8TH INTERNATIONAL SYMPOSIUM ON PARTICLE IMAGE VELOCIMETRY - PIV09
Melbourne, Victoria, Australia, August 25-28, 2009

Flow visualization of a forming droplet in a micro channel with FlexPIV
M.R. Duxenneuner1,3, O. Pust2 , P. Fischer1, E.J. Windhab1, and J.J. Cooper-White3
1

Laboratory for Food Process Engineering, Department of Food Science and Nutrition, ETH Zurich,
8092 Zurich, Switzerland; mduxenneuner@ethz.ch2Dantec Dynamics A/S, Tonsbakken 16-18, 2740 Skovlunde, Denmark; oliver.pust@dantecdynamics.com
3

Tissue Engineering and Microfluidics Laboratory, Australian Institute for Bioengineering andNanotechnology, University of Queensland, Brisbane 4072, Australia; j.cooperwhite@uq.edu.au
ABSTRACTThis work investigates monodisperse water droplet generation
into sunflower oil (SFO) using coflow microchannels with
only food-grade solutions of Tween-surfactants and hydroxylpropylether guar gum (HPGG) as disperse phase. We wereable to map simultaneously (inner and outer flow behaviour)
the entire flow situation given by the geometries, process
conditions, and material parameters, utilizing streak imaging(StrIm) and microPIV (μPIV) including the new FlexPIV
software and the additional FlexGrid module.

1. INTRODUCTION
Monodisperse emulsions are often required to produce wellstructured multiphase, as well as for lab-on-the-chip applications in food, pharmaceutical, personal care, and cosmetic
industries. The drop size and size distribution, interfacial
properties, and structuring elements in emulsions determine
quality characteristics of the final product. Monodisperse
emulsions are also used in fundamental droplet studies as well
as a precursor for encapsulation studies because the interpretation of experimental results is much simpler [1-2].
Narrow droplet size distribution is generally difficult toachieve even though modern dispersing devices (e.g. membrane emulsification, pressure homogenizers, rotor-stator
toothed disk turbines, opposed jet micro fluidizers, etc.) havebeen developed in order to produce almost monodisperse
emulsions. In most of these techniques the flow field and thestresses acting on the single droplets are not well defined [3].
Microchannel techniques, however, provide a simple and great
tool which allows the downsizing of industrial devices
utilizing the scaling effects. Numerous benefits such as
reduced sample amount, high sensitivity, short analysis time,and parallel processing lead to a simpler investigation ofcomplex processes [4-6]. Studies of micro droplet creation in
microchannels formed in different geometries (mostly Tchannel or flow-focusing) have been the subject of many
research studies using pure, well characterized solutions anddo not take into account the behaviour and interaction of foodgrade and natural products [7-9].
The coflow dispersing technique which we employed within
this work, utilizes the direct injection of a disperse phase into
an ambient, continuous phase via capillary [10]. We focusedon single water in oil droplet formation (w/o) using only foodgrade Tween-surfactants, polymer, and oil solutions for both
the dispersed (droplet) and continuous (bulk) phase. The
objective was to visualize the influence of single process andmaterial parameters on the droplet dynamics, as we mainly
varied the flow rate of both phases and the interfacial andelastic properties of the droplet solution [11]. However, tounderstand and analyse drop formation and breakup dynamics
and also in order to design micro-flow devices and droplet
based reactors, it is important to measure the dispersed andcontinuous flow fields in and around a droplet.

2. MATERIAL
2.1 Continuous phase
Commercially available, cold-pressed sunflower oil (SFO,
Florin AG) gained from sunflower seeds, was taken as theambient bulk phase for all experiments. This oil is widely usedin nutrition and food industry especially for uncooked foods(e.g. salad dressings, margarine, mayonnaise) because of its
high content of vitamin E and unsaturated fatty acids. Even
though it is natural product it shows rather constant zero shear
viscosity at 56 mPa·s over a range of shear rates.

Figure 1. μPIV setup using: (A1) Nd:YAG-double pulsed
laser, (A2) mercury lamp, (B) epi-fluorescent prism, (C)microscope objective lens, (D) microscope stage, (E)micro device, (G) barrier filter (transferred λ > 590 nm),(H) CCD SensiCam QE kit, (F) Harvard Syringe pumps.
2.2 Disperse phase
For the hydrophilic droplet phase we used non-ionic Tween
20, 40, 60, or 80 surfactants added to distilled water at different concentrated from 1 cmc (beginning of surfactant micelleformation) up to 1370 cmc (~2 V%; most surfactants are inform of micelles). The density and the zero shear viscosity of

the Tween-water mixtures were measured to be water-like.
Tween surfactants at higher concentrations are generally applied in food and cosmetic industry to produce and stabilize
various kinds of emulsions.
In order to study the extensional influence of the disperse
phase on the droplet kinetics and final droplet size, we addedhydrophobically modified guar gum (HPGG; hydroxylpropylether guar gum, also called JAGUAR) to the water phase (with0.01 to 0.2 wt%) [12].
2.3 Particle seeding
Different sized fluorescent (nile red) polymer microspheres(carboxylated polystyrene beads) as tracer particles (Duke
Scientific) were used to seed both the disperse (DR700 = 0.7μm) as well as the continuous phase (DR300 = 0.3 μm). The
particles show a maximum excitation wavelength of λex,max =542 nm (green) and an emission maximum wavelength of
λem,max = 612 nm (orange-red). The right choice of seeding
density (droplet phase 0.03 V% max., continuous phase 0.01
V% max.) was important allowing us to catch the in-focus particle over the background glow.

3. EXPERIMENTAL SETUP
In order to investigate the flow stream behaviour, we
performed μPIV and StrIm experiments employing PDMS
coflow microchannel with different capillary geometries. The
soft-lithography technique to manufacture PDMS-channels
utilized in the present work is a common expertise which isalready described elsewhere [9, 13].
Figure 1 illustrates and explains the experimental setup
utilizing an inverted microscope (Leica), a PC workstation
including Dantec Dynamics PIV/LIV capturing and analysing
system.

grids are adapted to the nature of the flow and the flow region
geometry. This approach yields advantages for PIV methods
such as:•Velocity vectors will only be calculated in defined areas(objects) where a grid has to be set before. Although it ispossible to exclude certain areas from being processed bymasking out regions of the particle images, the total image
is still processed by the cross-correlation algorithm.

•

The number and position of the velocity vectors aredecoupled from the size of the interrogation areas.

•

Grids of different density can be generated adapted
specifically to the conditions of the flow field at certain
positions. Additionally, it is possible to define different
processing parameters for each grid independently.

•

As wall structures or boundary lines cross the definedinterrogation areas at oblique angles, errors in thecalculated velocity vectors may occur. The possible
definition of a “wall object” prohibits false calculations bydisregarding the part of the interrogation area that belongs
to the wall structure.

4.2 Grid generation with FlexGrid module
Prior the velocity flow field calculations a grid similar to CFD
applications needs to be defined for each grid-object using asoftware module FlexGrid. FlexPIV analysis relies finally on
the grid definitions provided by FlexGrid. However, the newmodule offers various object geometries (rectangles, squares,
ellipses, circles, and polygon curves) which can be drawn
graphically using mouse commands or by direct numericalinput. Each object can then be connected to a specific gridtype such as Cartesian, polar, elliptic or Delaunay grid (Figure2a-d). The final processing parameters for the crosscorrelation output are designated to each grid individually.

4. ANALYSIS METHOD
4.1 FlexPIV software
Since the development of PIV (particle image velocimetry)
more than 20 years ago, many extensions and enhancements tothe technique were accomplished. Starting from autocorrelation, going to cross-correlation and further to adaptive
cross-correlation (nowadays the de-facto standard in PIVprocessing) the power of PIV increased tremendously.
Methods for special applications, like average correlation forµPIV or hybrid techniques combining PIV and PTV (particle
tracking velocimetry) for increased spatial resolution, foundtheir way into the community. The last leap forward was
denoted by the consideration of not only translational shifts
but also of strain and rotational deformations of the particle
pattern (“deformed Windows”). The results of all thesemethods, however, are velocity fields within a Cartesian(typically square) frame, in which all vectors are calculatedusing the same constant parameters (e.g. size of the interrogation area (IA), validation setup, etc) within an image. An
individual consideration of the local geometry and flow
situation does not take place. Only the development of amethod for flexible, flow and geometry adapted PIVprocessing (FlexPIV) remedies the limitations mentioned
before.
Using FlexPIV, a concept already known from Computational
Fluid Dynamics (CFD) is transferred to PIV applications. In
CFD it is commonplace long since that the computational

(a)

(b)

(c)

(d)

Figure 2. Illustration of grid-objects (rectangle or square,
circle, ellipse, polygon) and grid types (Cartesian, polar,
elliptic, and Delaunay grid).
Furthermore, the software module allows providing thedefined grid-object with one of the additional properties suchas “grid”, “hole”, “false hole” or “wall”. Using an object withthe property “hole” for example, areas within another grid canbe excluded from the processing (Figure 2c). An object withthe property “false whole” is used to define a grid within agrid without calculating redundant vectors in the common
area. The object property “wall” does not take into account thegrey level values covered by this object during crosscorrelation. A complex grid definition and the resulting
velocity field are shown in Figure 3 exemplified in a cylinder
wake.
Applying FlexPIV including FlexGrid instead of conventional
rigid grids, it is feasible to process PIV images in a flexible
way which is adapted to the actual flow situation. Hence, theachieved results with FlexPIV exceed what has been known sofar. In this work we will present an example of flow analysis,
where we visualize simultaneously the inner and outer flow ofa forming droplet in a microchannel utilizing FlexPIV and
FlexGrid.

polymer to the water. As the extensional properties of thepolymer elongates the droplet necking, the flow of the bulk
phase becomes smoother and the vortices do not exist anymore [14].

A

B
Figure 4. Fluorescent micrograph matched with thecorresponding vector field of a forming droplet at thecapillary tip within a coflow microchannel.

Figure 3. Example of grid-object and grid type definitions
using the FlexGrid module (A) and the related velocity
profile calculated with FlexPIV (B).
4.1 FlexPIV analysis
We performed FlexPIV analysis utilizing an adaptive
correlation algorithm with IA-overlap of at least 25-50%. The
IA were sized either 16x16 pxl or 32x32 pxl depending on thevelocity flow fields, so there was only little velocity variation(< 5-10%) within a IA. Further post-processing procedures
were kept to a minimum in all analysis of the presented μPIV
experiments. The area outside the actual microchannel was
masked out prior FlexPIV analysis to minimize the number offalse velocity vectors. In order to verify the result of the crosscorrelation, the original fluorescent micrograph and thecorresponding velocity vector field was matched as it is shownin Figure 4 [11].

3. RESULTS
Generally, the droplet formation during dripping in coflow
systems can be divided in 3 different stages: (i) droplet fillingwhere the droplet is attached to the capillary outlet andexpands in all direction till the equilibrium of the net forces islost (interfacial tension versus detaching forces), (ii) droplet
separation starts by forming a neck, and (iii) the neck thins
rapidly till the droplet finally pinches off (drag and inertial
overcome the interfacial tension force) [10].
Figure 5 shows the flow stream around a forming droplet atthe capillary tip. Applying StrIm, we were able to visualize little vortices using only water or Tween-water solution as thedroplet phase. Main differences in the flow stream could only
be observed once we added little amount (0.01wt%) of HPGG

The final Figure 6 presents a water droplet within a microchannel at the stage (i) of droplet filling (compare Figure 5iii). The experiment was run at flow rates of Qd = 0.1 ml/h (vd= 0.83 m/s, disperse phase) and Qc = 0.5 ml/h (vc = 0.17 m/s,
continuous phase). The fluorescent micrograph including theframe of the high-speed captured picture shows the actual position of the droplet inside the microchannel. We clearly observe, shown by the different colours of the velocity vectors(Figure 5 middle) that the speed of the disperse phase is thefastest close to the capillary outlet and decreases with increasing distance to the orifice. To analyse the flow profile quantitatively the velocity values of the disperse phase were thentaken at 4 different stream-wise positions (constant x-value,
parallel to the y-axis). We observed that the symmetrical velocity distribution at varies locations shows a decrease withgrowing droplet and with increasing distance from the capillary tip.

Figure 5. Streak images of a forming H2O droplet (Qd =0.01 ml/h) into SFO (Qc = 0.1 ml/h) inside a
microchannel. Both phases are seeded with particles.

9. CONCLUSIONS
We visualized the (w/o) droplet formation and pinch-off at thecapillary tip in coflow microchannels systematically using various food-grade solutions. Differences in the droplet evolution
process were observed with the addition of food-grade Tweensurfactants and modified guar (HPGG) to the disperse phasewhich includes modified droplet sizes, elongated drop creation, and the generation of a thread while pinching off. The
latter effect is a result of the elastic stresses developed withinthe fluid during the dispersing process.

Grants Australia, ETH Zurich fund TH-38/03-3, and the EUProject NMP-033339 “Con-trolled Release”.

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Figure 6. Illustration of the inner flow behaviour of aforming droplet at the stage of filling: velocity profile(middle), velocity graph (bottom), corresponding
micrographic (top) [14-15].

[13] McDonald J.C., Duffy D.C., Anderson J.R., Chiu D.T., Wu
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microfluidic systems in poly(dimethylsiloxane). Electrophoresis,21(1), 27-40.

Due to the drop forming at the capillary tip and the unsteadyflow behaviour within our experiments, it was impossible toapply a simple averaging or cross-correlation analysis method.
Both analytical methods would only lead to an instantaneous
velocity measurement with zero-averaging of random noise
peaks. FlexPIV algorithm with the additional FlexGrid module
instead, allowed detailed analysis with multiple processing
schemes of specifically defined, flexible object grids.

[14] Duxenneuner M.R., Fischer P., Cooper-White J.J., Windhab E.J.
(2009) Simultanous inner and outer flow visualization ofgenerated droplets in microchannel coflow. Lab on a Chip, (inpreparation).

ACKNOWLEDGMENTS
This work was mainly setup under an international
collaboration between University of Queensland and ETH
Zurich. It was financially supported by the ARC Discovery[15] Duxenneuner M.R., Fischer P., Windhab E.J., Cooper-White J.J.
(2009) Investigation of monodisperse droplet generation incoflow. In. Editor Fischer P., Proc. 5th International Symposium
on Food Rheology and Structure, ETH Zürich Switzerland, 15.18. June.