Synergistic Effects of Carbon Additives and Supercritical <scp>CO<sub>2</sub></scp> on Cell Morphology and Thermal Insulation of Extruded Polystyrene Composite Foam
Bibliographic record
Abstract
ABSTRACT Polystyrene (PS) foams with carbon additives are widely used in building applications due to their excellent thermal insulation properties, contributing to reduced energy consumption. However, the role of supercritical CO2 (sc‐CO2) as a blowing agent, in synergy with different forms of carbon additives, remains not fully understood in the context of PS extrusion foaming. In this study, composite foams were prepared by incorporating graphene nanoplatelets (GNP) and flaked graphite (FG) into PS using an extrusion foaming process. Results show that sc‐CO2 pressure plays a critical role in enhancing carbon dispersion, thereby significantly influencing foam morphology. At high sc‐CO2 pressure, the cell density of PS foam increased by up to two‐fold with a relatively low carbon loading (0.75 wt%). Moreover, the thermal conductivity of PS composite foam with 1.5 wt% FG was reduced by 6%, reaching a value of 32 mW/(m K). Transmission electron microscopy (TEM) confirmed a uniform dispersion of carbon particles in foams produced at elevated sc‐CO2 pressures. This study proposes a viable processing strategy for developing carbon‐reinforced PS composite foams with enhanced thermal insulation for energy‐efficient building 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) | 0.001 | 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".