Influence of Sawdust Particles Reinforcement on Physical and Mechanical Properties of High-Density Polyethylene (HDPE) Matrix Composites
Bibliographic record
Abstract
The influence of sawdust particles reinforcement on the physical and mechanical characteristics of high-density polyethylene (HDPE) matrix composites was studied for application as sustainable wood plastic composites (WPCs) for housing. The WPCs developed by compression moulding method were characterised. The results revealed web-like structures/cross-linking in the microstructure of the samples, which is a characteristic of polymers. The microstructure revealed a good dispersion of sawdust particles and compatibilizer in the HDPE matrix and bonding, which enhanced the properties of the composites. The control sample C exhibited water absorption of 0.22 % whereas sample S8 having 1.1 to 1.4 mm sawdust particles, 30 wt. % of sawdust particles content, and 3 wt. % of compatibilizer exhibited the least water absorption of 0.14 %. The unreinforced HDPE control sample exhibited a tensile strength of 12.53 MPa while sample S7 with the smallest size (less than 1 mm) and 30 wt. % of sawdust particles content, and 7 wt. % of compatibilizer exhibited the highest tensile strength of 16.22 MPa. This is 29.5 % higher than that of the control sample. The control sample exhibited a flexural strength of 10.2 MPa while sample S7 exhibited the highest flexural strength of 14.85 MPa, which is 45.6 % higher than that of the control sample. The control sample exhibited a hardness value of 13.93 HV while sample S7 exhibited the highest hardness value of 19.17 HV, which is 37.6 % greater than that of the control sample. Samples S5, S7, S8, and S9, which contained high content of sawdust particles demonstrated impact energy values of 34.27, 33.14, 35.17, and 36.46 J respectively. The unreinforced control sample demonstrated a low wear rate value of 0.35 g/Nm. However, sample 7 demonstrated the least wear rate of 0.23 g/Nm, which is 34.3 % lower than that of the control sample. In view of these characteristics, the composites especially sample 7, has the potentials for application as a sustainable building material.
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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".