Tribological and morphological properties of bentonite nano‐clay/<scp>CaCO<sub>3</sub></scp> reinforced high‐density polyethylene nanocomposites
Bibliographic record
Abstract
Abstract Elastomeric polymers such as high‐density polyethylene, have a variety of desirable features, that have supplanted traditional materials. However, high‐density polyethylene (HDPE) shows inadequate wear resistance, which limits its use for industrial applications, particularly in low‐load‐bearing applications such as flexible energy harvesting devices and sensors. The current work is engrossed in investigating the influence of hybrid reinforcements CaCO3 particles and bentonite nano clay as secondary reinforcements in high‐density polyethylene (HDPE)‐based composites on the wear and friction properties. The reinforcements were melt compounded with HDPE using a Brabender mixer and sampled using an injection molding machine. The wear test (ASTM G‐99‐04) was performed by a pin‐on‐disk tribo‐tester. In comparison to a base matrix, the synthesized hybrid composite achieved the maximum improvement in wear rate of 93%. The results revealed that there is a significant improvement in wear resistance. Morphological analysis revealed that due to the encapsulation and compatibilization effect of bentonite nano clay the hybrid composite exhibited improved wear performance. The results signify the synergistic effect of filler particles resulted in sufficient bonding for stress transfer due to the encapsulation of CaCO3 by nano clay. The wear mechanism observed optically was abrasion, fatigue, and adhesion wear that changed with the change in the weight percent of nanoparticles. Finally, the prepared composite with enhanced tribological properties such as low wear rate, low friction coefficient, and enhanced morphology can be used in low load‐bearing wear applications such as turbo nanogenerators and piezo nanogenerators. Highlights Bentonite nano clay and CaCO3 dispersed homogeneously in HDPE matrix. Wear resistance of HDPE increases by reinforcing particles (nano‐clay and CaCO3). Micro‐cutting, deformations, and particle husks were the possible wear mechanism. Encapsulation effects the hybrid composite to exhibit improved wear performance.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".