Infrared Temperature Control System for a Completely Noncontact Polymerase Chain Reaction in Microfluidic Chips

2007 ◽  
Vol 79 (4) ◽  
pp. 1294-1300 ◽  
Author(s):  
Michael G. Roper ◽  
Christopher J. Easley ◽  
Lindsay A. Legendre ◽  
Joseph A. C. Humphrey ◽  
James P. Landers
Keyword(s):  
Polymerase Chain Reaction ◽  
Control System ◽  
Temperature Control ◽  
Temperature Control System ◽  
Microfluidic Chips ◽  
Chain Reaction ◽  
Polymerase Chain

scholarly journals Non-Contact Temperature Control System Applicable to Polymerase Chain Reaction on a Lab-on-a-Disc

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2621 ◽  
Author(s):  
Junguk Ko ◽  
Jae-Chern Yoo
Keyword(s):  
Polymerase Chain Reaction ◽  
Control System ◽  
Temperature Control ◽  
Pcr Amplification ◽  
Contact Temperature ◽  
Temperature Control System ◽  
Chain Reaction ◽  
One Stop ◽  
Detection Capabilities ◽  
Polymerase Chain

Polymerase chain reaction (PCR) and the visual inspection of fluorescent amplicons for detection are commonly used procedures in nucleic acid tests. However, it has been extremely challenging to incorporate PCR onto a lab-on-a-disc (PCR–LOD) as it involves controlling the complicated and precise heating steps during thermal cycling and the measurement of reagent temperature. Additionally, a non-contact temperature control system without any connecting attachments needs to be implemented to facilitate the rotation of the PCR–LOD. This study presents a non-contact temperature control system to integrate conventional PCR onto an LOD. The experimental results demonstrate that our proposed system provides one-stop detection capabilities for Salmonella with a stable PCR amplification in a single PCR–LOD.

Neural-Genetic Optimization of Temperature Control in a Continuous Flow Polymerase Chain Reaction Microdevice

2005 ◽  
Author(s):  
Hing Wah Lee ◽  
Parthiban Arunasalam ◽  
Ishak A. Azid ◽  
Kankanhally N. Seetharamu
Keyword(s):  
Polymerase Chain Reaction ◽  
Reaction Zone ◽  
Temperature Control ◽  
Continuous Flow ◽  
Microfluidic Channel ◽  
Microfluidic Channels ◽  
Genetic Optimization ◽  
Chain Reaction ◽  
Polymerase Chain ◽  
Simulation Results

In this study, a hybridized neural-genetic optimization methodology realized by embedding finite element analysis (FEA) trained artificial neural networks (ANN) into genetic algorithms (GA) is used to optimize temperature control in a ceramic based continuous flow polymerase chain reaction (CPCR) device. The CPCR device requires three thermally isolated zones of 94°C, 65°C and 72°C for the corresponding process of denaturing, annealing and extension to complete a cycle of polymerase chain reaction. Three separately addressable heaters provide heat input to each zone, microfluidic channels allow for the transport of fluid between zones and thermal isolation between the zones is maintained by machining air-gaps into the device. The most important aspect of temperature control in the CPCR is to maintain temperature distribution at each reaction zone with a precision of ±1°C or better irrespective of changing ambient conditions. Results obtained from the FEA simulation are compared with published experimental work. Simulation results show good comparison with experimental work for the temperature control in each reaction zone of the microfluidic channels. The data is then used to train the ANN to predict the temperature distribution of the microfluidic channel for new heater input power and fluid flow rate. Using these data, optimization of temperature control in the CPCR device is achieved by embedding the trained ANN results as a fitness function into GA. The objective of the optimization is to minimize the temperature difference in each reaction zone of the microfluidic channel while satisfying the residence time requirement. Finally, the optimized results for the CPCR device are used to build a new FEA model for numerical simulation analysis. The simulation results for the neural-genetic optimized CPCR model and the initial CPCR model are then compared. The neural-genetic optimized model shows a significant improvement from the initial model establishing the optimization methods superiority.

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