Transforming Waste Polystyrene into High-Performance Porous Frames with Tunable Cellular Structures via Supercritical Nitrogen Foaming
Bibliographic record
Abstract
Due to white pollution-related sustainable development and environmental concerns, it is desirable to recycle the widely used plastic wastes, especially the nondegradable but versatile polystyrene (PS). In this work, the wasted PS particles are converted into a high value-added porous frame with tunable cellular structures via the eco-friendly supercritical nitrogen (scN 2 ) extruded foaming technology. Meanwhile, an interesting secondary cell growth phenomenon is found and elaborately explained when adjusting the gap of the rollers during the above process. Besides, the structure–function relationship among solid structures, cellular structures, and the pullout strength of the recycled PS (rPS) is analyzed. Furthermore, in order to optimize the structure design and study the effect of microstructure on its fracture mechanism in the screw pullout testing, three numerical models with different cellular structures are used to illustrate the propagation of cracks within the porous structures. Herein, it is indicated that the secondary cell growth can endow the rPS foams with a higher expansion ratio and open-cell content, but it is not conducive to improving the pullout strength of the materials due to the thin cell walls and low bending capacity. Through the combination of the experimental data and numerical simulation, this work develops a sustainable way to promote the recycling of waste PS into photo frames, mirror frames, decoration line, or other material applications, and provides innovative ideas for their structural design.
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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".