XPCS Investigation of the Dynamics of Filler Particles in Stretched Filled Elastomers
Bibliographic record
Abstract
The complexity of the mechanical behavior of filled elastomers can be partly attributed to the fact that the duration of an applied strain plays a crucial role. In order to bring new insights into this still incompletely solved problem, we look for relationships between the macroscopic mechanical relaxation and the relaxation of the filler particles at the nano- to mesoscale. To this end, X-ray photon correlation spectroscopy (XPCS) in homodyne and heterodyne configurations combined with tensile stress relaxation is employed. The paper is devoted to the study of the role of the filler–filler and the filler–matrix interactions in a cross-linked elastomer on the aging mechanisms under strain. The fillers investigated are carbon black, as an example of strong filler–matrix interactions, and hydroxylated silica for which the filler–filler interaction is strong (H-bonds). Homodyne XPCS correlation reveals features of jammed systems (compressed exponential and ballistic motion) for both systems. The exponents characterizing the aging of the homodyne relaxation times are not the same in the carbon black and in the silica filled samples. For both systems, the decrease of the particle velocity determined by heterodyne detection with aging time follows a power law. The silica sample is characterized by a slow decrease of the velocity during aging. For the carbon black sample, the velocity remains small and decreases faster than for the silica sample. The reverse is observed for the behavior of the tensile force.
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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".