Investigation of Shear Induced Relaxation in Soft Materials Pubblico
Chen, Dandan (2010)
Abstract
Abstract
Investigation of Shear Induced Relaxation in Soft Materials
By Dandan Chen
The study of how soft materials response to shear is important for
food
industry and drug delivery. However, the basic questions are
still
unsolved, due to the difficulty of seeing inside samples as they
are
sheared. Here we study plastic changes of soft materials by
using
advanced microscopes and particle-tracking techniques. From
the
simple systems like colloids (solid particles in a liquid) and
emulsions
(oil droplets in water), we try to understand the two primary
phenomena of shear-induced "plasticity" in soft materials:
micro-
dynamics and stress fluctuations.
In my colloidal experiment, I focus on the micro-dynamics of
colloidal
particles under large shear strain. By using fast confocal
microscopy,
we can observe and track colloids in a 3D movie. From their
trajectories we quantify the plastic rearrangements of the
particles in
several ways. Each of these measures of plasticity reveals
spatially
heterogeneous dynamics, with localized regions where many
particles
are strongly rearranging by these measures. We examine the shapes
of
these regions and find them to be essentially isotropic, with
no
alignment in any particular direction. Furthermore, individual
particles
are equally likely to move in any direction, other than the overall
bias
imposed by the strain.
In my emulsion experiment, I go further to study the
connection
between macroscopic stresses and microscopic dynamics. We use
the
2D emulsion disks to study the plastic changes of dense
materials
passing through a hopper channel. We find that under different
flux
rates, the flow profiles in the hopper are very similar. To
quantify the
plastic rearrangements, we focus on specific neighbor
rearrangements
called "T1 events". In addition, from the deformed shapes of
droplets,
we quantify the interactions between droplets. We find large
temporal
fluctuation of stresses in a large scale. From the micro-dynamics,
we
find the temporal changes of stresses are directly related to the
T1
rearrangements. Our analysis of this emulsion system shows a
direct
and local relationship between rearrangements and stress
fluctuations.
Table of Contents
Contents
1 Introduction 1
1.1 Soft material . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . 1
1.2 Jamming . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . 2
1.3 Spatially heterogeneous dynamics . . . . . . . . . . . . . . .
. . . . . 4
1.4 Force chains in jammed systems . . . . . . . . . . . . . . . .
. . . . . 6
2 Experimental techniques on colloids 14
2.1 Colloid . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 14
2.1.1 What is a colloid . . . . . . . . . . . . . . . . . . . . . .
. . . 14
2.1.2 Phase transition of hard-sphere system . . . . . . . . . . .
. . 15
2.2 Confocal microscope . . . . . . . . . . . . . . . . . . . . . .
. . . . . 16
3 Computer programming on colloids 20
3.1 Colloid identification algorithm . . . . . . . . . . . . . . .
. . . . . . 20
3.2 Tracking algorithm . . . . . . . . . . . . . . . . . . . . . .
. . . . . . 22
4 Microscopic structural relaxation in a sheared supercooled
colloidal
liquid 26
4.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 27
4.2 Experimental details . . . . . . . . . . . . . . . . . . . . .
. . . . . . 29
4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 32
4.3.1 Locally observed strain . . . . . . . . . . . . . . . . . . .
. . . 32
4.3.2 Individual particle motions . . . . . . . . . . . . . . . . .
. . . 35
4.3.3 Defining local plastic deformation . . . . . . . . . . . . .
. . . 45
4.3.4 Collective particle motions . . . . . . . . . . . . . . . . .
. . . 49
4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 58
5 Experimental techniques for studying emulsions 61
5.1 Producing emulsion samples . . . . . . . . . . . . . . . . . .
. . . . . 61
5.2 Microfluidic chip . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . 63
5.3 Bright-field microscope . . . . . . . . . . . . . . . . . . . .
. . . . . . 65
6 Computational techniques for studying emulsions 68
6.1 Emulsion droplet identification algorithm . . . . . . . . . . .
. . . . . 68
6.2 Neighbor identification algorithm . . . . . . . . . . . . . . .
. . . . . 69
6.3 Neighbor switch algorithm . . . . . . . . . . . . . . . . . . .
. . . . . 72
7 Stress flucatuations in a 2D frictionless flow through a hopper
75
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 75
7.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 77
7.2.1 Flow profile . . . . . . . . . . . . . . . . . . . . . . . .
. . . . 77
7.2.2 Rearrangements . . . . . . . . . . . . . . . . . . . . . . .
. . . 83
7.2.3 Stress fluctuations . . . . . . . . . . . . . . . . . . . . .
. . . 85
7.2.4 Stress fluctuations vs. T1 event . . . . . . . . . . . . . .
. . . 92
7.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . 98
8 Summary and outlook 100
8.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . 100
8.2 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . 101
Bibliography 102
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