A Simple Method to Convert Cellular Polymers into Auxetic Metamaterials
Bibliographic record
Abstract
The objective of this study was to present a simple and environmentally friendly process combining low pressure (vacuum) and mechanical compression to convert low-density polyethylene (LDPE) foams into low-density foams (76–125 kg/m3) with negative tensile and compressive Poisson’s ratios (NPR). As a first step, four series of recycled LDPE foams (electronics packaging) with starting densities of 16, 21, 30 and 36 kg/m3 were used to determine the effect of different processing conditions including temperature and pressure. Based on the optimized conditions, the tensile and compressive Poisson ratios of the resulting auxetic foams reached −2.89 and −0.66, while the tensile and compressive modulus of the auxetic foams reached 40 kPa and 2.55 kPa, respectively. The foam structure of the samples was characterized via morphological analysis and was related to the mechanical properties before and after the treatment (i.e., foams with positive and negative Poisson’s ratios). The tensile and compressive properties (Young’s modulus, strain energy, energy dissipation and damping capacity) for these auxetic foams were also discussed and were shown to be highly improved. These auxetic foams can be applied in sports and military protective equipment. To the best of our knowledge, there is only one report on vacuum being used for the production of auxetic foams.
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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 | 0.001 |
| 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".