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TTU CS Seminar

Design of droplet mLN devices
by Jeevan Maddala on 8 May 2014

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Transcript of TTU CS Seminar

A small change in inlet spacing leads to a drastic change in the exit dynamics this chaotic masking can be potentially used to send
encrypted chemical signals




Based on the inlet spacing different stable regions are obtained resembling a multi-stable behavior, which is a phenomena responsible for
memory storage in neurons



Transforming the data into radial coordinates shows
fractal behavior












Simulated data from the loop device that resembles a
kiwi fruit
Design of droplet based microfluidic devices for Lab-On-Chip applications
Jeevan Maddala

Applications
Background
Challenges
Point of care diagnosis
Biomimicry
Drug Discovery
How to check if a particular drug is allergic to you?
Droplet microfluidic technology
Fast to detect
Detection with minimum amount of blood
Can be made mobile
Challenges:
Designing these devices
Control of droplets
Chemistry
Reaction mechanism
Advantages using microfluidic technology
Screening 1 Million drops/Sec
Easy scale up
Precise results at lab scale and large scale
Less raw material cost
Challenges:
How to rationally design the structure to perform combinatorial chemistry?
Devices mimicking biological systems to understand and exploit the designs made by nature
Water
T-Junction
Whitesides., Nature, 2006
Manuprakash et al., Science 2007
Song et al., Angew. Chem. Int. Ed, 2006
Piotr Garstecki lab group
Microfluidics is the science of manipulating small (10 to 10 liters) amounts of fluids in micro channels

Advantages of droplet based microfluidic devices

Precisely control concentration of molecules in space and time
Throughput ( 1 Million/sec)
Oil
Understanding the behavior of droplets
Controlling droplet position in space and time
Designing the networks to achieve desired objective
Proposed GA approach
Objective 1: Design Ladder network to synchronize droplets coming at fixed delay with minimum bypasses
Results
Initial population is doubled by several mutations and one point crossovers
N to 2N
Selection operator is used to select N structures from 2N for next generation
2N to N
Selection contains the following metrics
Fitness function





Diversity




.
Genetic operators – Selection
Changes the structural properties drastically
Helps in locating all the optimal solutions

Implemented in two tiers:
Tier1: Changes the overall bypass number
Tier2: Searches among the existing structures by changing 1 bypass
Genetic operators - Mutation
Ladder network has the following components:
Total bypasses (TB) – Binary values
Value (V) – Real values
Choice of top segment (SC ) – Binary values
Choice of bottom segment (SC ) – Binary values
Coding Decoding
As number of bypasses(n) increases, number of equations to solve is in order of (n )

Simple analytical model is not possible due to droplet motion

Structure  functionality is nonlinear and discontinuous

Modifying single bypass could lead to a drastic change in functionality

Therefore finding optimal designs using conventional gradient based approaches is hard

Possible solutions to find optimal structures is through: Evolutionary algorithm or MINLP
Challenges in designing multi-bypass ladder networks
Objective 2: Design ladder network to synchronize droplets with input spacing
Results
GA is used to optimize the design based on functionality

Structural component converted to numerical strings
Coding

Genetic operators used to change structure
Mutation
One point cross over
Selection

Functionality is given in the fitness function

Fabrication limitations are posed as constrains in GA
Two off springs are produced by two existing structures

Off springs have properties of both the parents

Searches locally around the existing structures
Genetic operators –One point cross over
Synchronization of drops
Ladder networks
Functionality?
To synchronize droplets
Application:
Reducing input fluctuations for downstream processing
Optimal design?
Design & Functionality
Summary
Proposed solution
Problem:
Design the ladder structures based on objective fucntion ?
(Optimization)
3
Converting structure to numbers
One - one mapping
The coding scheme should span all the domain
Problem statement
Evolutionary optimization: Genetic Algorithm
Design of ladder networks
Dynamics of drops at bifurcating networks
Observations
Droplet behavior
We observe micro scale bifurcating networks in Nature, for e.g. Blood vessels, Leaf venation etc.

We observe that these networks are not only useful for optimal transport of fluids but also have the following advantages:

Encrypted Chemical Signaling
Memory Storage
Pattern formation
Binary droplet decision leads to Chaos
Results
Sigmoid function
Droplet encoder
Proposed an Artificial Intelligence (AI) based approach to design ladder networks

Uncovered ladder designs that are:
Robust to inlet fluctuations
Produce nonlinear transformation of spacing
Encode and decode droplet pairs
-9
-15
Future Work
Thank you!
Acknowledgments:

Dr. Raghunathan Rengaswamy
Dr. Siva Vanapalli

William S Wang



Loop Dynamics
R = R + n R
Used to predict the droplet dynamics

Postulates:

Conservation equation at the node

Flow is conserved at the node

Constitutive relationship

Pressure drop is equal to product of flow and resistance

Velocity of the droplet is linear function of flow rate
V = Q


Each droplet increases the resistance by the same amount



Resistance in the channel is summation of channel resistance, resistance due to droplets and valve resistance
Network model
M Schindler and A Ajdari, Physcal Review Letters,2008
(Palacios A M et al, PNAS, 2011)
(Hosaya et al., Exp Brain Res, 1991)
(Roth-Nebelsick et al., Annals of Botany, 2001)
T
c
d
c

Develop large scale software that could design a generic microfluidic network based on objective function

Implement MINLP frame to design these systems
Cytotoxicity studies
For e.g. loop device
For e.g. Ladder Networks
No diversity leads to local minima
10% Elite
80% Diverse
10% Random
t
b
Manipulate bypasses
Manipulate genes
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