Bibliographic record
Abstract
Abstract Microcellular plastics are typically thermoplastic polymers with a large number (∼billions per cm3) of tiny bubbles (of the order of 10 μm in diameter). Their densities can range from 3% to 95% of the solid polymer depending on the volume taken by the bubbles (which are denoted as cells in this paper). In general, microcellular plastics exhibit superior impact strength, toughness, fatigue life, thermal stability, dielectric strength, thermal and acoustical insulation performance, as well as optical properties, relatively to the solid counterparts. Other advantages of microcellular plastics include higher productivity due to its faster processing times and sink‐mark free injection‐molded parts with no residual stress and high dimensional stability. Owing to these unique properties, there are a large number of applications of microcellular plastics, particularly in automotive industries. This article summarizes the science and technology of microcellular plastics. To be specific, their history, science (ie, generation of polymer–gas solution, cell nucleation, growth, deterioration and stabilization), solid‐state processing technologies, continuous processing technologies (ie, extrusion and injection molding), design guidelines of each processing technologies, as well as the properties and application of microcellular plastics, are discussed in detail.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".