Microfluidic impedance sensors play a significant role in point-of-care applications, clinical tests and laboratory studies. Instead of the traditional signal process method with the envelope detector, filter banks and multistage amplifier, we have reported a new implement using synchronous sampling and orthogonal detecting. By studying the intensity and phase modulation in the cell impedance sensing progress, we have calculated the SNR of the new method compare with the raw data. Base on the physical model and the calculation, we have demonstrated a simulation result which shows a capability to detect the signal of 1um cell in a noisy environment

Jie DAI

Kecheng YANG

Yuhwa LO

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Sensors & Transducers, Vol. 162 , Issue 1, January 2014, pp. 42-46  42  Sensors & Transducers© 2014 by IFSA Publishing, S. L. http://www.sensorsportal.com     Studies of the Synchronous Sensing Method in the Microfluidic Cell Impedance Detection  1 Jie DAI, 2 Yuhwa LO, 1 Kecheng YANG 1 School of Optical and Electronic information, Huazhong University of Science and Technology, Wuhan 430074, China  2 Department of Electrical and Computer Engineering, University of California at San Diego,  La Jolla, CA, 92093-0407, United States of America E-mail: kcyang@mail.hust.edu.cn    Received: 21 October 2013   /Accepted: 9 January 2014   /Published: 31 January 2014   Abstract: Microfluidic impedance sensors play a significant role in point-of-care applications, clinical tests and laboratory studies. Instead of the traditional signal process method with the envelope detector, filter banks and multistage amplifier, we have reported a new implement using synchronous sampling and orthogonal detecting. By studying the intensity and phase modulation in the cell impedance sensing progress, we have calculated the SNR of the new method compare with the raw data. Base on the physical model and the calculation, we have demonstrated a simulation result which shows a capability to detect the signal of 1um cell in a noisy environment. Copyright © 2014 IFSA Publishing, S. L.  Keywords: Phase, Orthogonal wave, Multiply-add operation, Noise inhibition, Synchronous sampling.    1. Introduction  Microfluidic cell counters provide us powerful implements to point-of-care applications, clinical tests and laboratory studies [1, 2]. Compare to other cell detecting means with optical, acoustic and magnetic sensors, it is widely used since it’s low cost, label-free and easy to use [3-6].  Cell impedance has been studied by the cell counter researchers for years because it’s directly related to the cell size. By analyzing the response to DC/AC motivations, variant physical models and detecting methods have been developed [7-11]. For example, Gawad group has built an accurate microfluidic-electric model by inducing the frequency-permittivity bonding and geometric factor correction [12, 13]. Plenty of microfluidic impedance counters focuses on the signal intensity measurement were invented in recent years. Zhe Mei et al. reported an electrode design which can produced a uniform electric field and detected 7um cell with high resolution and low CV [14]. However, those devices suffered from the background noise which covered the weak signal of small cells. It's difficult, if not impossible, to make a post process circuit which can filter, amplify and recover the signal from a very small cell in the real application since it attenuates with the third power of the cell diameter. Also, the throughput range of the device is limited because the pass window of the filter bank on the PCB board cannot automatically fix to the cell signal with fluctuant frequency when the flow velocity changes. We reported a new signal process method based on the physical model of impedance measurement by using synchronous sampling and orthogonal detecting. The SNR of the signal is largely increased for cells with variant diameter without any envelope detector, filter banks or multistage amplifier.  With this method, it gives more probability to measure small cells like platelet or bacterial in  practical environment. Article number P_1747Sensors & Transducers, Vol. 162 , Issue 1, January 2014, pp. 42-46  43  Fig. 1. The Device Structure, Operation Principle and Signal Processing Diagram. (a) The demonstration of the sensor structure. The yellow rectangle pads are the surface electrodes. The red dash lines are the electric field lines. The green arrow shows the flow direction of the cell sample. The green sphere represents a cell entering the sensing area. The Vin_1 and Vin_2 indicate two separate motivation waves which are input into Channel 1 and 2. The V_ch1 and V_ch2 are the related voltage signal from the I-V convertor 1 and 2. (b) The signal processing flow. The differential amplifier between the I-V convertor and the Synchronous Sampler produces the difference of V_ch1 and V_ch2. The output and the reference wave are sampled simultaneously, processed and displayed on the screen.   2. Results   2.1. System Design and Work Flow  The design and work flow of a dual-channel micro Culter counter is shown in Fig. 1. In this model, two typical electric impedance [10] sensors were integrated into a microfluidic channel  (Fig. 1(a)). Two sine-wave voltages (Vin_1 and Vin_2) were separately applied to each surface electrode pairs placed face to face on the channel floor and ceiling. When the conductive solution fulfilled the sensing area between electrodes, an electric field was built to measure the impedance of the sample flow. The green arrow indicates the direction of cell suspension infused into the micro channel. Once a cell passing through the detection channel 1 (the left electrode pair), it slightly changed the impedance of this sensing area without disturbing the channel 2 (the right electrode pair). For a stable input voltage, the current of each channel was modulated by the target cell obeying Ohm's law. This modulation will be received and processed by the external electronics after being converted to voltage signal.  Unlike most of the present design which enhanced the raw signal from the impedance sensor with envelope detectors, filter banks and multistage amplifiers [14], a novel method based on the physical model is reported here with high resolution and sensitivity, especially in small cells or particles. Fig. 1(b) indicates the structure of the whole system including the post data processing. The signal V_ch1 and V_ch2 were generated by the inverting amplifier circuit when a cell ran through sensors. To raise the SNR, a differential amplifier was applied to inhibit the common noise. The output signal diffV  was sampled by a high speed synchronous sampler and turned to a digital signal. Meanwhile, a reference wave refV  which was orthogonal to Vin_1 and Vin_2 was also induced into the sampler. These two data streams were multiplied, then accumulated by a digital processor which also displayed the data  on screen.     Fig. 2. The equivalent model of an impedance sensor.   2.2. Principle of Methods  In the case of the classic Culter counter, several equivalent models were raised up to simplify the electric analysis [8, 11, 15]. Fig. 2 is a widely Sensors & Transducers, Vol. 162 , Issue 1, January 2014, pp. 42-46  44reported structure which sets the double layer capacitor dlC  in tandem with an RC parallel-serial network. In the network, mediaC  and mediaR  are the capacitance and resistance of the environmental media. When the cell displaces the same volume of highly conductive solution (like cell culture media or PBS), it will enlarge the total impedance by inducing the cell membrane capacitor membraneC and the cell cytoplasm resistance cytoplasmR . Base on this model, Gawad group has derived the total impedance totalZ  of the channel which the cell registered to [12, 13]. Considering the geometry factor and the frequency modulated permittivity, totalZ  can be expressed  as [13]:  11(1 )[(1 )(1 )]totaldlmedia membrane media membranecytoplasm membrane media mediaZj CR j C j R Cj R C +j R C     (1)  The parameters in equation (1) can be derived by using Maxwell’ mixture theory and the Schwarz-Christoffel mapping. According to the circuit design, the output of I-V convertor is:  01 _1 ( )| |f fout ztotal totalR R VV Vin Cos tZ Z     (2)  where out1V is the output from channel 1 which contains a cell. out2V  is the output from the empty channel 2. Fig 3(a) shows the difference of absolute value between the cell signal out1V  and the environment signal out2V . The curve rises at low frequency, reaches the top at around 100 kHz to1 MHz depend on the cell size and then goes flat at high frequency. The value decays exponentially with the cell diameter in a wide frequency range. Fig. 3(b) indicates the phase difference between out1V  and out2V . Similar to the absolute value, it variance a lot when cell size changes. The current methods generally focused on the signal intensity detection but ignored the phase information. Envelope detector, filter banks and multi-stage amplifier are used to detect the signal envelop, filter the noise and enhance the signal. However, it’s too difficult to distinguish the small cell since its initial SNR is too low. To minimize the environment noise, we added a differential circuit which can greatly reduce the common noise. The output of this stage is diffV , which can be described as:  1 1 | | ( )diff out out diff diffV V V V C os t     (3)      Fig. 3. The raw signal characteristics of the cell with variant sizes versus the frequency. (a) The difference of absolute impedance value between the channels with/without a cell versus the frequency. (b) The angular difference of impedance value between the channels with/without a cell versus the frequency.   We got both intensity and phase signal of diffV by multiplying it with an orthogonal wave refV , and    00 0| | ( ) ( )| | | |(2 ) ( )2 2mul diff refdiff diffdiff diffdiff diffV V VV V Cos t Sin tV V V VSin t Sin         (4)  The first item in the equation (4) is a second harmonic wave which can be removed by accumulation for more than one cycle. The second item is a DC component which can be largely increased while accumulating. Meanwhile, the differential-mode noise diffN  which follows the Gaussian white noise distribution will be inhibited. Here, the data SNR after processing was derived compare with the original SNR. Sensors & Transducers, Vol. 162 , Issue 1, January 2014, pp. 42-46  45 ( ) | |2| |diff diffprocesseddiffinitialSin VMNSNRVSNR, (5)  where M is the sampling number in each cycle, N is the cycle amount to accumulate,   is the standard deviation of Gaussian noise diffN  before data process. When diff is settled, we can greatly enhance the SNR by well setting the factor M and N.   2.3. Simulation Results  Base on the physical model, the geometry and electric parameters were set as below (See Table 1). The double layer capacitance dlC  was 30pF. The flow rate was 15 / min L . Artificial cell impedance was yielded in channel 1. Channel 2 without any cell was kept as the comparison. Two 5 Volts, 500 kHz cosine waves were separately added to the channels. The noise coupled into each channel followed Gaussian distribution 2(0, )N  , where 1 V  . Fig. 4 demonstrates the performance of the simulation result before and after the digital data processing. Here we set M=2000 and N=10.  Fig. 4(a), Fig. 4(b), Fig. 4(c) and Fig. 4(d) are the final output intensity with different cell diameter versus the time. The subpanel in each image shows the raw diffV signal. In Fig. 4(a), the SNR of the post processed data is around 3, while the signal of 1 um cell is totally submerged by the noise. Similar results occur in Fig. 4(b) and Fig. 4(c). The raw signal of 2 um cell is still invisible, although the final output is obvious. For 3 um cell, the raw SNR is just around 1. After the multiply-add operation, the signal looks much larger than the background noise. In Fig. 4(d), the noise of raw data is about 300 mV. After the processing, the noise is almost completely removed.   Table 1. Geometry and electric parameters.  Name Value Name Value Channel Width 50 um Culture media permittivity 90 Channel Height 50 um Cytoplasm permittivity 60 Electrode Width 20 um Culture media conductivity 1.6 S/m Thickness of cell membrane 5 nm Cytoplasm conductivity  0.6 S/m Cell membrane permittivity 11 Cell membrane conductivity 1e-8 S/m      Fig. 4. The performance of the signal process. The 4 big subpanels show the data after a multiply-add operation. These 4 small figures in the down left of each subpanel are the raw data before the digital process. (a), (b), (c) and (d) indicate the signal of the 1 um, 2 um, 3 um and 7 um cells. (5)Sensors & Transducers, Vol. 162 , Issue 1, January 2014, pp. 42-46  463. Conclusions  We have reported a new processing method which can greatly raise the SNR of the single cell counting base on the synchronous sampling and orthogonal detecting. By taking this approach, it’s more probable to capture the cell passing event in a noisy environment. Furthermore, it gives a chance to measure the small diameter cell, such as platelet or bacterial, which is difficult to be detected with the traditional post process means. Additionally, this method is simple, highly automatic and easy  to operate.   Acknowledgements  Here we thank Mengqi Huang, Hao Liu, Junwei Ye for the great help in the academic discussion. This work is funded by China Scholarship Council.   References  [1]. P. Yager, T. Edwards, E. Fu, et al., Microfluidic diagnostic technologies for global public health, Nature, Vol. 442, 2006, pp. 412-418. [2]. C. D. Chin, V. Linder, S. K. Sia, Lab-on-a-chip devices for global health: past studies and future opportunities, Lab on a Chip, Vol. 7, 2007, pp. 41-57. [3]. D. Ateya, J. Erickson, P. Howell, Jr., et al., The good, the bad, and the tiny: a review of microflow cytometry, Analytical and Bioanalytical Chemistry, Vol. 391, 2008, pp. 1485-1498. [4]. J. Godin, C.-H. Chen, S. H. Cho, et al., Microfluidics and photonics for Bio-System-on-a-Chip: A review of advancements in technology towards a microfluidic flow cytometry chip, Journal of Biophotonics, Vol. 1, 2008, pp. 355-376. [5]. A. Valero, T. Braschler, N. Demierre, et al., A miniaturized continuous dielectrophoretic cell sorter and its applications, Biomicrofluidics, Vol. 4, 2010, pp. 022807. [6]. D. N. Schafer, E. A. Gibson, E. A. Salim, et al., Microfluidic cell counter with embedded opticalfibers fabricated by femtosecond laser ablationand anodic bonding, Optical Express, Vol. 17, 2009,  pp. 6068-6073. [7]. K. Cheung, S. Gawad, P. Renaud, Impedance spectroscopy flow cytometry: On-chip label-free cell differentiation, Cytometry Part A, Vol. 65A, 2005, pp. 124-132. [8]. X. Cheng, Y.-S. Liu, D. Irimia, et al., Cell detection and counting through cell lysate impedance spectroscopy in microfluidic devices, Lab on a Chip, Vol. 7, 2007, pp. 746-755. [9]. C. D. Falokun, G. H. Markx, Electrorotation of beads of immobilized cells, Journal of Electrostatics, Vol. 65, 2007, pp. 475-482. [10]. S. Gawad, L. Schild, P. Renaud, Micromachined impedance spectroscopy flow cytometer for cell analysis and particle sizing, Lab on a Chip, Vol. 1, 2001, pp. 76-82. [11]. L. Yang, Y. Li, C. L. Griffis, et al., Interdigitated microelectrode (IME) impedance sensor for the detection of viable Salmonella typhimurium, Biosensors and Bioelectronics, Vol. 19, 2004, pp. 1139-1147. [12]. S. Gawad, K. Cheung, U. Seger, et al., Dielectric spectroscopy in a micromachined flow cytometer: theoretical and practical considerations, Lab on a Chip, Vol. 4, 2004, pp. 241-251. [13]. M. Hywel, S. Tao, H. David, et al., Single cell dielectric spectroscopy, Journal of Physics D: Applied Physics, Vol. 40, 2007, pp. 61. [14]. M. Zhe, C. Sung Hwan, A. Zhang, et al., Counting leukocytes from whole blood using a lab-on-a-chip Coulter counter, in Proceedings of the IEEE Annual International Conference of the Engineering in Medicine and Biology Society (EMBC’2012), 28 August 2012 – 1 September 2012, pp. 6277-6280. [15]. L. Yang, C. Ruan, Y. Li, Detection of viable Salmonella typhimurium by impedance measurement of electrode capacitance and medium resistance, Biosensors and Bioelectronics, Vol. 19, 2003, pp. 495-502.  ___________________                  2014 Copyright ©, International Frequency Sensor Association (IFSA) Publishing, S. L. All rights reserved. (http://www.sensorsportal.com)  

Studies of the Synchronous Sensing Method in the Microfluidic Cell Impedance Detection

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Studies of the Synchronous Sensing Method in the Microfluidic Cell Impedance Detection

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