- Email: [email protected]
Airborne particulate matter classification and concentration detection based on 3D printed virtual impactor and quartz crystal microbalance sensor
Sensors and Actuators A 238 (2016) 379–388
Contents lists available at ScienceDirect
Sensors and Actuators A: Physical journal homepage: www.elsevier.com/locate/sna
Airborne particulate matter classification and concentration detection based on 3D printed virtual impactor and quartz crystal microbalance sensor Jiuxuan Zhao a,1 , Minliang Liu a,1 , Liang Liang b , Wen Wang c , Jin Xie a,∗ a
State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, PR China Tissue Mechanics Laboratory, The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA c School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, PR China b
a r t i c l e
i n f o
Article history: Received 14 August 2015 Received in revised form 15 December 2015 Accepted 29 December 2015 Available online 6 January 2016 Keywords: Virtual impactor 3D printing PM 2.5 monitoring Quartz crystal microbalance
a b s t r a c t In this paper, design, fabrication and experiment of a miniature system for detection of airborne particulate matter (PM) are presented. The miniature system contains a virtual impactor and a quartz crystal microbalance (QCM) resonant sensor. The virtual impactor is fabricated by three-dimensional (3D) printing process for classifying airborne particles according to their size. The QCM resonant sensor is utilized to detect the mass of the separated particles from the virtual impactor. The design of virtual impactor is optimized by computational fluid dynamics simulation and the QCM for its resonance in thickness shear mode is analyzed by finite element method. Silicon dioxide powders with diameter in the range from 0.5 to 8 m are successfully separated according to their size by the virtual impactor, which indicates that the classification characteristic coincides with the theoretical and simulation results. PM concentration in a chamber is measured by the proposed monitoring system and the experimental results show that the resonant frequency of the QCM turns downward linearly with the PM mass loading increasing. © 2016 Elsevier B.V. All rights reserved.
1. Introduction Airborne particulate matter (PM) is a complex mixture of extremely small particles and liquid droplets, which consists of a number of components including acids, metals, tobacco smoke, dust and organic chemicals. Particle pollution, especially fine particles that have a diameter of 2.5 m or smaller (PM 2.5), can penetrate deep into the lungs and cause serious health effects, such as asthma, nonfatal heart attacks, respiratory symptoms, ecological allergies and decreased lung function [1–4]. In addition, fine particles are the main cause of various environment problems, such as reduced visibility, emissions cloud processes and heterogeneous chemical reactions [5,6]. With increasing awareness of the negative health and environment impacts, there has been an increasing interest in monitoring of PM 2.5. Although commercial systems have been successful to some extent, they are bulky and expensive. Various methods to realize detection of PM have been proposed
∗ Corresponding author. E-mail address: [email protected] (J. Xie). 1 These authors contributed equally to this work and should be considered co-first authors. http://dx.doi.org/10.1016/j.sna.2015.12.029 0924-4247/© 2016 Elsevier B.V. All rights reserved.
with the aim to decrease volume and cost of the monitoring system. The concept based on thermophoretic deposition and film bulk acoustic resonator (FBAR) mass sensor has been demonstrated and validated with a sensitivity of 2 g/m3 with 10 min of testing time [7]. Also, a thermally actuated high-frequency resonator with piezoresistive readout was utilized as particulate mass sensor [8]. A novel microsensor for capacitive detection of PM directly in air was also presented [9]. Furthermore, the particles can be charged and concentration can be known by measuring charge quantity with the charge sensor [10–12]. In a PM monitoring system, it is essential to classify particles according to their size, so that they can be monitored accurately. There are a variety of methods used for particles classification, including inertial classification, gravitational sedimentation, centrifugation, and thermal precipitation [13]. The inertial classification through a virtual impactor (VI) is widely used in airborne particles sampling for its high performance and simple implementation[14,15]. Most of the reported virtual impactor are based on in-plane structure and assembled with other parts of the monitoring system. Recently, three-dimensional (3D) printing process has been widely used to fabricate micro channels for Microelectromechanical Systems (MEMS) [16] and microfluidic systems
380
J. Zhao et al. / Sensors and Actuators A 238 (2016) 379–388
Fig. 1. Schematic of the 3D printed virtual impactor integrated with QCM sensor for detecting airborne particles.
[17] due to its flexibility in geometrical designs. The 3D printing process can also be applied to the designs of virtual impactor to realize complicated 3D micro channels without assembly effort, but so far no relevant studies have been reported. In this work, we present a 3D printed virtual impactor to avoid assembly tolerance and maintain accurate alignment, which can minimize nozzle wall losses [18] and make the whole system compact. A quartz crystal microbalance (QCM) is utilized as a sensor to detect mass of the particles, which are separated according to their size by the virtual impactor. QCM is a high precision measuring tool with the characteristics of simple structure, low cost and real-time output [19,20]. The miniaturized PM monitoring system has advantages of low cost, low power consumption, ease of use and good portability. 2. Design and simulation The proposed design of PM monitoring system are shown in Fig. 1. The miniature system consists of a virtual impactor, a QCM sensor and a QCM holder that is used to ensure the QCM floating. There are three serpentine channels in the virtual impactor to separate particles into two groups: fine particle stream and coarse particle stream. The particles pass into the virtual impactor from the particles inlet and then are separated according to their size. The particles with diameter of 2.5 m and smaller get into the two major flow channels and then absorbed onto the surface of the QCM electrode, which will induce a shift of resonant frequency of the QCM. In this section, design and simulation of the virtual impactor will be given first, and then the QCM sensor with characterization method will be introduced. 2.1. Virtual impactor
Fig. 2. (a) Flow steam line of the virtual impactor (obtained using CFD simulation) with 90% major flow and 10% minor flow. (b) Schematic of the particles classification.
accelerated through a converging injection nozzle. Large particles follow a straight flow called minor flow because of their large inertial momentum. Small particles, on the other hand, migrate to the side channels known as the major flow due to their small inertial momentum, which is normal to the injection nozzle. Such design of virtual impactor allows particles to be classified according to their size. For effective classification of the particles, the flow needs to be precisely controlled, so that more than 90% of particles are diverted to the major flow and less than 10% of particles to the minor flow [15]. In this study, a virtual impactor with cut-off diameter of 2.5 m is designed. The cut-off diameter is the aerodynamic diameter of a particle with a collection efficiency of 50%. The collection efficiency depends on the Stokes number (Stk), Reynolds number (Re), flow rate (Q) and injection nozzle width (W). According to the conventional impaction theory, the dimensionless Stokes number is a critical factor for impaction and is given by [10] 2
A virtual impactor has been used to sample airborne particles on account of its collection efficiency and small internal particle loss. Fig. 2 shows the flow distribution and particle classification of a virtual impactor. In the virtual impactor, the influx flow is
Stk =
2
p dp QCc p dp UCc U = = 9W W/2 9hW2
(1)
where is the relaxation time (the time constant in the exponential decay of the particle velocity due to drag), U is the average
J. Zhao et al. / Sensors and Actuators A 238 (2016) 379–388 Table 1 Design parameters of the virtual impactor.
to the inlet. The collection efficiency of the virtual impactor can be calculated as
Parameters
Symbols
Values
Flow rate Width of the injection nozzle Thickness of the microchannel Stokes number Reynolds number Jet-to-plate distance Width of minor flow channel Length of minor flow channel Width of major flow channel Length of major flow channel
Q W h Stk50 Re S Wmi Lmi Wma Lma
0.27 L/min 1 mm 1 mm 0.58 984 2 mm 1 mm 112 mm 5 mm 18 mm
=
Nin−Nmi Nin
× 100
air velocity in the impactor nozzle, p is the particle density, dp is the particle diameter, Cc is Cunningham slip correction factor, is the dynamic viscosity of a particle (1.18 × 10−5 Pa × s) and h is the virtual impactor height. The slip correction factor is specified as
dp −1.10 2
(2)
where is the mean air molecule free path (0.066 m) and considered only when the particle diameter is smaller than 1.0 m, otherwise its value is assumed to be 1. The nozzle width of virtual impactor is one of the most important design parameters. According to Eq. (1), W can be expressed as
W=
2
p dp QCc 9Stk50 h
(3)
where Stk50 is the Stk at the collection efficiency of 50 percent. When the channel is rectangular, the suggested value of Stk50 is 0.58 [21]. The flow rate is set to be 0.27 L/min and the injection nozzle width W is 1 mm. The jet-to-plate distance S is calculated using the injection nozzle width W, because the minimum S to W ratio should be larger than 1.5 for the impactor. In order to achieve a sharp curve of collection efficiency, laminar flow in the flow channels is required, so that we optimize the nozzle width and flow rate to realize the Reynolds number in the range from 500 to 3000, by using Re =
4WhU (W + h)
where is the air density. Following the above design procedure, the Re number is calculated to be 984 for laminar regime. The design parameters of the virtual impactor are listed in Table 1. Computational fluid dynamic (CFD) simulation (ANSYS Fluent 15.0, ANSYS Inc.) was employed to analyze air flow and classification of particles in the virtual impactor. The inlet was modeled as velocity input, and the outlet was out flow. Pressure distribution in the microchannel is analyzed to design proper length and width of the microchannel to ensure the needed flow distribution. As shown in Fig. 3(a), pressure in the minor flow channel is higher than the one in the major flow channels due to narrow and long topology of minor flow channel. Fig. 3(b) shows the flow distribution which agrees with the expected ratio value. Discrete particles were then introduced into the flow. In the simulation, only classification area of the virtual impactor is analyzed to save solution time. The outlet pressures were set according to the previous simulation results. Particles ranging from 0.5 to 8 m in size were supplied to the inlet with a flow rate of 0.27 L/min. The simulation results (from Fig. 3(c) to (e)) show that particles with diameter smaller than the cut-off value tend to follow the major flow, while larger particles move straight down the minor flow. Collection efficiency is the ratio of the particle concentration collected in the major channels to particle concentration supplied
(5)
where Nin and Nmi are the number-concentration of the particles at the inlet and minor flow, respectively. The simulation result of collection efficiency are shown in Fig. 4. The cutoff diameter is determined by curve fitting using the Boltzmann sigmoidal function (Origin 8.0, OriginLab Inc.) as follows [22]
A1 − A2
E dp = A2 +
2 2 Cc = 1 + 1.257 + 0.40 exp dp dp
381
1+e
dp −d0
−
(6)
b
where E dp is the collection efficiency for a given particle aerodynamic diameter dp , d0 is the median aerodynamic diameter, b is the width of fitting, and A1 and A2 are the coefficients calculated by the regression. The designed cut-off diameter of the proposed virtual impactor is 2.5 m. According to curve fitting, the diameter is 2.65 m when the collection efficiency is 50%, with approximately 6% deviation from the designed value. From the simulation result, it is seen that the designed virtual impactor meets the requirement. 2.2. QCM sensor As shown in Fig. 5, a single QCM is composed of a thin quartz crystal plate with gold electrodes on each side. AT cut quartz crystal is widely used to be made of microbalances since the resonant frequency is temperature independent [23], which is important for the microbalances to be used as mass sensors. The application of a voltage between the electrodes results in a shear deformation within the quartz crystal due to the piezoelectric properties and crystalline orientation of the quartz [24]. The thickness shear vibration mode with maximum displacement at the crystal surface is normally applied for sensor application, as shown in Fig. 5. For higher resonant frequency, the QCM is much sensitive to surface perturbation. The Sauerbery equation gives the linear relationship between resonance frequency shift of QCM and mass attached on the electrode surface [25]: 2
2f0 f = − √ m = −m A q q
(7)
where f and m are the frequency shift and mass change, respectively, f0 is the fundamental frequency, A is the active area where the crystal is coated with electrodes on both sides, q is the shear modulus (29.47 GPa) and q is the density (2648 kg/m3 ) of the quartz crystal. The sensitivity of the QCM sensor is calculated to be = −2.88 × 1011 Hz/kg. The minus sign means that the resonant frequency decreases with the increase of mass loading. Finite element simulation using COMSOL was performed to model the resonant frequency of the QCM and thickness shear deformation. The eigen-frequency analysis was performed to yield the quartz surface displacement at thickness shear mode, as shown in Fig. 5. In the shearing vibration mode, the shear deformation is zero in the middle of the quartz thickness and is maximum but with opposite phases at the top and bottom surfaces of the quartz substrate. 2.3. Fabrication of virtual impactor A 3D computer model of the virtual impactor was created in SolidWorks, with which the device was printed directly from the computer-aided design (CAD) file by ProJet® 3510 series professional 3D printer (3D System Inc.). The material of the structure is
382
J. Zhao et al. / Sensors and Actuators A 238 (2016) 379–388
Fig. 3. CFD simulation result for the 3D printed virtual impactor. (a) pressure distribution. (b) flow stream line. (c) 0.5 m particle motion. (d) 2.5 m particle motion. (e) 8 m particle motion.
Fig. 4. Collection efficiency curve after CFD simulation and Boltzmann sigmoidal fitting. Percent error between the design and simulation cut-off diameter was approximately 6%.
Visijet M3 crystal which provides true plastic look and the support material is a white, melt-away wax. The heat distortion temperature at pressure of 0.45 MPa for the VisiJet M3Crystal is 56 ◦ C. The process starts with the 3D model and then the software slices the file and sends it to the printer. The printhead prints layers of UV curable liquid plastic onto a flat platform. Also, the wax support
material is jetted to fill the features of serpentine channels. The UV lamp flashes to solidify material and create a fully cured plastic part. Upon completion of the printing process, a high-pressure water jet exposed the final device design by dissolving the support material. The photograph of the printed virtual impactor are shown in Fig. 6.
J. Zhao et al. / Sensors and Actuators A 238 (2016) 379–388
383
Fig. 5. Schematic view of the QCM, with the thickness shearing vibration mode on the right by finite element simulation using COMSOL. The vibration mode shape includes two views, the 3D view and the side view.
Fig. 6. Photograph of the virtual impactor fabricated by 3D printed technology.
Chamfers were made on the corner of the channels to minimize particle wall losses.
and a mass flow controller (MFC, 5964 Series, Brook Instrument Inc.) was utilized to precisely control the flow rate. 3.2. Characterization of the system
3. Experiment setup 3.1. Characterization of the virtual impactor Fig. 7(a) shows the experimental setup for evaluating the particles classification characteristics of the virtual impactor. The setup consists of two sections, the air supply and the PM generator. In the PM generator chamber, silicon dioxide particles with diameter in the ranging from 0.5 to 8 m were generated in suspension using two fans. The generated particles were supplied to the virtual impactor with a flow rate of 0.27 L/min. In the virtual impactor, the particles were delivered to the major flow and minor flow channels. A polymethyl methacrylate (PMMA) slide was placed on the outlet of the virtual impactor to absorb particles. The required air flow was supplied by a vacuum pump (Rocker 300, Rocker Scientific. Inc.)
Fig. 7(b) shows the experimental setup for particle mass detection. The QCM was placed on one of the major flow outlet to replace the PMMA slide to absorb the particles. A thin photoresist film was coated on the electrode surface of the QCM to improve the surface adhesion. The resonant frequency of the QCM sensor was measured by a network analyzer (Agilent E5061B) for impedance analysis. The resonant frequency had a shift when silicon dioxide particles were loaded on the adhesive surface. The photograph of the experimental setup are shown in Fig. 8. 4. Results and discussion Particles distribution test has been preliminarily performed in order to experimentally evaluate the classification characterization of the virtual impactor. After pumping in the air with particles for
384
J. Zhao et al. / Sensors and Actuators A 238 (2016) 379–388
Fig. 7. Experimental setups (a) for examining the classification characteristics of the virtual impactor, and (b) for particle mass detection using the QCM sensor.
Fig. 8. Photograph of the experimental setup for prototype characterization.
5 min, the PMMA slide located at the outlet of the microchannels was coated with the particles. We observed the major flow and minor flow areas and measured the diameter of the particle using an optical microscope (Zeiss Axio). Fig. 9(a) shows the particles in the major flow area, most of which are smaller than 2.5 m in diameter. Also, several particles larger than 2.5 m in diameter are found since there is a small collection efficiency for large particles in the major flow area. In the area of the minor flow outlet, the particle number evidently decreases and the diameter of particles are mostly larger than 2.5 m, as shown in Fig. 9(b). Several particles with diameter smaller than 2.5 m also can be observed in the area of the minor flow outlet, which coincides with the theoretical and simulation analysis. The statistical result for the PM in the area of the major flow output through image processing are shown in Fig. 10, which indicates the proper design of the virtual impactor.
The frequency response of the QCM was measured at ambient pressure and room temperature, as shown in Fig. 11. The resonant frequency is about 4.985 MHz and the quality factor (Q) is more than 33,000. The phase shift at resonance is about 180 degrees. Besides fundamental resonant frequency, we can also find the second and third resonances, which are the 3rd and 5th overtones of the QCM resonator. Fig. 12(b) shows the measured frequency-impedance of the QCM at three stages: before coating film, after coating film and loading PM. The microscope images of the QCM surface for these three stages are shown in Fig. 12(a). Before the adhesive film was coated on the electrode of the QCM, the resonant frequency was about 4.985 MHz and the impedance at resonance was about 63 . When the electrode was coated with photoresist, the resonant frequency turned downward to 4.956 MHz and the impedance
J. Zhao et al. / Sensors and Actuators A 238 (2016) 379–388
385
Fig. 9. Optical micrograph of the PMMA slide after particles distribution. (a) The area of the major flow outlet, (b) the area of the minor flow outlet.
Fig. 10. Statistical result for the PM in the area of the major flow output through image processing.
increased to 74 . After PM loading, the resonant frequency of the QCM decreases and the impedance increases. The system for PM 2.5 detection was characterized. The concentration in the PM generator chamber was considered to be constant since only small amount of particles flowed through the virtual impactor. The measurement lasted 40 min and the resonant frequency was recorded in every 5 min. The experimental result is shown in Fig. 13, with data linearly fitted. The testing result indicates the mass loading on the QCM sensor increases linearly with time. The fitting sensitivity is about 11 Hz/min for the chamber, which implies that the QCM sensor dynamically responds to the mass concentration as particles are continuously injected into the diluting chamber. During 40 min of experiment, the frequency shift was about 443 Hz. According to the sensitivity of the QCM indicated in Eq. (7), the mass of particles deposited on the adhesive film was 1.537 g. The total volume of the air passing through the
virtual impactor can be calculated by multiplying the constant flow rate with the particle injection time. Hence the mass concentration in the air can be obtained by dividing the measured mass loading with the air volume. After calculation, the concentration of the test chamber was 142 g/m3 . The precision for measuring the PM 2.5 concentration depends on the minimum detectable mass of the QCM sensor, flow rate and testing duration. Due to the continuous mechanical vibrations of the piezoelectric crystal and other sources of noise such as the air flow, the resonant frequency of QCM sensor will fluctuate when working [26]. The short-term frequency fluctuation of the QCM ıf0 was found to be less than 1 Hz (0.2 ppm) over one hundred measurements. Therefore, the mass resolution of the QCM sensor is calculated to be ım = −1 ıf0 = 3.47ng [27]. However, in practice the minimum detectable mass is larger than this calculated value because of other sources of noise ignored during calculation.
386
J. Zhao et al. / Sensors and Actuators A 238 (2016) 379–388
Fig. 11. The measured transmission magnitude and phase of the QCM.
Fig. 12. (a) The microscope images of the QCM surface at three stages: before coating film, after coating film and loading particulate matter. (b) Measured resonant frequency shifts of the QCM sensor using impedance analysis at the three stages. The resonant frequency appears at the point of minimum impedance.
J. Zhao et al. / Sensors and Actuators A 238 (2016) 379–388
387
Fig. 13. The experimental and curve fitting results with PM, and the experimental result without PM for comparison showing the linear response of the miniature monitoring system in a PM generator chamber.
5. Conclusion Airborne particulate matter (PM) especially with diameter of 2.5 m and smaller presents a significant health risk and environment pollution, and there is a great need for a portable PM monitor that is capable of recording and tracking personal PM exposure. In this paper, we presented the design and fabrication of a miniature system for PM 2.5 concentration detection and experimentally evaluated the performance by exposing the sensor in a chamber full with fine silicon dioxide powder. The system is based on a virtual impactor to classify particles and a QCM sensor to detect the separated particles mass. The virtual impactor was fabricated by 3D printing process, which avoids assembly tolerance and maintains accurate alignment. After classification, the particles in the major flow area are mostly with diameter smaller than 2.5 m, which verifies the good performance of the virtual impactor. The miniaturized system was demonstrated to be a low-cost and real-time environmental monitoring tool, which paves the way to portable devices for pervasive mapping of air pollution. The system can be further improved by removing particles absorbed on the electrode surface of the QCM to implement repeated measurement [28,29]. Furthermore, by decreasing the minimum dimensions of the 3D printing process, thus decreasing the required flow rate, the air flow can be supplied by a more compact pump to generate particle flow motion [30]. The QCM sensor also can be integrated with a signal processing circuit. Therefore, it is possible to realize a small and portable PM monitoring according to such strategy.
Acknowledgements This work is supported by the “Zhejiang Provincial Natural Science Foundation of China (No. LY14E050018)”, the “National Natural Science Foundation of China (No. 51475423 and No.
51275465)”, and the “Zhejiang Open Foundation of the Most Important Subjects”.
References [1] P.A. Solomon, P. Gehr, D.H. Bennett, R.F. Phalen, L.B. Méndez, B. Rothen-Rutishauser, et al., Macroscopic to microscopic scales of particle dosimetry: from source to fate in the body, Air Qual. Atmos. Health 5 (2012) 169–187. [2] N. Künzli, R. Kaiser, S. Medina, M. Studnicka, O. Chanel, P. Filliger, et al., Public-health impact of outdoor and traffic-related air pollution: a European assessment, Lancet 356 (2000) 795–801. [3] C.A. Pope III, D.W. Dockery, Health effects of fine particulate air pollution: lines that connect, J. Air Waste Manage. Assoc. 56 (2006) 709–742. [4] M. Kampa, E. Castanas, Human health effects of air pollution, Environ. Pollut. 151 (2008) 362–367. [5] M.Z. Jacobson, Y.J. Kaufman, Y. Rudich, Examining feedbacks of aerosols to urban climate with a model that treats 3-D clouds with aerosol inclusions, J. Geophys. Res. Atmos. 1984–2012 (2007) 112. [6] K. Tsigaridis, M. Kanakidou, Secondary organic aerosol importance in the future atmosphere, Atmos. Environ. 41 (2007) 4682–4692. [7] I. Paprotny, F. Doering, P.A. Solomon, R.M. White, L.A. Gundel, Microfabricated air-microfluidic sensor for personal monitoring of airborne particulate matter: design, fabrication, and experimental results, Sens. Actuators A 201 (2013) 506–516. [8] A. Hajjam, J.C. Wilson, S. Pourkamali, Individual air-borne particle mass measurement using high-frequency micromechanical resonators, Sens. J. IEEE 11 (2011) 2883–2890. [9] M. Carminati, L. Pedalà, E. Bianchi, F. Nason, G. Dubini, L. Cortelezzi, et al., Capacitive detection of micrometric airborne particulate matter for solid-state personal air quality monitors, Sens. Actuators A 219 (2014) 80–87. [10] Y.-H. Kim, D. Park, J. Hwang, Y.-J. Kim, Integrated particle detection chip for environmental monitoring, Lab on a Chip 8 (2008) 1950–1956. [11] G. Jaramillo, C. Buffa, M. Li, F.J. Brechtel, G. Langfelder, D.A. Horsley, MEMS electrometer with femtoampere resolution for aerosol particulate measurements, Sens. J. IEEE 13 (2013) 2993–3000. [12] J. Zhao, H. Ding, J. Xie, Electrostatic charge sensor based on a micromachined resonator with dual micro-levers, Appl. Phys. Lett. 106 (2015) 233505. [13] P. Kulkarni, P.A. Baron, K. Willeke, Aerosol Measurement: Principles, Techniques and Applications, John Wiley & Sons, 2011. [14] D. Liang, W.-P. Shih, C.-S. Chen, C.-A. Dai, A miniature system for separating aerosol particles and measuring mass concentrations, Sensors 10 (2010) 3641–3654.
388
J. Zhao et al. / Sensors and Actuators A 238 (2016) 379–388
[15] Y.-H. Kim, J.-Y. Maeng, D. Park, I.-H. Jung, J. Hwang, Y.-J. Kim, Micromachined cascade virtual impactor with a flow rate distributor for wide range airborne particle classification, Appl. Phys. Lett. 91 (2007) 043512. [16] S.-Y. Wu, C. Yang, W. Hsu, L. Lin, 3D-printed microelectronics for integrated circuitry and passive wireless sensors, Microsyst. Nanoeng. 1 (2015). [17] J.L. Erkal, A. Selimovic, B.C. Gross, S.Y. Lockwood, E.L. Walton, S. McNamara, et al., 3D printed microfluidic devices with integrated versatile and reusable electrodes, Lab on a Chip 14 (2014) 2023–2032. [18] B.W. Loo, C.P. Cork, Development of high efficiency virtual impactors, Aerosol. Sci. Technol. 9 (1988) 167–176. [19] J. Lee, J. Jang, D. Akin, C.A. Savran, R. Bashir, Real-time detection of airborne viruses on a mass-sensitive device, Appl. Phys. Lett. 93 (2008) 013901. [20] T. Lederer, B.P. Stehrer, S. Bauer, B. Jakoby, W. Hilber, Utilizing a high fundamental frequency quartz crystal resonator as a biosensor in a digital microfluidic platform, Sens. Actuators A 172 (2011) 161–168. [21] J.S. Haglund, A.R. McFarland, A circumferential slot virtual impactor, Aerosol. Sci. Technol. 38 (2004) 664–674. [22] P. Demokritou, S.J. Lee, P. Koutrakis, Development and evaluation of a high loading PM2. 5 speciation sampler, Aerosol. Sci. Technol. 38 (2004) 111–119. [23] J. Zhu, S. Huang, J. Ye, X. Zhang, G. Liu, Design of a quartz crystal with transparent electrode used for both QCM–D and LSPR technology, Sens. Actuators A 229 (2015) 141–146. [24] D. Ballantine Jr, R.M. White, S.J. Martin, A.J. Ricco, E. Zellers, G. Frye, et al., Acoustic Wave Sensors: Theory, Design, & Physico-Chemical Applications, Academic press, 1996. [25] G. Sauerbrey, Verwendung von Schwingquarzen zur Wägung dünner Schichten und zur Mikrowägung, Z. Angew. Phys. 155 (1959) 206–222. [26] L. Rodriguez-Pardo, J.F. Rodriguez, C. Gabrielli, H. Perrot, R. Brendel, Sensitivity, noise, and resolution in QCM sensors in liquid media, Sens. J. IEEE 5 (2005) 1251–1257. [27] K. Ekinci, X. Huang, M. Roukes, Ultrasensitive nanoelectromechanical mass detection, Appl. Phys. Lett. 84 (2004) 4469–4471. [28] Q. Qi, G.J. Brereton, Mechanisms of removal of micron-sized particles by high-frequency ultrasonic waves, ultrasonics, ferroelectrics, and frequency control, IEEE Trans. 42 (1995) 619–629. [29] H.S. Wasisto, S. Merzsch, A. Waag, E. Uhde, T. Salthammer, E. Peiner, Evaluation of photoresist-based nanoparticle removal method for recycling silicon cantilever mass sensors, Sens. Actuators A 202 (2013) 90–99. [30] I. Paprotny, F. Doering, R.M. White. MEMS particulate matter (PM) monitor for cellular deployment particulate matter (PM) monitor for cellular deployment, IEEE Sensorsconference, pp 2010 2435–2440.
Biographies Jiuxuan Zhao received the B.Eng. degree from LuDong University, Yantai, China in 2013. Now he is pursuing master degree in Zhejiang University, Hangzhou, China. His research interest is microelectromechanical systems (MEMS) sensors and actuators.
Minliang Liu received the B.Eng. degree from Zhejiang University, Hangzhou, China, in 2015. Now he is pursuing PhD degree in Georgia Institute of Technology, GA, USA. His research interest includes design of micro sensors & actuators and cardiovascular biomechanics. Liang Liang received the B.Eng. degree from Tsinghua Univeristy, Beijing, China in 2002, and the Ph.D. degree from Yale University, CT, USA, in 2013. He is currently working as a post-doc researcher at Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, GA, USA. His research interests are in the areas of imaging informatics – biomedical image analysis, intelligent algorithms for sensors and robotics. Wen Wang received the B.Eng. degree from Hangzhou Dianzi University, Zhejiang, China, in 1990, the M.Eng. degree and the Ph.D. degree from Zhejiang University, Hangzhou, China, in 1992 and 1996, respectively. From 1996 to 2012, he worked in Zhejiang University. From 2009 to 2010, he worked in the Center for Precision Metrology of the University of North Carolina at Charlotte, NC, USA as a visiting researcher. In July 2012, he joined the School of Mechanical Engineering, Hangzhou Dianzi University, Zhejiang, China, as a professor. His research interests include Micro/Nano measuring and control technology, coordinate measuring machines, precision engineering and mechatronics. Jin Xie received the B. Eng. degree from Tsinghua University, Beijing, China, in 2000, the M.Eng. degree from Zhejiang University, Hangzhou, China, in 2003, and the Ph.D. degree from Nanyang Technological University, Singapore, in 2008. From 2007 to 2011, he worked in Institute of Microelectronics, Singapore. In June 2011, he joined the Department of Mechanical Engineering, University of California, Berkeley, CA, USA, as a post-doc researcher. In October 2012, he joined the Department of Mechanical Engineering, Zhejiang University, Hangzhou, China, as a professor. His research interests include microelectromechanical systems (MEMS) design and processes, energy harvesters, inertial sensors, acoustics and vibration measurement.
Contents lists available at ScienceDirect
Sensors and Actuators A: Physical journal homepage: www.elsevier.com/locate/sna
Airborne particulate matter classification and concentration detection based on 3D printed virtual impactor and quartz crystal microbalance sensor Jiuxuan Zhao a,1 , Minliang Liu a,1 , Liang Liang b , Wen Wang c , Jin Xie a,∗ a
State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, PR China Tissue Mechanics Laboratory, The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA c School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, PR China b
a r t i c l e
i n f o
Article history: Received 14 August 2015 Received in revised form 15 December 2015 Accepted 29 December 2015 Available online 6 January 2016 Keywords: Virtual impactor 3D printing PM 2.5 monitoring Quartz crystal microbalance
a b s t r a c t In this paper, design, fabrication and experiment of a miniature system for detection of airborne particulate matter (PM) are presented. The miniature system contains a virtual impactor and a quartz crystal microbalance (QCM) resonant sensor. The virtual impactor is fabricated by three-dimensional (3D) printing process for classifying airborne particles according to their size. The QCM resonant sensor is utilized to detect the mass of the separated particles from the virtual impactor. The design of virtual impactor is optimized by computational fluid dynamics simulation and the QCM for its resonance in thickness shear mode is analyzed by finite element method. Silicon dioxide powders with diameter in the range from 0.5 to 8 m are successfully separated according to their size by the virtual impactor, which indicates that the classification characteristic coincides with the theoretical and simulation results. PM concentration in a chamber is measured by the proposed monitoring system and the experimental results show that the resonant frequency of the QCM turns downward linearly with the PM mass loading increasing. © 2016 Elsevier B.V. All rights reserved.
1. Introduction Airborne particulate matter (PM) is a complex mixture of extremely small particles and liquid droplets, which consists of a number of components including acids, metals, tobacco smoke, dust and organic chemicals. Particle pollution, especially fine particles that have a diameter of 2.5 m or smaller (PM 2.5), can penetrate deep into the lungs and cause serious health effects, such as asthma, nonfatal heart attacks, respiratory symptoms, ecological allergies and decreased lung function [1–4]. In addition, fine particles are the main cause of various environment problems, such as reduced visibility, emissions cloud processes and heterogeneous chemical reactions [5,6]. With increasing awareness of the negative health and environment impacts, there has been an increasing interest in monitoring of PM 2.5. Although commercial systems have been successful to some extent, they are bulky and expensive. Various methods to realize detection of PM have been proposed
∗ Corresponding author. E-mail address: [email protected] (J. Xie). 1 These authors contributed equally to this work and should be considered co-first authors. http://dx.doi.org/10.1016/j.sna.2015.12.029 0924-4247/© 2016 Elsevier B.V. All rights reserved.
with the aim to decrease volume and cost of the monitoring system. The concept based on thermophoretic deposition and film bulk acoustic resonator (FBAR) mass sensor has been demonstrated and validated with a sensitivity of 2 g/m3 with 10 min of testing time [7]. Also, a thermally actuated high-frequency resonator with piezoresistive readout was utilized as particulate mass sensor [8]. A novel microsensor for capacitive detection of PM directly in air was also presented [9]. Furthermore, the particles can be charged and concentration can be known by measuring charge quantity with the charge sensor [10–12]. In a PM monitoring system, it is essential to classify particles according to their size, so that they can be monitored accurately. There are a variety of methods used for particles classification, including inertial classification, gravitational sedimentation, centrifugation, and thermal precipitation [13]. The inertial classification through a virtual impactor (VI) is widely used in airborne particles sampling for its high performance and simple implementation[14,15]. Most of the reported virtual impactor are based on in-plane structure and assembled with other parts of the monitoring system. Recently, three-dimensional (3D) printing process has been widely used to fabricate micro channels for Microelectromechanical Systems (MEMS) [16] and microfluidic systems
380
J. Zhao et al. / Sensors and Actuators A 238 (2016) 379–388
Fig. 1. Schematic of the 3D printed virtual impactor integrated with QCM sensor for detecting airborne particles.
[17] due to its flexibility in geometrical designs. The 3D printing process can also be applied to the designs of virtual impactor to realize complicated 3D micro channels without assembly effort, but so far no relevant studies have been reported. In this work, we present a 3D printed virtual impactor to avoid assembly tolerance and maintain accurate alignment, which can minimize nozzle wall losses [18] and make the whole system compact. A quartz crystal microbalance (QCM) is utilized as a sensor to detect mass of the particles, which are separated according to their size by the virtual impactor. QCM is a high precision measuring tool with the characteristics of simple structure, low cost and real-time output [19,20]. The miniaturized PM monitoring system has advantages of low cost, low power consumption, ease of use and good portability. 2. Design and simulation The proposed design of PM monitoring system are shown in Fig. 1. The miniature system consists of a virtual impactor, a QCM sensor and a QCM holder that is used to ensure the QCM floating. There are three serpentine channels in the virtual impactor to separate particles into two groups: fine particle stream and coarse particle stream. The particles pass into the virtual impactor from the particles inlet and then are separated according to their size. The particles with diameter of 2.5 m and smaller get into the two major flow channels and then absorbed onto the surface of the QCM electrode, which will induce a shift of resonant frequency of the QCM. In this section, design and simulation of the virtual impactor will be given first, and then the QCM sensor with characterization method will be introduced. 2.1. Virtual impactor
Fig. 2. (a) Flow steam line of the virtual impactor (obtained using CFD simulation) with 90% major flow and 10% minor flow. (b) Schematic of the particles classification.
accelerated through a converging injection nozzle. Large particles follow a straight flow called minor flow because of their large inertial momentum. Small particles, on the other hand, migrate to the side channels known as the major flow due to their small inertial momentum, which is normal to the injection nozzle. Such design of virtual impactor allows particles to be classified according to their size. For effective classification of the particles, the flow needs to be precisely controlled, so that more than 90% of particles are diverted to the major flow and less than 10% of particles to the minor flow [15]. In this study, a virtual impactor with cut-off diameter of 2.5 m is designed. The cut-off diameter is the aerodynamic diameter of a particle with a collection efficiency of 50%. The collection efficiency depends on the Stokes number (Stk), Reynolds number (Re), flow rate (Q) and injection nozzle width (W). According to the conventional impaction theory, the dimensionless Stokes number is a critical factor for impaction and is given by [10] 2
A virtual impactor has been used to sample airborne particles on account of its collection efficiency and small internal particle loss. Fig. 2 shows the flow distribution and particle classification of a virtual impactor. In the virtual impactor, the influx flow is
Stk =
2
p dp QCc p dp UCc U = = 9W W/2 9hW2
(1)
where is the relaxation time (the time constant in the exponential decay of the particle velocity due to drag), U is the average
J. Zhao et al. / Sensors and Actuators A 238 (2016) 379–388 Table 1 Design parameters of the virtual impactor.
to the inlet. The collection efficiency of the virtual impactor can be calculated as
Parameters
Symbols
Values
Flow rate Width of the injection nozzle Thickness of the microchannel Stokes number Reynolds number Jet-to-plate distance Width of minor flow channel Length of minor flow channel Width of major flow channel Length of major flow channel
Q W h Stk50 Re S Wmi Lmi Wma Lma
0.27 L/min 1 mm 1 mm 0.58 984 2 mm 1 mm 112 mm 5 mm 18 mm
=
Nin−Nmi Nin
× 100
air velocity in the impactor nozzle, p is the particle density, dp is the particle diameter, Cc is Cunningham slip correction factor, is the dynamic viscosity of a particle (1.18 × 10−5 Pa × s) and h is the virtual impactor height. The slip correction factor is specified as
dp −1.10 2
(2)
where is the mean air molecule free path (0.066 m) and considered only when the particle diameter is smaller than 1.0 m, otherwise its value is assumed to be 1. The nozzle width of virtual impactor is one of the most important design parameters. According to Eq. (1), W can be expressed as
W=
2
p dp QCc 9Stk50 h
(3)
where Stk50 is the Stk at the collection efficiency of 50 percent. When the channel is rectangular, the suggested value of Stk50 is 0.58 [21]. The flow rate is set to be 0.27 L/min and the injection nozzle width W is 1 mm. The jet-to-plate distance S is calculated using the injection nozzle width W, because the minimum S to W ratio should be larger than 1.5 for the impactor. In order to achieve a sharp curve of collection efficiency, laminar flow in the flow channels is required, so that we optimize the nozzle width and flow rate to realize the Reynolds number in the range from 500 to 3000, by using Re =
4WhU (W + h)
where is the air density. Following the above design procedure, the Re number is calculated to be 984 for laminar regime. The design parameters of the virtual impactor are listed in Table 1. Computational fluid dynamic (CFD) simulation (ANSYS Fluent 15.0, ANSYS Inc.) was employed to analyze air flow and classification of particles in the virtual impactor. The inlet was modeled as velocity input, and the outlet was out flow. Pressure distribution in the microchannel is analyzed to design proper length and width of the microchannel to ensure the needed flow distribution. As shown in Fig. 3(a), pressure in the minor flow channel is higher than the one in the major flow channels due to narrow and long topology of minor flow channel. Fig. 3(b) shows the flow distribution which agrees with the expected ratio value. Discrete particles were then introduced into the flow. In the simulation, only classification area of the virtual impactor is analyzed to save solution time. The outlet pressures were set according to the previous simulation results. Particles ranging from 0.5 to 8 m in size were supplied to the inlet with a flow rate of 0.27 L/min. The simulation results (from Fig. 3(c) to (e)) show that particles with diameter smaller than the cut-off value tend to follow the major flow, while larger particles move straight down the minor flow. Collection efficiency is the ratio of the particle concentration collected in the major channels to particle concentration supplied
(5)
where Nin and Nmi are the number-concentration of the particles at the inlet and minor flow, respectively. The simulation result of collection efficiency are shown in Fig. 4. The cutoff diameter is determined by curve fitting using the Boltzmann sigmoidal function (Origin 8.0, OriginLab Inc.) as follows [22]
A1 − A2
E dp = A2 +
2 2 Cc = 1 + 1.257 + 0.40 exp dp dp
381
1+e
dp −d0
−
(6)
b
where E dp is the collection efficiency for a given particle aerodynamic diameter dp , d0 is the median aerodynamic diameter, b is the width of fitting, and A1 and A2 are the coefficients calculated by the regression. The designed cut-off diameter of the proposed virtual impactor is 2.5 m. According to curve fitting, the diameter is 2.65 m when the collection efficiency is 50%, with approximately 6% deviation from the designed value. From the simulation result, it is seen that the designed virtual impactor meets the requirement. 2.2. QCM sensor As shown in Fig. 5, a single QCM is composed of a thin quartz crystal plate with gold electrodes on each side. AT cut quartz crystal is widely used to be made of microbalances since the resonant frequency is temperature independent [23], which is important for the microbalances to be used as mass sensors. The application of a voltage between the electrodes results in a shear deformation within the quartz crystal due to the piezoelectric properties and crystalline orientation of the quartz [24]. The thickness shear vibration mode with maximum displacement at the crystal surface is normally applied for sensor application, as shown in Fig. 5. For higher resonant frequency, the QCM is much sensitive to surface perturbation. The Sauerbery equation gives the linear relationship between resonance frequency shift of QCM and mass attached on the electrode surface [25]: 2
2f0 f = − √ m = −m A q q
(7)
where f and m are the frequency shift and mass change, respectively, f0 is the fundamental frequency, A is the active area where the crystal is coated with electrodes on both sides, q is the shear modulus (29.47 GPa) and q is the density (2648 kg/m3 ) of the quartz crystal. The sensitivity of the QCM sensor is calculated to be = −2.88 × 1011 Hz/kg. The minus sign means that the resonant frequency decreases with the increase of mass loading. Finite element simulation using COMSOL was performed to model the resonant frequency of the QCM and thickness shear deformation. The eigen-frequency analysis was performed to yield the quartz surface displacement at thickness shear mode, as shown in Fig. 5. In the shearing vibration mode, the shear deformation is zero in the middle of the quartz thickness and is maximum but with opposite phases at the top and bottom surfaces of the quartz substrate. 2.3. Fabrication of virtual impactor A 3D computer model of the virtual impactor was created in SolidWorks, with which the device was printed directly from the computer-aided design (CAD) file by ProJet® 3510 series professional 3D printer (3D System Inc.). The material of the structure is
382
J. Zhao et al. / Sensors and Actuators A 238 (2016) 379–388
Fig. 3. CFD simulation result for the 3D printed virtual impactor. (a) pressure distribution. (b) flow stream line. (c) 0.5 m particle motion. (d) 2.5 m particle motion. (e) 8 m particle motion.
Fig. 4. Collection efficiency curve after CFD simulation and Boltzmann sigmoidal fitting. Percent error between the design and simulation cut-off diameter was approximately 6%.
Visijet M3 crystal which provides true plastic look and the support material is a white, melt-away wax. The heat distortion temperature at pressure of 0.45 MPa for the VisiJet M3Crystal is 56 ◦ C. The process starts with the 3D model and then the software slices the file and sends it to the printer. The printhead prints layers of UV curable liquid plastic onto a flat platform. Also, the wax support
material is jetted to fill the features of serpentine channels. The UV lamp flashes to solidify material and create a fully cured plastic part. Upon completion of the printing process, a high-pressure water jet exposed the final device design by dissolving the support material. The photograph of the printed virtual impactor are shown in Fig. 6.
J. Zhao et al. / Sensors and Actuators A 238 (2016) 379–388
383
Fig. 5. Schematic view of the QCM, with the thickness shearing vibration mode on the right by finite element simulation using COMSOL. The vibration mode shape includes two views, the 3D view and the side view.
Fig. 6. Photograph of the virtual impactor fabricated by 3D printed technology.
Chamfers were made on the corner of the channels to minimize particle wall losses.
and a mass flow controller (MFC, 5964 Series, Brook Instrument Inc.) was utilized to precisely control the flow rate. 3.2. Characterization of the system
3. Experiment setup 3.1. Characterization of the virtual impactor Fig. 7(a) shows the experimental setup for evaluating the particles classification characteristics of the virtual impactor. The setup consists of two sections, the air supply and the PM generator. In the PM generator chamber, silicon dioxide particles with diameter in the ranging from 0.5 to 8 m were generated in suspension using two fans. The generated particles were supplied to the virtual impactor with a flow rate of 0.27 L/min. In the virtual impactor, the particles were delivered to the major flow and minor flow channels. A polymethyl methacrylate (PMMA) slide was placed on the outlet of the virtual impactor to absorb particles. The required air flow was supplied by a vacuum pump (Rocker 300, Rocker Scientific. Inc.)
Fig. 7(b) shows the experimental setup for particle mass detection. The QCM was placed on one of the major flow outlet to replace the PMMA slide to absorb the particles. A thin photoresist film was coated on the electrode surface of the QCM to improve the surface adhesion. The resonant frequency of the QCM sensor was measured by a network analyzer (Agilent E5061B) for impedance analysis. The resonant frequency had a shift when silicon dioxide particles were loaded on the adhesive surface. The photograph of the experimental setup are shown in Fig. 8. 4. Results and discussion Particles distribution test has been preliminarily performed in order to experimentally evaluate the classification characterization of the virtual impactor. After pumping in the air with particles for
384
J. Zhao et al. / Sensors and Actuators A 238 (2016) 379–388
Fig. 7. Experimental setups (a) for examining the classification characteristics of the virtual impactor, and (b) for particle mass detection using the QCM sensor.
Fig. 8. Photograph of the experimental setup for prototype characterization.
5 min, the PMMA slide located at the outlet of the microchannels was coated with the particles. We observed the major flow and minor flow areas and measured the diameter of the particle using an optical microscope (Zeiss Axio). Fig. 9(a) shows the particles in the major flow area, most of which are smaller than 2.5 m in diameter. Also, several particles larger than 2.5 m in diameter are found since there is a small collection efficiency for large particles in the major flow area. In the area of the minor flow outlet, the particle number evidently decreases and the diameter of particles are mostly larger than 2.5 m, as shown in Fig. 9(b). Several particles with diameter smaller than 2.5 m also can be observed in the area of the minor flow outlet, which coincides with the theoretical and simulation analysis. The statistical result for the PM in the area of the major flow output through image processing are shown in Fig. 10, which indicates the proper design of the virtual impactor.
The frequency response of the QCM was measured at ambient pressure and room temperature, as shown in Fig. 11. The resonant frequency is about 4.985 MHz and the quality factor (Q) is more than 33,000. The phase shift at resonance is about 180 degrees. Besides fundamental resonant frequency, we can also find the second and third resonances, which are the 3rd and 5th overtones of the QCM resonator. Fig. 12(b) shows the measured frequency-impedance of the QCM at three stages: before coating film, after coating film and loading PM. The microscope images of the QCM surface for these three stages are shown in Fig. 12(a). Before the adhesive film was coated on the electrode of the QCM, the resonant frequency was about 4.985 MHz and the impedance at resonance was about 63 . When the electrode was coated with photoresist, the resonant frequency turned downward to 4.956 MHz and the impedance
J. Zhao et al. / Sensors and Actuators A 238 (2016) 379–388
385
Fig. 9. Optical micrograph of the PMMA slide after particles distribution. (a) The area of the major flow outlet, (b) the area of the minor flow outlet.
Fig. 10. Statistical result for the PM in the area of the major flow output through image processing.
increased to 74 . After PM loading, the resonant frequency of the QCM decreases and the impedance increases. The system for PM 2.5 detection was characterized. The concentration in the PM generator chamber was considered to be constant since only small amount of particles flowed through the virtual impactor. The measurement lasted 40 min and the resonant frequency was recorded in every 5 min. The experimental result is shown in Fig. 13, with data linearly fitted. The testing result indicates the mass loading on the QCM sensor increases linearly with time. The fitting sensitivity is about 11 Hz/min for the chamber, which implies that the QCM sensor dynamically responds to the mass concentration as particles are continuously injected into the diluting chamber. During 40 min of experiment, the frequency shift was about 443 Hz. According to the sensitivity of the QCM indicated in Eq. (7), the mass of particles deposited on the adhesive film was 1.537 g. The total volume of the air passing through the
virtual impactor can be calculated by multiplying the constant flow rate with the particle injection time. Hence the mass concentration in the air can be obtained by dividing the measured mass loading with the air volume. After calculation, the concentration of the test chamber was 142 g/m3 . The precision for measuring the PM 2.5 concentration depends on the minimum detectable mass of the QCM sensor, flow rate and testing duration. Due to the continuous mechanical vibrations of the piezoelectric crystal and other sources of noise such as the air flow, the resonant frequency of QCM sensor will fluctuate when working [26]. The short-term frequency fluctuation of the QCM ıf0 was found to be less than 1 Hz (0.2 ppm) over one hundred measurements. Therefore, the mass resolution of the QCM sensor is calculated to be ım = −1 ıf0 = 3.47ng [27]. However, in practice the minimum detectable mass is larger than this calculated value because of other sources of noise ignored during calculation.
386
J. Zhao et al. / Sensors and Actuators A 238 (2016) 379–388
Fig. 11. The measured transmission magnitude and phase of the QCM.
Fig. 12. (a) The microscope images of the QCM surface at three stages: before coating film, after coating film and loading particulate matter. (b) Measured resonant frequency shifts of the QCM sensor using impedance analysis at the three stages. The resonant frequency appears at the point of minimum impedance.
J. Zhao et al. / Sensors and Actuators A 238 (2016) 379–388
387
Fig. 13. The experimental and curve fitting results with PM, and the experimental result without PM for comparison showing the linear response of the miniature monitoring system in a PM generator chamber.
5. Conclusion Airborne particulate matter (PM) especially with diameter of 2.5 m and smaller presents a significant health risk and environment pollution, and there is a great need for a portable PM monitor that is capable of recording and tracking personal PM exposure. In this paper, we presented the design and fabrication of a miniature system for PM 2.5 concentration detection and experimentally evaluated the performance by exposing the sensor in a chamber full with fine silicon dioxide powder. The system is based on a virtual impactor to classify particles and a QCM sensor to detect the separated particles mass. The virtual impactor was fabricated by 3D printing process, which avoids assembly tolerance and maintains accurate alignment. After classification, the particles in the major flow area are mostly with diameter smaller than 2.5 m, which verifies the good performance of the virtual impactor. The miniaturized system was demonstrated to be a low-cost and real-time environmental monitoring tool, which paves the way to portable devices for pervasive mapping of air pollution. The system can be further improved by removing particles absorbed on the electrode surface of the QCM to implement repeated measurement [28,29]. Furthermore, by decreasing the minimum dimensions of the 3D printing process, thus decreasing the required flow rate, the air flow can be supplied by a more compact pump to generate particle flow motion [30]. The QCM sensor also can be integrated with a signal processing circuit. Therefore, it is possible to realize a small and portable PM monitoring according to such strategy.
Acknowledgements This work is supported by the “Zhejiang Provincial Natural Science Foundation of China (No. LY14E050018)”, the “National Natural Science Foundation of China (No. 51475423 and No.
51275465)”, and the “Zhejiang Open Foundation of the Most Important Subjects”.
References [1] P.A. Solomon, P. Gehr, D.H. Bennett, R.F. Phalen, L.B. Méndez, B. Rothen-Rutishauser, et al., Macroscopic to microscopic scales of particle dosimetry: from source to fate in the body, Air Qual. Atmos. Health 5 (2012) 169–187. [2] N. Künzli, R. Kaiser, S. Medina, M. Studnicka, O. Chanel, P. Filliger, et al., Public-health impact of outdoor and traffic-related air pollution: a European assessment, Lancet 356 (2000) 795–801. [3] C.A. Pope III, D.W. Dockery, Health effects of fine particulate air pollution: lines that connect, J. Air Waste Manage. Assoc. 56 (2006) 709–742. [4] M. Kampa, E. Castanas, Human health effects of air pollution, Environ. Pollut. 151 (2008) 362–367. [5] M.Z. Jacobson, Y.J. Kaufman, Y. Rudich, Examining feedbacks of aerosols to urban climate with a model that treats 3-D clouds with aerosol inclusions, J. Geophys. Res. Atmos. 1984–2012 (2007) 112. [6] K. Tsigaridis, M. Kanakidou, Secondary organic aerosol importance in the future atmosphere, Atmos. Environ. 41 (2007) 4682–4692. [7] I. Paprotny, F. Doering, P.A. Solomon, R.M. White, L.A. Gundel, Microfabricated air-microfluidic sensor for personal monitoring of airborne particulate matter: design, fabrication, and experimental results, Sens. Actuators A 201 (2013) 506–516. [8] A. Hajjam, J.C. Wilson, S. Pourkamali, Individual air-borne particle mass measurement using high-frequency micromechanical resonators, Sens. J. IEEE 11 (2011) 2883–2890. [9] M. Carminati, L. Pedalà, E. Bianchi, F. Nason, G. Dubini, L. Cortelezzi, et al., Capacitive detection of micrometric airborne particulate matter for solid-state personal air quality monitors, Sens. Actuators A 219 (2014) 80–87. [10] Y.-H. Kim, D. Park, J. Hwang, Y.-J. Kim, Integrated particle detection chip for environmental monitoring, Lab on a Chip 8 (2008) 1950–1956. [11] G. Jaramillo, C. Buffa, M. Li, F.J. Brechtel, G. Langfelder, D.A. Horsley, MEMS electrometer with femtoampere resolution for aerosol particulate measurements, Sens. J. IEEE 13 (2013) 2993–3000. [12] J. Zhao, H. Ding, J. Xie, Electrostatic charge sensor based on a micromachined resonator with dual micro-levers, Appl. Phys. Lett. 106 (2015) 233505. [13] P. Kulkarni, P.A. Baron, K. Willeke, Aerosol Measurement: Principles, Techniques and Applications, John Wiley & Sons, 2011. [14] D. Liang, W.-P. Shih, C.-S. Chen, C.-A. Dai, A miniature system for separating aerosol particles and measuring mass concentrations, Sensors 10 (2010) 3641–3654.
388
J. Zhao et al. / Sensors and Actuators A 238 (2016) 379–388
[15] Y.-H. Kim, J.-Y. Maeng, D. Park, I.-H. Jung, J. Hwang, Y.-J. Kim, Micromachined cascade virtual impactor with a flow rate distributor for wide range airborne particle classification, Appl. Phys. Lett. 91 (2007) 043512. [16] S.-Y. Wu, C. Yang, W. Hsu, L. Lin, 3D-printed microelectronics for integrated circuitry and passive wireless sensors, Microsyst. Nanoeng. 1 (2015). [17] J.L. Erkal, A. Selimovic, B.C. Gross, S.Y. Lockwood, E.L. Walton, S. McNamara, et al., 3D printed microfluidic devices with integrated versatile and reusable electrodes, Lab on a Chip 14 (2014) 2023–2032. [18] B.W. Loo, C.P. Cork, Development of high efficiency virtual impactors, Aerosol. Sci. Technol. 9 (1988) 167–176. [19] J. Lee, J. Jang, D. Akin, C.A. Savran, R. Bashir, Real-time detection of airborne viruses on a mass-sensitive device, Appl. Phys. Lett. 93 (2008) 013901. [20] T. Lederer, B.P. Stehrer, S. Bauer, B. Jakoby, W. Hilber, Utilizing a high fundamental frequency quartz crystal resonator as a biosensor in a digital microfluidic platform, Sens. Actuators A 172 (2011) 161–168. [21] J.S. Haglund, A.R. McFarland, A circumferential slot virtual impactor, Aerosol. Sci. Technol. 38 (2004) 664–674. [22] P. Demokritou, S.J. Lee, P. Koutrakis, Development and evaluation of a high loading PM2. 5 speciation sampler, Aerosol. Sci. Technol. 38 (2004) 111–119. [23] J. Zhu, S. Huang, J. Ye, X. Zhang, G. Liu, Design of a quartz crystal with transparent electrode used for both QCM–D and LSPR technology, Sens. Actuators A 229 (2015) 141–146. [24] D. Ballantine Jr, R.M. White, S.J. Martin, A.J. Ricco, E. Zellers, G. Frye, et al., Acoustic Wave Sensors: Theory, Design, & Physico-Chemical Applications, Academic press, 1996. [25] G. Sauerbrey, Verwendung von Schwingquarzen zur Wägung dünner Schichten und zur Mikrowägung, Z. Angew. Phys. 155 (1959) 206–222. [26] L. Rodriguez-Pardo, J.F. Rodriguez, C. Gabrielli, H. Perrot, R. Brendel, Sensitivity, noise, and resolution in QCM sensors in liquid media, Sens. J. IEEE 5 (2005) 1251–1257. [27] K. Ekinci, X. Huang, M. Roukes, Ultrasensitive nanoelectromechanical mass detection, Appl. Phys. Lett. 84 (2004) 4469–4471. [28] Q. Qi, G.J. Brereton, Mechanisms of removal of micron-sized particles by high-frequency ultrasonic waves, ultrasonics, ferroelectrics, and frequency control, IEEE Trans. 42 (1995) 619–629. [29] H.S. Wasisto, S. Merzsch, A. Waag, E. Uhde, T. Salthammer, E. Peiner, Evaluation of photoresist-based nanoparticle removal method for recycling silicon cantilever mass sensors, Sens. Actuators A 202 (2013) 90–99. [30] I. Paprotny, F. Doering, R.M. White. MEMS particulate matter (PM) monitor for cellular deployment particulate matter (PM) monitor for cellular deployment, IEEE Sensorsconference, pp 2010 2435–2440.
Biographies Jiuxuan Zhao received the B.Eng. degree from LuDong University, Yantai, China in 2013. Now he is pursuing master degree in Zhejiang University, Hangzhou, China. His research interest is microelectromechanical systems (MEMS) sensors and actuators.
Minliang Liu received the B.Eng. degree from Zhejiang University, Hangzhou, China, in 2015. Now he is pursuing PhD degree in Georgia Institute of Technology, GA, USA. His research interest includes design of micro sensors & actuators and cardiovascular biomechanics. Liang Liang received the B.Eng. degree from Tsinghua Univeristy, Beijing, China in 2002, and the Ph.D. degree from Yale University, CT, USA, in 2013. He is currently working as a post-doc researcher at Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, GA, USA. His research interests are in the areas of imaging informatics – biomedical image analysis, intelligent algorithms for sensors and robotics. Wen Wang received the B.Eng. degree from Hangzhou Dianzi University, Zhejiang, China, in 1990, the M.Eng. degree and the Ph.D. degree from Zhejiang University, Hangzhou, China, in 1992 and 1996, respectively. From 1996 to 2012, he worked in Zhejiang University. From 2009 to 2010, he worked in the Center for Precision Metrology of the University of North Carolina at Charlotte, NC, USA as a visiting researcher. In July 2012, he joined the School of Mechanical Engineering, Hangzhou Dianzi University, Zhejiang, China, as a professor. His research interests include Micro/Nano measuring and control technology, coordinate measuring machines, precision engineering and mechatronics. Jin Xie received the B. Eng. degree from Tsinghua University, Beijing, China, in 2000, the M.Eng. degree from Zhejiang University, Hangzhou, China, in 2003, and the Ph.D. degree from Nanyang Technological University, Singapore, in 2008. From 2007 to 2011, he worked in Institute of Microelectronics, Singapore. In June 2011, he joined the Department of Mechanical Engineering, University of California, Berkeley, CA, USA, as a post-doc researcher. In October 2012, he joined the Department of Mechanical Engineering, Zhejiang University, Hangzhou, China, as a professor. His research interests include microelectromechanical systems (MEMS) design and processes, energy harvesters, inertial sensors, acoustics and vibration measurement.