Bulk and surface mechanical properties of clay modified HDPE used in liner applications
Bibliographic record
Abstract
Abstract High‐density polyethylene (HDPE)/clay nanocomposites were prepared by melt blending process. The HDPE was mixed with different organoclays and polyethylene‐grafted‐maleic anhydride was used as a compatibiliser. A masterbatch procedure was used to obtain final organoclays concentrations of 1, 2.5 and 5 wt%. The effects of various types of nanoclays and their concentrations on morphological, thermal and mechanical properties of nanocomposites were investigated. Surface mechanical properties such as instrumented nanohardness, modulus of elasticity and creep were also measured using a nanoindentation technique. Young's, storage and loss moduli, were found to be higher than that of the neat polymer at low loading (2.5 wt%) for clay Cloisite 15A and at higher loading (5 wt%) for clay Nanomer 1.44P. The ultimate strength and the toughness decreased slightly compared to pure HDPE. The differential scanning calorimetry analysis revealed that the peak temperature of the nanocomposites increased with increased clay content while the crystallinity decreased. Also, dynamic mechanical analysis revealed the storage and loss moduli are enhanced by addition of nanoclay. Both mechanical and thermal properties of HDPE/Nanomer 1.44P nanocomposite showed interesting trends. All properties first dropped when 1 wt% of the clay was added. Thereafter, a gradual increase or decrease then followed as the loading of Nanomer was increased. These trends were observed for all mechanical properties. The results obtained from nanoindentation tests for surface mechanical properties also showed similar trend to that of bulk measurements. Based on these measurements a nanoclay additive for a liner grade HDPE was selected. © 2011 Canadian Society for Chemical Engineering
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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 | 0.000 | 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) | 0.000 | 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".