Properties of Sustainable Composite Construction Materials Derived from Recycled Polymers and Nanofillers
Bibliographic record
Abstract
This paper presents the investigation of sustainable construction composites manufactured from rPET and rHDPE with the addition of nano-silica, graphene oxide, and nanoclay. The composites were fabricated by melt blending and compression molding. Mechanical, thermal, and durability performances of the composites were tested according to ASTM specifications. Experimental test results revealed that with an optimum loading of 3 wt% nanofillers, there is an enhancement in tensile and flexural strength by up to 35%, improvement of thermal stability by 20-25 °C, and a reduction of water absorption by about 25% compared to unreinforced polymers. SEM, FTIR, and XRD analyses confirm enhancement in interfacial bonding and refinement of microstructure. Compressive strength in the range of 38 to 43 MPa was obtained, which indicates that these materials are suitable for lightweight panels and non-structural elements. The results are in agreement with previously reported literature data and emphasize the possibility of recycled polymer-nanofiller composites to provide low-density durable construction material with an environmental benefit. The engineering-oriented outcome of this study focuses on the optimization of filler dispersion and performance to enable scalable and sustainable application.
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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 | 0.001 | 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".