Fabrication of PDMS microfluidic chips used in rapid diagnosis by micro jetting
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In this paper, a simple method was demonstrated to fabricate polydimethylsiloxane (PDMS) microfluidic chips used in rapid diagnosis based on printing the liquid molds directly. The liquid droplets were jetted by a glass micronozzle onto the hydrophilic glass substrate based on microfluidic pulse interior force to form various liquid molds, and then a replication process and a bonding process were followed for the fabrication of PDMS microfluidic chips. The effects of the wettability of the substrate and the overlap between the droplets on the formation of the liquid molds were investigated. Liquid molds with width ranging from 40 μm to 365 μm were prepared with an aspect ratio of 0.080 through controlling the overlap and the droplet size. The surface of the fabricated microchannels was smooth as the arithmetical mean deviation of the profile Ra was 179.1 nm with the 80 × 320 μm2 area at the bottom. The microreactions at the well-defined interfaces of several different solutions were realized with the fabricated PDMS microfluidic chips.
KeywordsPDMS microfluidic chips Directly printing Liquid molds Hydrophilic surface Smooth microchannel
This work is supported by the National Natural Science Foundation of China (No.51175268) and the Open Program of Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing (No.BM2013006) and the Zhejiang Provincial Top Key Discipline of Mechanical Engineering.
- 16.Looper J, Harrison M, Armato SGA (2016) Computer-aided diagnosis system to detect pathologies in temporal subtraction images of chest radiographs[C]//SPIE medical imaging. International Society for Optics and. Photonics:978539–978539Google Scholar
- 30.Zhang Y, Wang S, Ji G, et al. (2013) An MR brain images classifier system via particle swarm optimization and kernel support vector machine[J]. Sci World J 2013:130134Google Scholar
- 33.Zhang YD, Wang SH, Liu G, et al. (2016) Computer-aided diagnosis of abnormal breasts in mammogram images by weighted-type fractional Fourier transform[J]. Advances in Mech Eng 8(2):1687814016634243Google Scholar