Linking interfacial work of deformation from deconvoluted macro-rheological spectrum to early stage healing in selected polyurethanes
Bibliographic record
Abstract
The use of rheology and terminal flow relaxation times to predict healing behavior at long healing times is by now quite well accepted. In this work we go one step further and explore the use of macro-rheology (in particular the stored work of deformation) to predict the early stage interfacial healing properties (fracture resistance) of a set of self-healing polyurethanes. The interfacial healing is measured by single edge notch fracture experiments, using short healing times and a low healing temperature to exclude the effect of long range molecular motion on mechanical properties restoration. The systems based on aromatic diisocyanates show high fracture resistance after healing, while very limited restoration of the mechanical properties is observed for aliphatic and cycloaliphatic based polyurethanes. Linear sweep rheology and time-temperature-superposition allow obtaining the macro-rheological master curve and the mechanical relaxation spectra (H(t)). The application of a recently established deconvolution protocol to the H(t) gives the characteristic relaxation times and stored works of deformation associated to individual dynamic processes such as segmental motion, reversible bonds, and terminal flow. It is found that the calculated stored works of deformation related to the reversible bond relaxation reproduce the trend observed by fracture resistance at healed interfaces and reveal a qualitative correspondence between reversible bonds work of deformation and interfacial healing fracture resistance. Moreover, the method seems to point to the existence of a threshold interfacial work of deformation below which no efficient load transfer can be observed.
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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".