Hybrid Matrix Using Polyester Resin to Improve the Physical and Mechanical Properties of Recycled Expanded Polystyrene Matrix
Bibliographic record
Abstract
In this study, an innovative hybrid matrix based on recycled expanded polystyrene (EPS), dissolved in gasoline, and polyester resin (PR) is developed.The aim is to improve the physical and mechanical properties of the dissolved EPS, used previously by the authors as a matrix in composite materials.A rate of 20, 30, 40, 50, and 60 wt% of PR were added in the EPS/PR hybrid matrix.Two processes were used for drying the hybrid matrix, namely ambient temperature (ATDP) and thermal drying process (ThDP) at 60C° .Pycnometer and three-point bending measurements with a characterization by scanning electron microscopy (SEM) were carried out in order to determine: the bulk density, the flexural modulus, and the maximum stress and also to observe the EPS/PR morphology.The results showed that the addition of PR increases the physical properties of the EPS/PR hybrid matrix in both drying processes with a drastic improvement of 3 to 6 times in the mechanical properties.Using ThDP at 60℃ makes the matrix lighter and more resistant even at 20 wt% of PR than using ATDP.Otherwise, using ThDP can increase the density from 7 to 16% and improve the tensile strength from 11 to 64% of the dissolved EPS.It was concluded that the elaboration of the innovative hybrid matrix EPS/PR with ThDP at 60℃ improves the physical and mechanical properties of the EPS.Thus, the authors recommend the use of solar flat plate collectors as a renewable drying process.
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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".