Effect of nanoclay addition on the foaming behavior of linear polypropylene‐based soft thermoplastic polyolefin foam blown in continuous extrusion
Bibliographic record
Abstract
Abstract The goal of this study is to fabricate a soft foam with high cell density and high foam expansion based on thermoplastic polyolefin (TPO), and to supply a potential candidate for soft thermoset foams. It was found that a linear polypropylene (PP)‐based TPO foam exhibited very poor cell morphology because of its weak melt strength. Nanoclay was introduced into the TPO to improve its foaming behavior. The extrusion foaming experiments demonstrated that the introduction of 0.5 wt% nanoclay significantly increased the cell morphology and expansion ratio of TPO/clay nanocomposite foams due to the enhanced cell nucleation and the possible cell coalescence suppression at low temperatures. At a high nanoclay content of 2.0 wt%, the cell density and foam expansion of foams increased continuously compared with the 0.5 wt% nanoclay addition. Our study suggested that it was possible to produce soft TPO foams with a well‐defined cell structure and high foam expansion using a continuous method, but that a proper PEOc content control was needed to maximize foam expansion. POLYM. ENG. SCI., 2011. ©2011 Society of Plastics Engineers.
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 | 0.001 |
| 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".