Chemistry, Processing, Properties, and Applications of Rubber Foams
Bibliographic record
Abstract
With the ever-increasing development in science and technology, as well as social awareness, more requirements are imposed on the production and property of all materials, especially polymeric foams. In particular, rubber foams, compared to thermoplastic foams in general, have higher flexibility, resistance to abrasion, energy absorption capabilities, strength-to-weight ratio and tensile strength leading to their widespread use in several applications such as thermal insulation, energy absorption, pressure sensors, absorbents, etc. To control the rubber foams microstructure leading to excellent physical and mechanical properties, two types of parameters play important roles. The first category is related to formulation including the rubber (type and grade), as well as the type and content of accelerators, fillers, and foaming agents. The second category is associated to processing parameters such as the processing method (injection, extrusion, compression, etc.), as well as different conditions related to foaming (temperature, pressure and number of stage) and curing (temperature, time and precuring time). This review presents the different parameters involved and discusses their effect on the morphological, physical, and mechanical properties of rubber foams. Although several studies have been published on rubber foams, very few papers reviewed the subject and compared the results available. In this review, the most recent works on rubber foams have been collected to provide a general overview on different types of rubber foams from their preparation to their final application. Detailed information on formulation, curing and foaming chemistry, production methods, morphology, properties, and applications is presented and discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".