Morphological Analysis of Foamed HDPE/LLDPE Blends by X-ray Micro-Tomography: Effect of Blending, Mixing Intensity and Foaming Temperature
Bibliographic record
Abstract
Non-invasive x-ray micro-computed tomography was employed for thorough quantitative and qualitative analysis of the cellular structure of foams made of linear low density polyethylene (LLDPE), high density polyethylene (HDPE) and their blends. Special emphasis was given to the differences between the results of 3D and 2D analyses, to evaluate the possible errors while studying the morphology using conventional 2D techniques (e.g. SEM). Blends with the weight compositions of 90%LLDPE/10%HDPE and 75%LLDPE/25%HDPE were produced at different rotor speeds of 10, 60 and 120 rpm and batch foaming was examined over a wide range of temperature. The void fraction values from 2D and 3D analysis were found to agree well with those obtained with the Archimedes method. Results showed more uniform cell size distribution for blends mixed at the lower spectrum of screw rotational speed. Among the blends with higher void fraction values and relatively uniform cellular structure, higher average cell size (3–30%) and cell population density (1.25–2.5 times) were noticed in 3D analysis compared with 2D data. The micro-CT images at different cross sections revealed anisotropic cell growth and more elongated cells along the thickness of the specimen. It was also observed that, with increase in foaming temperature, cell shrink prevailed over cell coalescence in the samples with lower viscosity (prepared at low rpm of 10), while for those with higher viscosity (prepared at an rpm of 60) cell coalescence was more dominant.
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 | 0.001 | 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".