8.3 Multiphysics modeling and coupling in lab-on-a-chip devices

Lab-on-a-chip devices are complex systems that require understanding multiple physical processes. Multiphysics modeling helps predict how these processes interact, enabling better device design and performance optimization.

By integrating fluid dynamics, electrokinetics, heat transfer, and biochemical reactions into a single model, researchers can uncover non-intuitive effects and emergent behaviors. This approach is crucial for advancing lab-on-a-chip technology and its applications.

Multiphysics Modeling for Lab-on-a-Chip Devices

Importance of Multiphysics Modeling

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  Frontiers | Biomedical Application of Functional Materials in Organ-on-a-Chip View original

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• Predicts complex interactions between different physical phenomena in lab-on-a-chip devices (fluid dynamics, heat transfer, electrokinetics)
• Integrates multiple physical processes in a single model for comprehensive understanding of device performance
• Enables investigation of emergent behaviors and non-intuitive effects not apparent when considering individual physical processes
• Optimizes design parameters for lab-on-a-chip devices

Challenges in Multiphysics Modeling

• Computational complexity increases with multiple physical processes
• Scale disparities between different physical phenomena require careful consideration
• Accurate coupling between various physics domains necessitates advanced modeling techniques
• Selection of appropriate numerical methods and solver algorithms crucial for convergence and stability
• Requires deep understanding of underlying physics and mathematical representations

Physical Phenomena in Lab-on-a-Chip Systems

Fluid Dynamics and Transport Phenomena

• Laminar and turbulent flows govern transport of analytes and reagents
• Mass transport mechanisms (diffusion, convection) critical for understanding movement of molecules and particles
• Surface phenomena (wetting, capillary effects, surface tension) significantly influence fluid behavior due to high surface-area-to-volume ratio

Electrokinetics and Thermal Effects

• Electrokinetic phenomena (electrophoresis, electroosmosis) play crucial role in separation and detection processes
• Heat transfer and thermal management affect reaction kinetics, fluid properties, and overall system performance
• Mechanical stresses and deformations in microfluidic structures impact fluid flow and other physical processes

Chemical and Biochemical Interactions

• Chemical reactions occurring within lab-on-a-chip devices must be considered for accurate predictions
• Biochemical interactions influence system behavior and performance
• Surface chemistry and functionalization affect molecular interactions and device functionality

Coupled Multiphysics Models for Lab-on-a-Chip

Model Development and Implementation

• Identify and prioritize relevant physical phenomena based on specific lab-on-a-chip application
• Establish governing equations and boundary conditions for each physical domain
• Implement coupling mechanisms between different physical processes (fluid-structure interaction, electrokinetic-hydrodynamic coupling)
• Select numerical methods and discretization schemes for each physical domain
• Develop strategies for handling disparate time and length scales in different physical processes

Numerical Techniques and Solver Strategies

• Implement adaptive mesh refinement techniques to resolve areas of high gradients or complex interactions
• Utilize appropriate solver algorithms for coupled multiphysics systems
• Establish convergence criteria considering stability and accuracy requirements
• Balance computational efficiency with desired level of accuracy in simulations

Model Validation and Refinement for Lab-on-a-Chip

Experimental Validation and Comparison

• Design experiments to obtain relevant data for model validation (key performance metrics, observable phenomena)
• Develop quantitative comparison methods to assess agreement between simulation results and experimental data
• Perform statistical analysis and error estimation techniques for robust validation
• Assess model's predictive capabilities for different operating conditions and device configurations

Model Refinement and Optimization

• Conduct sensitivity analyses to identify influential parameters and physical processes
• Perform parametric studies to explore design space and optimize device performance
• Implement iterative refinement processes to improve model accuracy
• Incorporate new experimental insights and address discrepancies between simulations and measurements
• Develop strategies for model simplification and reduction while maintaining acceptable accuracy