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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".