Microscopic signatures of yielding in concentrated nanoemulsions under large-amplitude oscillatory shear
Bibliographic record
Abstract
We report x-ray photon correlation spectroscopy (XPCS) experiments on a series of concentrated oil-in-water nanoemulsions with varying droplet volume fraction subjected to in situ steady-state large-amplitude oscillatory shear (LAOS). The shear strain causes periodic echoes in the x-ray speckle patterns that lead to peaks in the intensity autocorrelation function. Above an onset strain amplitude that depends on nanoemulsion concentration, the peaks become attenuated, signaling spatially heterogeneous, shear-induced droplet dynamics. These dynamics include irreversible rearrangements among the droplets that occur in some regions of the nanoemulsions during a given shear cycle and residual strain-like displacements in those regions that do not rearrange. The wave-vector dependence of the peak attenuation indicates a power-law distribution in the size of regions undergoing shear-induced rearrangement that is similar to that observed previously in LAOS-XPCS measurements on concentrated nanocolloidal gels. The values of the onset strains for rearrangement correlate with the concentration-dependent macroscopic yielding behavior of the nanoemulsions. Specifically, they occur below the strains at which the nanoemulsions become effectively fluidized and, except for the lowest-concentration nanoemulsion in the study, significantly above the threshold strain for nonlinear rheological response.
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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) | 0.000 | 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 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".