Conversion of Polypropylene (PP) Foams into Auxetic Metamaterials
Bibliographic record
Abstract
In this work, a simple and environmentally friendly process combining low pressure (vacuum) and mechanical compression is proposed to convert recycled polypropylene (PP) foams (28 kg/m3) into low density foams (90–131 kg/m3) having negative tensile and compressive Poisson’s ratios (NPR). The main objective of the work was to determine the effect of processing conditions (vacuum time, temperature and mechanical pressure). Based on the optimized conditions, the tensile Poisson’s ratio of the resulting auxetic foams reached −1.50, while the minimum compressive Poisson’s ratio was −0.32 for the same sample. The foam structure was characterized via morphological analysis (SEM) to determine any changes related to the treatment applied. Finally, the tensile and compressive properties (Young’s modulus, strain energy, energy dissipation and damping capacity) are also presented and discussed. It was observed that the mechanical properties of the resulting auxetic foams were improved compared to the original PP foam (PP-O) for all tensile properties in terms of modulus (19.9 to 59.8 kPa), strength (0.298 to 1.43 kPa) elongation at break (28 to 77%), energy dissipation (14.4 to 56.3 mJ/cm3) and damping capacity (12 to 19%). Nevertheless, improvements were also observed under compression in terms of the energy dissipation (1.6 to 3.6 mJ/cm3) and the damping capacity (13 to 19%). These auxetic foams can find applications in sport and military protective equipment, as well as any energy mitigation system.
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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) | 0.000 | 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".