Extruded foams from recycled polystyrene containing thermal black particles and nanofibrillated cellulose fibrils for eco-responsible insulation applications
Bibliographic record
Abstract
This work explores the incorporation of thermal black particles and nanofibrillated cellulose fibrils, both individually and in hybrid combinations, as additives in recycled polystyrene foam production. Despite being one of the most widely used thermoplastics worldwide in the foams manufacturing industry, polystyrene is among the least recycled plastics. The foamability of a recycled polystyrene was investigated as a function of thermal black content, the cleanest and most eco-responsible form of carbon black from its industry, and of nano-fibrillated cellulose content, nanoscale building block in wood fibers’ microfibrils. Eco-responsible foams were obtained using carbon dioxide as a blowing agent, recognized for being environmentally friendly and not depleting the ozone layer. The characterization results are presented in terms of foam morphology, open cell content, foam density, expansion ratio, compression strength, thermal insulation, and acoustic insulation. These findings reveal that these eco-responsible extruded foams exhibit performance comparable to commercial counterparts. The incorporation of nanoscale carbon fillers and cellulose nanofibrils enhances the recycled polystyrene foams performance, creating opportunities for greener and more sustainable insulation and cushioning 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".