Numerical Optimization of Key Design Parameters of a Thermoelectric Microfluidic Sensor for Ultrasensitive Detection of Biochemical Analytes

Autor: Gergana G. Nestorova, Louis G. Reis, Saif Mohammad Ishraq Bari
Rok vydání: 2020
Předmět:
Zdroj: Journal of Thermal Science and Engineering Applications. 13
ISSN: 1948-5093
1948-5085
DOI: 10.1115/1.4047826
Popis: The design of highly sensitive thermoelectric microfluidic sensors for the characterization of biochemical processes is an important area of engineering research. This study reports the design and fabrication of a continuous-flow biosensor with an integrated thermopile and three-dimensional numerical analysis of the critical design parameters that significantly increase the detection sensitivity of the platform. The paper discusses the impact of volumetric flowrate, channel height, material thermal properties, and heat sink on the magnitude of the thermoelectric signal. In the platform understudy, the heat generated by the enzymatic reaction between glucose oxidase-conjugated antibody and glucose is converted to an electric output by an antimony-bismuth thin-film thermopile with a theoretical Seebeck coefficient of 7.14 µV mK−1. Since this experimental configuration has been implemented in a various biochemical analysis, particular emphasis in this work is maximizing the detection sensitivity of the device. Computational thermal modeling was performed to investigate the impact of channel height (50 µm, 100 µm, 150 µm, and 200 µm), the volumetric flow rate of the substrate (25 µL min−1 and 50 µL min−1), and the microdevice material (glass, PMMA, and PDMS) on the output of the thermoelectric sensor. Experimental data validated the model and provided an excellent correlation between the predicted and measured voltage output. Results show that fabricating the calorimeter out of materials with lower thermal diffusivity, reducing the channel height, and eliminating the heat sink at the reference junction of the thermopile increases the sensitivity of the platform by 783%.
Databáze: OpenAIRE