Transition in the Glassy Dynamics of Melts of Acid-Hydrolyzed Phytoglycogen Nanoparticles
Bibliographic record
Abstract
The deformability, responsiveness, and tunability of soft nanoparticles (NPs) offer unique opportunities to learn about their complex properties and the interactions between particles. In the present study, we provide new insights into the physical properties of phytoglycogen (PG) NPs, which are soft, compact particles with a dendritic architecture that are produced in the kernels of sweet corn. In particular, we study PG NPs modified using acid hydrolysis, which not only reduces their diameter but also alters their stiffness, internal structure, and the interactions between particles in aqueous dispersions. We used steady shear rheology to determine the dependence of the relative zero-shear viscosity ηr of aqueous dispersions of acid-hydrolyzed PG NPs on the effective volume fraction ϕeff, which indicated a reduction in stiffness of the particles relative to that of native PG NPs. We quantified this difference by analyzing the nature of the colloidal glasses formed at high ϕeff. We measured a smaller value of the fragility index m for acid-hydrolyzed PG NP glasses than that for native PG NP glasses, indicating that acid-hydrolyzed PG NPs form stronger glasses and are therefore softer than native PG NPs. Unlike the native PG NPs, we observed a distinctive change in the character of the glass transition of the acid-hydrolyzed PG NPs as ϕeff was increased above ϕeff∼1: a crossover in the dependence of ηr on ϕeff from Vogel–Fulcher–Tammann behavior to a more gradual, Arrhenius-like behavior. By expressing the steady shear and oscillatory rheology data in terms of generalized Péclet numbers, we obtained collapse of the data onto master curves. We interpret this result in terms of the acid-hydrolyzed PG NPs predominantly interpenetrating neighboring particles at large ϕeff, for which fluctuations of the outer chains enhance the mobility of the particles and make α-relaxation times τα experimentally accessible.
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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 | 0.001 | 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".