Mechanical relaxations in heat‐aged polycarbonate. Part I: Comparison between two molecular weights
Bibliographic record
Abstract
Abstract As part of a wider research program related to polycarbonate embrittlement, the effects of heat‐aging on mechanical relaxation behavior have been studied by examining the relationship between secondary transitions and stress relaxation behavior. In this Part I, the differences in response between two molecular weight polycarbonates (PC) are compared for injection molded samples. Dynamic mechanical spectra showed that the presence of an intermediate β transition (∼ 80°C) is strongly dependent on molecular weight and heat‐aging. However, the β 1 (35°C) and the γ (‐100°C) peaks are generally insensitive to either effect. The study also attempted to interpret the similarities and differences in relaxation response using free volume and conformational change arguments, which have been subject to much scrutiny. Using the KWW stretched exponential to characterize stress relaxation, it appeared that bulk free volume recovery concepts could explain differences in stress relaxation response but not the corresponding losses in toughness. Hence, it is proposed that changes in relaxation response are most likely due to an interplay of relatively large scale molecular volume and molecular conformation processes that affect inter molecular cooperativity. These high‐activation processes are related to the broad β region (β 1 < T < T g ).
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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 | 0.000 | 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) | 0.002 | 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".