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Glassy and Jammed Systems: Structures and Dynamics

This dissertation studies structure and dynamics across colloidal glasses, 3D rod packings, and oil-in-water emulsion rheology, with oil–water surface tension (Σ) used to nondimensionalize yield stress in the emulsion analysis.

At-a-Glance Summary

Primary surface measurement reported

Oil–water surface tension (Σ) is used to nondimensionalize yield stress as a function of volume fraction in the emulsion rheology chapter (Figure 4.7, panel b).

Dropometer attribution in the paper

Oil–water surface tension (Σ) appears as the interfacial normalization term in the yield-stress presentation described for Figure 4.7.

How the surface-tension / contact-angle data were used in the study

Surface tension (Σ) is used as a scaling quantity for yield stress versus volume fraction, presented alongside an alternative nondimensionalization by thermal energy (k_B T) in the same figure entry (Figure 4.7).

Paper Details

Title
Glassy and Jammed Systems: Structures and Dynamics
Authors
Cong Cao
Journal
Emory University (PhD dissertation)
Year
2020
License
Non-exclusive license granted to Emory University to archive, make accessible, and display the dissertation (Distribution Agreement).

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What Was Measured

Primary surface / interfacial measurement

Oil–water surface tension (Σ) is used as a normalization factor for yield stress versus volume fraction in the emulsion rheology chapter’s figure descriptions (Figure 4.7, panel b).

Supporting measurements

The dissertation describes confocal microscopy measurements of colloidal-glass aging near rough versus smooth boundaries, x-ray tomography measurements of boundary effects in 3D rod packings, and rheometer-based shearing experiments on oil-in-water emulsions with yield-stress behavior analyzed versus volume fraction. Emulsion imaging is also described in figure entries using optical microscopy and SEM.

Role of the Dropometer

Oil–water surface tension (Σ) is used as the nondimensionalization term for yield stress plotted versus volume fraction in the emulsion rheology figure description (Figure 4.7, panel b), presented alongside a thermal-energy-based nondimensionalization (k_B T) in panel (a).

The Figure 4.7 entry notes that the legend identifies droplet diameter (d) and the source of the plotted data.

Method Snapshot

Method Snapshot Table

System / series (as described) Sample / composition (as stated) Key variables reported Surface / interfacial term reported Measurement outputs described Instruments Conditions (as stated) Figure(s)
Emulsion imaging (monodisperse) Monodisperse emulsions (silicone oil + water) obtained by fractionation; mean diameter ~2 µm Mean droplet diameter (~2 µm) — Micrograph of monodisperse emulsions Leica DM IRB research microscope - Fig. 4.1
Emulsion / particle imaging Polystyrene particles (d = 2 µm); swelled emulsions (average diameter ~6 µm) Particle diameter (2 µm); emulsion diameter (~6 µm) — Micrographs of particles and swelled emulsions Leica DM IRB research microscope - Fig. 4.2
Emulsion imaging (polymerized) TPM emulsions polymerized by AIBN; monodisperse d = 1.16 µm; bidisperse mean diameters d_small = 1.06 µm and d_large = 1.86 µm Droplet diameters (1.16 µm; 1.06/1.86 µm) — SEM images of polymerized TPM emulsions SEM - Fig. 4.3
Emulsion rheology (flow curves) Monodisperse emulsions (2.04 µm; 1.16 µm) and bidisperse emulsions (1.06/1.86 µm) Droplet diameter; volume fraction (φ); shear stress (σ); strain rate (γ̇) — σ vs γ̇ curves labeled by φ; fits with Herschel–Bulkley model and TC model; guidelines indicating transition between samples with and without yield stress Rheometer - Fig. 4.4
Emulsion rheology (fit parameters) 1.16/2.03 µm samples Volume fraction (φ); fit parameters (n, η_bg, γ̇_c, σ_y) — HB and TC model fitting parameters vs φ (including regions described for parameter sensitivity) Rheometer (data source for fits) - Fig. 4.5
Emulsion rheology (yield stress vs φ) Emulsions labeled by mean droplet diameter (example values referenced in figure entry include d = 1.03 µm and d = 2.03 µm) Volume fraction (φ); yield stress (σ_y); droplet diameter (d); effective temperature (T_eff) (as described in figure entry) — σ_y vs φ (experimental, labeled by mean d), with simulation results shown in panel (b) (as described) Rheometer (experimental); simulation results (as referenced) - Fig. 4.6
Emulsion rheology (normalized yield stress) Yield stress plotted vs volume fraction (φ), with droplet diameter (d) indicated in the legend Volume fraction (φ); droplet diameter (d) Oil–water surface tension (Σ) (nondimensionalization); thermal energy (k_B T) (alternative nondimensionalization) Yield stress vs φ with nondimensionalization by k_B T (panel a) and by Σ (panel b) Rheometer (yield stress source referenced across Chapter 4 figures) - Fig. 4.7

Key Findings

Wall-induced layering tracks with slowed dynamics in colloidal glass

Near smooth boundaries, particles form layers and motion is dramatically slower near the boundary compared to the bulk; near rough boundaries, layers nearly vanish and particle motion is nearly identical to the bulk. The abstract links gradients in dynamics near boundaries to gradients in structure for both boundary types.

Rod packings show boundary-dependent volume fraction changes

Rod packings in smaller cylindrical containers have lower volume fractions than those in larger containers, and x-ray tomography results show boundary effects that depend on boundary orientation. The abstract states the boundary influence extends approximately half a particle length into the interior.

Emulsions exhibit yield-stress behavior and droplet-size-dependent transition signatures

For oil-in-water emulsions, the abstract describes solid-like behavior with a yield stress above a critical volume fraction (φ). It reports both glass and jamming transitions for smaller diameter droplets and only a jamming transition for larger diameter droplets, with the bidisperse sample behaving similarly to the small-droplet sample.

Yield stress is presented with interfacial and thermal normalizations

The List of Figures description for Figure 4.7 reports yield stress versus volume fraction plotted with nondimensionalization by thermal energy (k_B T) in panel (a) and by oil–water surface tension (Σ) in panel (b), with the legend indicating droplet diameter (d) and the source of the data.

Rheological model fitting is used to evaluate data collapse behavior

The abstract states the emulsion rheology data are fit using both the Herschel–Bulkley model and a Three-Component model, and that the raw rheological data would not collapse into a master curve based on the fitting parameters.

Figures & Visuals

Figure 4.7 — Interfacial (Σ) and thermal (k_B T) normalization of yield stress

What it shows

Shows yield stress as a function of volume fraction with panel (b) nondimensionalized by oil–water surface tension (Σ) and panel (a) nondimensionalized by thermal energy (k_B T), with droplet diameter (d) and data source identified in the legend.

Figure 4.4 — Flow curves across droplet sizes with model fits

What it shows

Plots shear stress (σ) versus strain rate (γ̇) for monodisperse (2.04 µm; 1.16 µm) and bidisperse (1.06/1.86 µm) emulsions, labeled by volume fraction (φ), with Herschel–Bulkley and TC model fits.

Figure 4.6 — Yield stress versus volume fraction with droplet-diameter labeling

What it shows

Presents yield stress (σ_y) as a function of volume fraction (φ) for experimental data labeled by mean droplet diameter, with simulation results described in the panel (b) entry.

Why It Matters

In the emulsion rheology chapter, yield stress is presented as a function of volume fraction with a nondimensionalization using oil–water surface tension (Σ) (Figure 4.7b), alongside a thermal-energy-based nondimensionalization (k_B T) (Figure 4.7a). The figure entry also indicates that droplet diameter (d) and the data source are part of the figure legend.

Across the broader dissertation scope, the abstract connects structure–dynamics relationships near boundaries in colloidal glasses, boundary effects in rod packings, and droplet-size-dependent transition behavior in emulsions, concluding that liquid–solid transitions may depend on particle type.

Practical Takeaways

Use Σ-based normalization when presenting emulsion yield stress

The emulsion chapter includes yield stress versus volume fraction presented in nondimensional form using oil–water surface tension (Σ) (Figure 4.7b).

Pair normalized yield-stress plots with droplet-size labeling

The Figure 4.7 entry states that droplet diameter (d) is indicated in the legend, along with the source of the data.

Track yield-stress onset using flow-curve families and fits

Flow curves (σ vs γ̇) are shown for multiple droplet sizes and volume fractions, with Herschel–Bulkley and TC model fits described for those datasets (Figure 4.4).

Expect droplet-size-dependent transition behavior in emulsion rheology

The abstract reports both glass and jamming transitions for smaller diameter droplets and only a jamming transition for larger diameter droplets, with the bidisperse sample behaving similarly to the small-droplet sample.

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