ESI Group provides advanced simulation software, collaborative R&D, and consultation and product development services for the design and optimization of BioMEMS, lab-on-chip, and microfluidic devices. Our multiphysics software package CFD-ACE+ rigorously simulates the complex interacting physics (microfluidics, electrokinetics, biochemistry, electrostatics, stress etc.) that govern biochip performance.

Our mission is to enable rapid screening of concepts and optimization of downselected designs (simulation-based design), thereby accelerating your product development.

CFD-ACE+ has been extensively used to conceptualize, analyze and optimize many different components and systems that characterize a typical biochip. These range from sample preparation techniques (separation, injection, amplification / pre-concentration, reagent mixing) to sample detection (optical, fluorescence-based, electrochemical etc.).

Design issues for a typical biochip are shown in the schematic:

CFD-ACE+ has been successfully used by a number of clients in the biotechnology industry.

Motorola testifies (emphasis added):
… extensive thermal-fluidic modeling was done on both systems in order to virtually prototype and optimize the designs. This enabled several design iterations to be performed without requiring costly and time-consuming device fabrication at each change. The system models proved to be quite accurate as good correlation with measured data was also shown.

Comparison of CFD-ACE+ simulation and experiments of sample multiplexing (experimental images courtesy of Prof. Chong Ahn, U. Cincinnati)

Microfluidics

Biochemistry

Electrokinetics/Chemistry

• Hydrophobic/Hydrophilic filling and dispensing
• Pressure Driven Flow
• Taylor-Aris Dispersion
• Sample Mixing 
• Particle/Cell Transport
• Fluid-Structure Interaction
• Heat Transfer (PCR Cycling)

• Mass Transport or Kinetics-Limited Binding
• Antigen-Antibody, Ligand-Receptor Binding
• Multi-Protein, Multi-Receptor, Competitive Binding
• DNA Hybridization
• Surface or Volume-Immobilized Enzyme Catalysis (michaelis-Menten)
• Microsphere-based Detection (Immunoassays)

• Electroosmosis/Electrophoresis
• Ionization Involving Acid/Base Reactions, Ampholyte Chemistry
• Modes of Focusing / Separation - Isoelectric Focusing,  Isotachophoresis, pH Gradient Electrophoresis, CE-ITP, etc.
• Field Amplified Sample Stacking
• Electrochemical Sensing
• Conventional & Traveling Wave Dielectrophoresis
• Sample Dispersion Under EOF
• Electromagnetics
• AC/DC Electric Fields
• Joule Heating
• Electrokinetic Injection  

Void entrapments (presence of bubbles) is an often-confronted problem in the design of microarrays and microfluidic chips. Occurrence of these bubbles can be avoided through careful design of the chip and control of the filling process. The free surface flow module (VOF) in CFD-ACE+ can be used to study the capillary filling (by action of surface forces) of hydrophobic and hydrophilic fluids. The following example shows how a simple design change (deepening of the well) can avoid bubble entrapment.

Device Optimization

Electrochemical biosensors use electrochemical methods for transduction. They can be subdivided in to three types:

1. Potentiometric sensors that involve the measurement of potential of a cell at zero current. The potential will be proportional to the logarithm of the concentration of the substrate being measured.
2. Amperometric sensors where an increasing (decreasing) potential is applied to the cell until oxidation (reduction) of the substance to be analyzed occurs. This results in sharp rise (decrease) in the cell current to give a peak current. The height of this peak current will be directly proportional to the concentration of the electroactive species.
3. Conductimetric sensors use the relationship between the conductance and ionic species concentration to measure the concentration of the substrate.

CFD-ACE+ has the capability to simulate various biosensors using the Flow, Heat, Chemistry and Electric modules. Such simulations help designers optimize sensors in terms of process conditions, selection of buffer pH, membranes, and cell geometry, among others.

The sample problem shown demonstrates how CFD-ACE+ can be used to optimize an oxygen biosensor that works on the amperometric method. Simulations have been performed to quantitatively estimate how the signal varies with oxygen concentration, as well as to understand the more complex phenomenon of sensitivity of the assay due to variations in the diffusivity of the oxygen caused by Joule heating.

Electrokinetic injection in cross channel configurations is a technique used for introducing precisely metered samples in a microfluidic channel. Design and optimization of the injection process involves the selection of appropriate voltages as well as precise timing for switching the electric field.  CFD-ACE+ can help determine the optimal process parameters and geometric design for accurate sample injection, as well as develop a fundamental understanding of the electrokinetic transport processes occurring in the system.

Electrokinetic Sample Injection

Click here for animation.

Electroosmotic Flow (EOF) refers to the bulk movement of an aqueous solution past a stationary solid surface due to an externally applied electric field. This requires the existence of a charged double-layer at solid-liquid interface. The electroosmotic flow produces a flat velocity profile across the channel.

• EOF can be modeled via specifying electroosmotic mobility or zeta potential
• Electroosmotic mobility will increase proportionally to the surface charge density
• Reducing the electric field will decrease the electroosmotic flow
• Electroosmosis varies with the pH of the solution

CFD-ACE+ has the capability to simulate EOF by specifying either electroosmotic mobility or zeta potential. These properties can be specified as a constant, predefined function, or via user defined options.

The sample problem shown demonstrates how CFD-ACE+ can be used to optimize electrokinetic systems in terms of applied voltage and geometry to achieve the desired mass flow rate. It can also be used to study the flow phenomenon of Taylor dispersion that occurs due to spatially varying zeta potential, subsequently generating internal pressure gradients.

Enzymatic Biosensor

Isoelectric Focusing

Kinetic rate coefficients are often extracted from data using least squares fitting performed on an assumed functional form for the rate data. Problems arise in this type of procedure when there is the presence of a mass transport limitation (produced when the rate of reaction is faster than the rate of diffusion of reactant to the reaction surface). It is difficult to account analytically for mass transport limitations, so the fitted coefficients using the above procedure are not the intrinsic rates of reaction.

Using CFD-ACE+, intrinsic kinetic coefficients can be extracted from rate data regardless of the complexity of the reaction mechanism. Instead of using an assumed functional form for the theoretical curves, the curves are generated using simulations, thereby deconvolving the effect of mass transport on the chemistry. The optimization algorithm uses a Gauss-Newton Non-Linear Least Squares with non-negativity constraints. Fitting of multiple data sets simultaneously (Global Fitting) is also supported. The top figure to the right is an example of global fitting of Carboxymethylsulfonamide binding to Anhydrase-II.

Microfluidic Passive Valves

Surface tension effects are dominant at small length scales prevalent in microfluidic devices. Hydrophobic surfaces act as "passive" (because of an absence of moving parts) valves and are being increasingly used for flow control in these systems. Lab-On-A-CD devices use centrifugal forces generated by rotation of the CD to provide the driving force for fluid transport. CFD-ACE+ can model and determine optimal rotation speeds for precisely controlled fluidic motion on the CD.

Microsphere-Based Biosensor

Mixing

Optical Biosensor

Electrophoresis

Contour Maps showing Protein Separation by CZE/ITP

Click here for animation.

Sample Dispensing