Thermal Analysis Of A Continuous Flow Polymerase Chain Reaction (Cpcr} Microdevice Using Finite Element Method And Neuro-Genetic

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Date
2006-03
Authors
Hing Wah, Lee
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Abstract
A successful polymerase chain reaction (PCR) process used for amplification of the deoxyribonucleic acid (DNA) can only be achieved when the necessary temperature and residence time requirement are fulfi"ed. In view of this, temperature analysis and subsequently temperature control must be performed. Recent research focused more on the fabrication and the development on CPCR microdevice while neglecting thorough analysis on the temperature characteristics. In addition, explanation and presentation of the temperature control methods are almost non-existent. To eliminate the above limitations, a three-dimensional finite element analysis (FEA) and a two-dimensional analytical finite element model has been developed to analyze the thermal performance of the CPCR microdevice. For the FEA, two types of modeling, namely the detailed and the simplified model, have been used while only the simplified model is used for the analytical simulation. Comparison of results between temperatures obtained using the ANSYS® FEA (detailed and simplified model) and the analytical FEM simulation (simplified model) with the published experimental results showed good agreement. However, simulations using the simplified model predicted a lower temperature than that of the detailed model due to the higher convective heat transfer area present in thesimplifiedmodel'and the effect of the DNA buffer flow separation in the detailed model. From the temperature analysis, it can be inferred that to obtain a successful PCR process for a constant flow velocity of 1.3mm/s, the input power must be assigned with a value between 560mW - 580mW, 60mW - 80mW and 300mW - 31 OmW for heater 1, heater 2 and heater 3 respectively. This work also introduced a novel temperature control methodology using Neuro-Genetic optimization which combined the artificial neural network (ANN) and genetic algorithms (GA). Results for temperature predictions obtained using ANN showed good agreements with the finite element simulated results. From the GA analysis, the optimized result with the corresponding vaiue of 580mW, 80mW and 306mW for heater 1, heater 2 and heater 3 respectively is obtained where it is abiG tel fulfil both the temperature and residence time requirement.
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Engineering , Machanical
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