High-throughput Droplet Detection In The Microfluidic System

With the development of Micro-Electro-Mechanical System(MEMS), droplet microfluidic system has been widely used in chemical analysis, bio-pharmaceuticals, DNA amplification and cell analysis. The application of droplet-based system has a requirement that the dimension, speed, concentration of droplet should be controlled in accuracy because of the require of experimental conditions and droplet detection is an urgent need. In this paper, based on the overview of existing droplet detection methods, droplet detection based on image processing and the principle of capacitance are proposed. The purpose is to design a microfluidic system which is portable, convenient detection, accuracy control. The advantage of droplet detection method is no need to add markers, high sensitivity, suitable for field testing.The microfluidic system includes droplet generation system and digital microscope system. Droplet generation system is made up of Nitrogen cylinder, micropumps, micro-chip, droplet collection device and some other devices if necessary. Droplet generation system is used for droplet generation. The generated droplets are used for droplet detection experiments. Digital microscope system consists of microscope, CCD(Charge Coupled Device) camera and monitor of digital microscope system. Digital microscope system is applied to observe the droplet development, acquire droplet images and process droplet images.In this paper, droplet dimension detection based on image processing is described. Droplet images are acquired by a common CCD camera because the microfluidic system is used in field test(Non laboratory conditions). Droplet images have a motion blur because the exposure time of common CCD camera is too long for motion droplets in the microchip. Motion deblurring is an indispensable part for droplet dimension, shape and volume detection by image processings in order to improve the detection precision. The first step of image processing is the motion deblurring after image denoising. The motion blur is removed by motion deblurring. Secondly, the droplet image is divided into the target area and the background area by image threshold segmentation. Thirdly, target information of the droplet image is extracted through region of interest extraction. Finally, the shape and dimension are obtained by edge detection. The volume of droplet is calculated by the results of droplet dimension.The image denoising is the first step of droplet detection based on the image processing method. The result of image processing and the precision of droplet detection are influenced directly by the result of image denoising. In this research, the wavelet denoising algorithm which has good time-frequency characteristics is used for droplets image denoising. Furthermore, the wavelet soft-threshold and hard-threshold denoising method are improved. A threshold function between soft threshold and hard threshold are adopted by droplets image denoising.The shape, dimension and volume of droplet are obtained through droplet detection based on image processing. Image processing has limitations and the information of droplet is not enough. The droplet detection based on the principle of capacitive sensors is proposed. The optimal conditions for the droplets generation are obtained by numerical simulation and the capacitor microchip is designed according to the numerical simulation results. The capacitor microchip includes microchannel and capacitive transducer. Thereafter an external detection circuit is designed. The droplets causing a change in the capacitive sensor is very small since the size of droplets is very small in microchannels. The external detection circuit is used for detection the change of capacitive transducer and conversion the capacitance into voltage signal.To sum up, the shape, size, velocity, volume and inclusions concentration of droplet are not only obtained but the information of droplet in droplet microfluidic system can be detected accurately. The droplet microfluidic system is controlled according to the droplet detection results. It will be widely used in the field of biochemistry.