Conversion of low density polyethylene foams into auxetic metamaterials
Bibliographic record
Abstract
Abstract Cellular polymers, such as polyethylene foams, are commonly used in the packaging industry. These materials have short service life and generate a high volume of waste after use. In order to valorize this waste and produce added‐value applications, it is proposed to convert these materials into highly efficient energy absorption structures. This was done by modifying the original cellular morphology of the foams (spheroidal or polygonal) into a re‐entrant structure to produce auxetic materials. This work presents an optimized process combining mechanical compression and solvent vapor evaporation‐condensation leading to low density foams (77–200 kg/m3) having negative Poisson's ratios (NPR). Three series of recycled low density polyethylene (LDPE) foams with an initial density of 16, 21, and 36 kg/m3were used to optimize the processing conditions in terms of treatment temperature, time, and pressure. From all the samples prepared, a minimum Poisson's ratio of −3.5 was obtained. To further characterize the samples, the final foam structure was analyzed to relate with mechanical properties and compare with conventional foams having positive Poisson's ratios. The results are discussed using tensile properties and energy dissipation which were shown to be highly improved for auxetic foams. Overall, the resulting foams can be used in several applications such as sport and military protection 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".