Flow-Based Passive Microfluidic Architecture for Homogeneous Mixing

Accurate sample preparation, an essential component in microfluidic design automation, poses a great challenge to lab-on-chip (LoC) designers. Although continuous-flow based microfluidic biochips (CFMBs) offer many advantages, the problem of achieving fast, stable, and controllable sample concentration becomes more pronounced when such chips are used. Most of the flow-based dilutions and/or mixing methods need certain settling time to ensure an output flow with stable concentration profile resulting in initial loss of fluids. Control-valve sequencing along with time management for injecting input fluids further complicates the scenario. In this paper, we address the problem of preparing a homogeneous mixture of several input-fluids with different concentration factors. We present a binary tree-based free-flowing microfluidic architecture, which obviates the need for any control valve. The passive fluidic network supports balanced hydraulic resistance along all input-output fluid paths and ensures low Reynolds number. We show that the homogeneity of mixing can be expedited by properly assigning inlets to fluids with different concentrations based on a graph-matching algorithm. The method reveals how design automation tools can be effectively used for solving a problem of fluid dynamics. Theoretical attributes of the proposed architecture, when compared with COMSOL Multiphysics based simulations, provide excellent agreement with high accuracy.

[1]  Kai Hu,et al.  Control-Layer Routing and Control-Pin Minimization for Flow-Based Microfluidic Biochips , 2017, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[2]  Bhargab B. Bhattacharya,et al.  COMSOL-Based Design and Validation of Dilution Algorithm with Continuous-Flow Lab-on-Chip , 2017 .

[3]  Tsung-Yi Ho,et al.  A top-down synthesis methodology for flow-based microfluidic biochips considering valve-switching minimization , 2013, ISPD '13.

[4]  L. Fu,et al.  Microfluidic Mixing: A Review , 2011, International journal of molecular sciences.

[5]  Kai Hu,et al.  Fault Diagnosis for Leakage and Blockage Defects in Flow-Based Microfluidic Biochips , 2016, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[6]  Wei Duan,et al.  Lab-on-a-chip: a component view , 2010 .

[7]  Sudip Roy,et al.  Dilution and Mixing Algorithms for Flow-Based Microfluidic Biochips , 2017, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[8]  Howard A. Stone,et al.  Introduction to Fluid Dynamics for Microfluidic Flows , 2007 .

[9]  Samuel K Sia,et al.  Commercialization of microfluidic point-of-care diagnostic devices. , 2012, Lab on a chip.

[10]  Bhargab B. Bhattacharya,et al.  Design of Continuous-Flow Lab-on-Chip with 3D Microfluidic Network for Sample Preparation , 2019, 2019 32nd International Conference on VLSI Design and 2019 18th International Conference on Embedded Systems (VLSID).

[11]  Philip Brisk,et al.  Random design of microfluidics. , 2016, Lab on a chip.

[12]  Benjamin Schuler,et al.  Microfluidic mixers for the investigation of rapid protein folding kinetics using synchrotron radiation circular dichroism spectroscopy. , 2008, Analytical chemistry.

[13]  Krishnendu Chakrabarty,et al.  Optimization of Dilution and Mixing of Biochemical Samples Using Digital Microfluidic Biochips , 2010, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[14]  William Thies,et al.  Abstraction layers for scalable microfluidic biocomputing , 2008, Natural Computing.

[15]  Wang Xiang,et al.  Concentration gradient generation methods based on microfluidic systems , 2017 .

[16]  Ulf Schlichtmann,et al.  Testing microfluidic Fully Programmable Valve Arrays (FPVAs) , 2017, Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017.

[17]  Sebastian J Maerkl,et al.  A software-programmable microfluidic device for automated biology. , 2011, Lab on a chip.

[18]  Chia-Hung Liu,et al.  Reactant minimization during sample preparation on digital microfluidic biochips using skewed mixing trees , 2012, 2012 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

[19]  Xin-She Yang,et al.  Introduction to Algorithms , 2021, Nature-Inspired Optimization Algorithms.