Improvement of Hardening Stiffness Test as an Indicator of Environmental Stress Cracking Resistance of Polyethylene
Bibliographic record
Abstract
Long term mechanical behavior of polyethylene (PE) is of great importance especially in cases where structural integrity is required. In order to predict characteristics of the mechanical behavior of PE, it is necessary to fully understand the molecular structure of the employed resins. In this study, evaluation of several micromolecular properties of PE is conducted. These properties influence an important performance indicator of PE for structural applications, namely, the environmental stress cracking resistance (ESCR). ESCR in PE resins occurs through a slow crack growth mechanism under low applied stresses and long periods of time. This property is usually assessed by unreliable and time consuming testing methods such as the notch constant load test (NCLT) on notched PE specimens in the presence of an aggressive fluid at elevated temperatures. In the work presented herein, relationships between molecular structure and material response characteristics, mainly between molecular weight properties and short chain branching content in relation to strain hardening behavior of PE resins, were investigated based on results from tensile experiments. Inter-lamellar entanglements are believed to be the main feature controlling slow crack growth of PE. Extent of entanglements and entanglement efficiency has been investigated by monitoring the strain hardening behavior of PE resins in solid state through a uniaxial tensile test. The hardening stiffness (HS) test for prediction of ESCR was refined and improved to cover a broader range of PE resins, along with easier sample preparation, and faster testing. The improved test offers a more reliable and consistent ESCR picture without the drawbacks of the subjective notching process and ad-hoc presence of aggressive fluids.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".