Evaluating the effect of environmental conditions on high compressive strain rates in unfilled and filled neoprene rubbers
Bibliographic record
Abstract
Elastomers are known for their strain-rate-dependent properties not only to quasistatic but also to high strain rate deformations, where mechanical behavior is significantly affected by inertia. Concurrently, environmental changes, such as temperature and humidity variations, can impact their stress response to deformation. This study investigates the effects of material layers within neoprene samples on mitigating these environmental changes. While the presence of an intermediate layer proves effective against temperature and humidity influence, it fails to block the impact of increasing high strain rates. Moreover, the different humidity levels at room and elevated temperatures do not significantly alter the mechanical behavior of filled neoprene samples compared to pure neoprene. Notably, in unfilled neoprene, an increase in humidity levels, other than an absolutely dry environment, leads to a notable stress level rise at room temperature, while under elevated temperature conditions, there is a significant stress decrease with increasing humidity. However, neoprene filled with polyester/cotton or nylon displays resilience to diminishing mechanical behavior under various temperature and humidity regulations, indicating that the material layer within these samples effectively "protects" the rubbers from potential stress lapses observed in unfilled neoprene. While a high strain rate compression affects the behavior of the filled variants significantly, increasing humidity and temperature have minimal impact on their stress levels. These findings offer valuable insights into the dynamic responses of elastomers to environmental changes, highlighting the advantages of using filled rubbers in diverse applications.
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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".