Bibliographic record
Abstract
Abstract Auxetic foams have gained popularity within the research community because of their enhanced properties, such as low density combined with high relative stiffness, toughness, and damping properties. Low density polystyrene (PS) foams are commonly used in the packaging industry, but have a short service life and generate a high volume of waste (white pollution). This is why their recycling and valorizing is necessary and imperative. The objective of this work is to present a simple and environmentally friendly process combining low pressure (vacuum) and mechanical compression to convert recycled PS foams (15 kg/m3) into low density foams (50–63 kg/m3) having negative tensile and compressive Poisson's ratios (NPR). The effect of processing conditions (vacuum level, temperature, mechanical pressure, and time) were studied. Based on the optimized conditions, the tensile Poisson's ratio of the resulting auxetic foams reached −0.65 for the Y direction (width) and −0.74 for the Z direction (thickness) when stretching in the X direction (length). On the other hand, the minimum compressive Poisson's ratios were −0.32 for the Y direction and −0.28 for the Z direction. The foam structure was characterized via morphological analysis to determine the changes after treatment. Finally, tensile and compressive properties (Young's modulus, strain energy, energy dissipation, and damping capacity) are also discussed. It was observed that the mechanical properties of the resulting auxetic foams were improved compared to the original PS foam (PS‐O) in terms of resilience and strength. For example, the elongation at break of auxetic foams (31%–62%) were much higher compared to PS‐O (7%). These auxetic foams can be used in several applications, such as sports and military protective equipment.
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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) | 0.001 | 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".