A deconvolution protocol of the mechanical relaxation spectrum to identify and quantify individual polymer feature contributions to self-healing
Bibliographic record
Abstract
Starting from experimental macro-rheological data, we develop a fitting protocol that succeeded in the separation of the overlapping relaxation phenomena in the dissipative regime for a set of intrinsic healing polymers healing most effectively near their glass transition temperature Tg. To allow for a proper deconvolution, the rheological master curves are converted to a relaxation spectrum (H(τ)) and this is fitted using an optimized mechanical model, e.g. the Maxwell-Weichert model. The deconvolution of overlapping segmental mobility and reversible interactions is successfully demonstrated for a set of polyimide and polyamide polymers containing none, one and two reversible dynamic features near-Tg. Through the fitting parameters, the relaxation timescale of each feature and their apparent process enthalpies are obtained. The quantitative data obtained using the fitting protocol are then compared to macroscopic healing results. As a result, a clear correspondence between the energy stored by the system to accomplish reversible (e.g. H-bonds, π-π) and chain interdiffusion relaxation transitions and the healing efficiency of such polymers are obtained. The implementation of this protocol allows for a clearer identification of the relevant mechanisms in self-healing polymers and paves the way for the development of more efficiently healable polymeric systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".