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3 Simulating Droplet Microfluidic Networks
Figure 3.8 depicts that the droplet is successfully trapped in the trapping well.
Then, the droplet stays in the trapping well, since the Young-Laplace pressure is
equal to 50.4 mbar (i.e., as the droplet entirely fills the trapping well, the trapping
well radius is equal to the droplet radius), which is larger than the applied pressure
of 30 mbar. Hence, the droplet is not squeezed out through any of the two gaps.
Furthermore, as soon as the droplet is fully contained in the trapping well (i.e., after
14 ms), it blocks the flow into the two narrow channels.
Figure 3.9 shows that a droplet over a perpendicular channel blocks the flow into
this channel. The simulation framework uses the position of the droplet’s “head”
and “tail” in order to determine the time span when the droplet clogs the channel.
Overall, these two small networks confirm the correct simulation of the phenomena, which is heavily utilized in the following case study.
3.4 Case Study
In this section, the potential that the proposed simulation framework provides
during the design of droplet microfluidic networks is demonstrated. To this end,
the simulation framework is applied in the design process of a practically relevant
droplet microfluidic network, namely the one proposed in [13] for screening drug
compounds that inhibit the tau-peptide aggregation. More precisely, the design
of this microfluidic network is considered in both fashions—in the “traditional”
fashion (i.e., manually, with many prototyping iterations) and in a fashion where the
advanced simulation framework is additionally used.
This case study shows that, compared to the traditional design process which
required six fabricated prototypes, one person month of an experienced designer,
and financial costs of USD 1200, the proposed simulation framework can significantly help when deriving the specification. More precisely, this study demonstrates
that, using the simulation framework, the designer is guided towards the design,
which has been finally used in [13]. Moreover, the simulations even allow further
explorations of new designs and, e.g., predictions for their throughputs.
3.4.1 Considered Droplet Microfluidic Network
In this case study, a droplet microfluidic network is considered, which can be
used to screen drug compounds that inhibit the tau-peptide aggregation [13].
This phenomenon is related to neurodegenerative disorders such as Alzheimer’s
disease [90] and here protein misfolding and aggregation are considered to play
a significant role. Therefore, the screening process is to figure out the compounds
that can inhibit protein aggregation. The use of droplets for this application allows
the significant reduction of the sample consumption volume by a factor of 100 as
3 Simulating Droplet Microfluidic Networks
Figure 3.8 depicts that the droplet is successfully trapped in the trapping well.
Then, the droplet stays in the trapping well, since the Young-Laplace pressure is
equal to 50.4 mbar (i.e., as the droplet entirely fills the trapping well, the trapping
well radius is equal to the droplet radius), which is larger than the applied pressure
of 30 mbar. Hence, the droplet is not squeezed out through any of the two gaps.
Furthermore, as soon as the droplet is fully contained in the trapping well (i.e., after
14 ms), it blocks the flow into the two narrow channels.
Figure 3.9 shows that a droplet over a perpendicular channel blocks the flow into
this channel. The simulation framework uses the position of the droplet’s “head”
and “tail” in order to determine the time span when the droplet clogs the channel.
Overall, these two small networks confirm the correct simulation of the phenomena, which is heavily utilized in the following case study.
3.4 Case Study
In this section, the potential that the proposed simulation framework provides
during the design of droplet microfluidic networks is demonstrated. To this end,
the simulation framework is applied in the design process of a practically relevant
droplet microfluidic network, namely the one proposed in [13] for screening drug
compounds that inhibit the tau-peptide aggregation. More precisely, the design
of this microfluidic network is considered in both fashions—in the “traditional”
fashion (i.e., manually, with many prototyping iterations) and in a fashion where the
advanced simulation framework is additionally used.
This case study shows that, compared to the traditional design process which
required six fabricated prototypes, one person month of an experienced designer,
and financial costs of USD 1200, the proposed simulation framework can significantly help when deriving the specification. More precisely, this study demonstrates
that, using the simulation framework, the designer is guided towards the design,
which has been finally used in [13]. Moreover, the simulations even allow further
explorations of new designs and, e.g., predictions for their throughputs.
3.4.1 Considered Droplet Microfluidic Network
In this case study, a droplet microfluidic network is considered, which can be
used to screen drug compounds that inhibit the tau-peptide aggregation [13].
This phenomenon is related to neurodegenerative disorders such as Alzheimer’s
disease [90] and here protein misfolding and aggregation are considered to play
a significant role. Therefore, the screening process is to figure out the compounds
that can inhibit protein aggregation. The use of droplets for this application allows
the significant reduction of the sample consumption volume by a factor of 100 as