can affect the structural properties of polymeric foams Utilizing nucleation agents as a material parameter improves the structural properties of polymeric foams significantly in different processing methods.Methods: The effect of adding two types of nucleating agents including talc microparticles and clay nanoparticles in different sizes on the structural properties of polyethylene foams made by rotational molding process was investigated Azodicarbonamide was used as the chemical blowing agent. Talc microparticles (1% by weight) and clay nanoparticles were added to polyethylene and foamed using rotational molding process. Cell density, cell size, and expansion ratio were investigated as structural properties.Findings: The findings revealed that the effect of talc microparticles on increasing cell density and decreasing cell size was more significant than the effect of clay nanoparticles. Cell density of polyethylene foam was improved by 96% and 89% by adding 1% (by weight) of talc microparticles and clay nanoparticles, respectively. A 20% and 17.5% decrease in the cell size of polyethylene foam was also observed with the addition of 1% (by weight) of talc microparticles and clay nanoparticles respectively.
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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) | 0.002 | 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".