Enhancing mechanical performance of polystyrene carbon composite foams through supercritical CO <sub>2</sub> foaming: An experimental study
Bibliographic record
Abstract
Conventional polystyrene (PS) foams are widely used in packaging and insulation, but suffer from limited mechanical strength, which restricts their use in load-bearing applications. This study investigates the enhancement of the mechanical performance of PS foam through the incorporation of graphene nanoplatelets (GNP) and flaked graphite (FG), processed via supercritical CO 2 (sc-CO 2 ) extrusion foaming at two pressures (17.3 MPa and 20.6 MPa). The influence of sc-CO 2 pressure on additive dispersion and mechanical behavior was evaluated using compression testing, dynamic mechanical analysis (DMA), micro-computed tomography (micro-CT). Results showed that increasing the sc-CO 2 pressure significantly enhanced compressive strength from 0.30 MPa to 0.40 MPa for 0.75 wt% GNP foams (34% increase) and from 0.30 MPa to 0.50 MPa for 0.75 wt% FG foams (66% increase). Enhanced storage and loss moduli in DMA confirmed improvements in stiffness and energy dissipation. Micro-CT imaging revealed more well-defined closed-cell structures and uniform carbon particle dispersion at the higher pressure. Overall, these findings emphasize the importance of pressure-optimized sc-CO 2 foaming as an effective strategy for producing lightweight, durable PS-carbon composite foams suitable for structural, insulation, and packaging applications.
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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".