An aerosol sensor for PM1 concentration detection based on 3D printed virtual impactor and SAW sensor

Sensors and Actuators A 288 (2019) 67–74

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Sensors and Actuators A: Physical journal homepage: www.elsevier.com/locate/sna

An aerosol sensor for PM1 concentration detection based on 3D printed virtual impactor and SAW sensor Yong Wang, Yinshen Wang, Weixin Liu, Dongyang Chen, Changju Wu, Jin Xie ∗ The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, People’s Republic of China

a r t i c l e

i n f o

Article history: Received 28 September 2018 Received in revised form 25 November 2018 Accepted 11 January 2019 Available online 24 January 2019 Keywords: Aerosol sensor PM1 monitoring Virtual impactor SAW sensor

a b s t r a c t This paper reports an aerosol sensor for PM1 monitoring based on a virtual impactor (VI) and a surface acoustic wave (SAW) sensor. The VI is fabricated using three-dimensional (3D) printing techniques for PM1 classification and a SAW sensor is put at the VI major flow outlet to detect PM1 particles mass. The pressure and flow distribution of the VI and their effects on particles classification have been simulated to optimize the VI structure. In order to enhance surface adhesion for capturing particles, a layer of glycol film is coated on the sensor surface by the atomization method. The aerosol sensor performance is verified through separation and detection of commercial silicon dioxide particles with diameter in the range of 0.1–4 ␮m. The experimental results show that after classification by the virtual impactor, most of particles in the major flow are PM1 particles and the resonant frequency of the SAW sensor presents a linear decrease trend with the increment of PM1 mass. Moreover, the aerosol sensor has good repeatability and measurement accuracy, and shows high sensitivity of 7.446 Hz/min per ␮g/m3 . © 2019 Elsevier B.V. All rights reserved.

1. Introduction Particulate matter (PM) is an important pollutant in the atmosphere that is composed of a mixture of solid and liquid particles suspended in the air [1,2]. Although there have been numerous methods for PM definition, one of the main criteria to measure its harm to public health is the aerodynamic diameter [3]. Up to now, PM particles that have the most influence on human health are those particles with aerodynamic diameter smaller than 10 ␮m (PM10 ) [4]. PM10 particles are inhalable particles, which are able to penetrate within the respiratory tract [5,6]. Besides, according to the size of PM, PM10 particles can also be subdivided into “coarse particles” (PM2.5 -PM10 , diameter 2.5–10 ␮m), “fine particles” (PM2.5 , diameter ≤ 2.5 ␮m) and “submicron particles” (PM1 , diameter ≤ 1 ␮m) [7–9]. The PM sizes have been directly determined their adverse effects on human health [10–12]. Particle pollution, especially submicron particles (PM1 ) can pose a greater health risk than coarse and fine particles because they can penetrate deeper into the lungs and blood streams and on average they can contain higher levels of harmful substances [13–16]. Despite the larger potential of PM1 to adversely influence on human health, many studies are still focusing on the monitoring of coarse and

∗ Corresponding author. E-mail address: [email protected] (J. Xie). https://doi.org/10.1016/j.sna.2019.01.013 0924-4247/© 2019 Elsevier B.V. All rights reserved.

fine PM fractions (i.e. PM10 and PM2.5 ) [17–20]. Therefore, it is very essential to develop a low-cost and portable aerosol sensor for PM1 monitoring. It is particularly important to separate the PM size for a PM detecting system. Recently, various methods have been used for PM classification such as thermal precipitation, gravitational sedimentation, inertial classification and centrifugation [21]. Among these techniques, the VI, one kind of inertial classifiers, has been widely utilized for PM classification because of its good collection efficiency and performance [22–24]. However, most of flow channels of the reported VI need to assemble, which may bring the assembly error and then influences the particle collection efficiency [18,25]. Recently, 3D printing techniques have gained great concern because it can be utilized to make complex flow channel structure with nanometer scale resolution [26,27]. In addition, 3D printing techniques have advantages of ease of learning and fast building time. Hence, we can use 3D printing techniques to make the virtual impactor with complex flow channel structure. However, few studies have been reported so far [28,29]. In this paper, we report a 3D printed VI assembled with a SAW sensor for PM1 concentration detection. Computational fluid dynamics (CFD) simulation has been used to optimize the VI structure to realize flow distribution for classifying particles without applying a pump at the minor flow outlet [30,31]. And a SAW sensor is used for detecting PM1 mass on account of its low cost, high sensitivity and fast response time [32,33]. The 3D printing process

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Fig. 1. Schematic of the aerosol sensor integrated by assembling the SAW sensor into the VI.

Fig. 3. The flow distribution in major flow as a function of minor flow outlet pressure obtained by CFD simulation.

Fig. 2. The schematic of (a) flow line distribution and (b) PM classification in the VI.

improves the VI dimensional precision and avoids the assembly error, which then leads to a good measurement accuracy of the senor. Besides, the aerosol sensor has good reliability and repeatability, and shows high sensitivity of 7.446 Hz/min per ␮g/m3 . The proposed aerosol sensor has advantages of low cost, low power consumption and compact size. 2. System design

2.1. Virtual impactor design The flow line distribution of the VI is shown in Fig. 2(a), which is simulated using COMSOL 5.3. The air stream enters into the virtual impactor from the injection nozzle and then is divided into two groups: major flow and minor flow. When particles are introduced into the air flow and pass through the VI, they are classified according to their size. As shown in Fig. 2(b), large particles with diameter larger than the VI cutoff diameter are inclined to move in a straight line and enter into the minor flow on account of large inertia. But for small particles smaller than the VI cutoff diameter, they tend to flow along the flow line and get into the major flow. In this study, a VI with cutoff diameter of 1 ␮m is designed. The cutoff diameter of the VI is dependent on the flow rate Q, Stokes number Stk, injection nozzle width W and Reynolds number Re [34]. Among these parameters, the dimensionless Stk is a critical parameter in the design of VI, which is given by [35] p dp2 QCC p dp2 UCC U = = 9W W/2 9W 2 H



W=

(1)

p dp2 QCC 9HStk50

(2)

Besides, the laminar flow in the flow channel is demanded. Thus, the Reynolds number of the injection nozzle should be in the range of 500–3000, which can be calculated by Re =

Fig. 1 shows the schematic of the SAW based aerosol sensor, which is composed of three parts: an upper VI, a lower VI and a SAW sensor. The VI is utilized to classify particles and the SAW sensor is used for the detection of particles mass. And the aerosol sensor is integrated by assembling the SAW sensor into the VI.

Stk =

where  is the relaxation time, CC is the Cunningham slip correction factor, U is the average air velocity, p is the particle density, dp is the particle diameter,  is the air dynamic viscosity and H is the injection nozzle height. The Stokes number depends on flow channel geometry parameters and Stk50 is the Stk value at the collection efficiency of 50%. For the VI with square nozzle, the recommended Stk50 value is 0.229 [36,37]. And the CC value is calculated to be 1.166 [38]. Then the width of the injection nozzle can be determined by

2Q LC U =  (H + W )

(3)

where  and LC are the air density and the hydraulic diameter of the injection nozzle, respectively. Furthermore, because the virtual impactor is the bottleneck on the path of air flow in term of the air pressure drop, the minimization of the pressure drop also needs to be considered in the design of VI. There have been some studies showing that square nozzle suffers least pressure loss [18]. So the cross section of the injection nozzle is selected as a square. Finally, the injection nozzle width is set to be 0.8 mm and the inlet flow rate of the VI is calculated to be 0.44 L/min. The minor flow channel width D is 1.0 mm and the jet-to-plate distance S is 1.2 mm. And the calculated Reynolds number of the flow channel is 627.3, which meets the laminar flow requirement. 2.2. Virtual impactor simulation and optimization For a VI, the flow distribution is very important because it determines the particle collection efficiency. There have been some studies showing that when 90% of the inlet flow gets into the major flow, the VI presents a good collection efficiency [23]. However, for a simple VI with cross structure, the flow distribution cannot be satisfied. Therefore, the minor flow outlet pressure needs to be controlled to satisfy the flow distribution for classifying particles. Fig. 3 shows two-dimensional (2D) simulation of flow distribution in the major flow as a function of minor flow outlet pressure. The simulation results show that as the minor flow outlet pressure increases,

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Fig. 4. 2D simulation of (a) pressure distribution and (b) flow velocity distribution of the VI after applying an 81.86 Pa pressure to the minor flow outlet.

Fig. 5. Particle traces simulation in the VI for (a) 0.2 ␮m particles, (b) 1 ␮m particles, (c) 1.8 ␮m particles and (d) 2.5 ␮m particles.

more air flow enters into the major flow. When the outlet pressure is 81.86 Pa, about 90% of the inlet flow enters into the major flow, which meets flow distribution of particles classification. Fig. 4(a) shows pressure distribution of the VI when an 81.86 Pa pressure is applied to the minor flow outlet. The simulation indicates that the pressure in minor flow is higher than that in major flow and the straight flow channel has smaller friction pressure loss. Fig. 4(b) shows the flow velocity distribution of the VI with an 81.86 Pa minor flow outlet pressure. From the simulation, we can see that the flow velocity in the major flow is symmetrically distributed and is distinctly higher than that in the minor flow, which is in good agreement with the expected value. Then, CFD simulation was used to analyze the particle traces in the VI, as shown in Fig. 5. The minor flow outlet pressure was set to be 81.86 Pa according to the previous simulation results. As the PM shapes in real atmosphere are irregular, for the simulation convenience, all PM particles were assumed to be spherical and they were injected into the VI at an inlet flow velocity of 11.46 m/s. The simulations show that ultrafine particles (PM0.2 ) tend to flow along the flow line and get into the major flow. But for larger inertia par-

ticles (PM1.8 and PM2.5 ), they are inclined to move in a straight line and flow into the minor flow. The PM1 particles flow into both the minor and major flow at a collection efficiency of about 50%. Therefore, when the air flow distribution ratio between major flow and minor flow is 9:1, the VI presents a good classification performance. However, in practical test, it is very complex and inconvenient to apply an outlet pressure to the minor flow for ensuring the needed flow distribution. But, a proper pressure in minor flow can be generated by increasing the flow channel length or designing a sinuous flow channel structure. In consideration of the VI size, we designed a sinuous minor flow channel to adjust the pressure and flow distribution of the VI for meeting the particle classification requirements. Besides, we also used CFD simulation to optimize the VI structure with smaller internal particle loss and vortexes based on our previous work [29], so the service life of the VI has been greatly extended. The final VI structure was obtained through numerous simulations, as shown in Fig. 6. Fig. 6(a) shows CFD simulation of pressure distribution in the optimized VI structure. The simulation result reveals that the inlet pressure of the minor flow is higher than that of major flow because

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Fig. 6. CFD simulation of (a) pressure distribution and (b) flow velocity distribution of the VI with optimized structure.

Table 1 Flow rate distribution of the VI obtained by CFD simulation. Volumetric flow rate (m3 /s)

Value

Total flow inlet Major flow outlet 1 Major flow outlet 2 Minor flow outlet

7.3333333E-6 3.2633205E-6 3.2632897E-6 8.0672485E-7

of its narrow and sinuous flow channel structure. Fig. 6(b) shows the flow velocity distribution of the VI simulated by COMSOL 5.3. It is obvious that the flow velocity in the major flow is higher than that in the minor flow, which means that more air flow enters into the major flow. Moreover, the flow rate distribution in the major and minor flow was also simulated, as listed in Table 1. The simulations show that about 90% of the inlet flow gets into the major flow, which meets flow distribution for classifying particles. We also simulated the particle collection efficiency in the major flow as a function of particle diameter, as shown in Fig. 7. The particle collection efficiency is defined as the ratio of the particle concentration in the major flow to particle concentration introduced to the VI inlet [28]. When the particle collection efficiency is 50%, the fitted cutoff diameter is 1.05 ␮m, with a smaller deviation from the designed value, indicating that the virtual impactor has a good collection efficiency.

Fig. 8. Optical images of the VI fabricated using 3D printing techniques.

2.3. Virtual impactor fabrication 3D printing techniques was used to fabricate the virtual impactor. Initially, we used SolidWorks software to create a VI 3D model, then the model was printed by the professional 3D printer (ProJet MJP 3600, 3D System Inc., USA). The fully automated printing process simultaneously jetted a model material and a support material in 16-␮m layers. Once the printing process was completed, a high-pressure water jet was used to dissolve the support material [39]. Finally, the VI became a replica mold and the model material of the VI is photosensitive resin. Fig. 8 shows the 3D printed VI, which has high dimensional precision and an interior surface roughness of 1.47 ␮m. 2.4. PM1 mass detection

Fig. 7. Particle collection efficiency in the major flow as a function of particle diameter simulated by COMSOL 5.3.

An aluminum nitride (AlN) based SAW sensor with a resonant frequency of 147.24 MHz was utilized to detect the PM1 mass due to its high sensitivity. The details of the design parameters and fabrication process of the SAW device have been shown in our previous work [40]. When particles deposite on the sensor sensing surface, the resonant frequency of the SAW sensor will decrease due to mass loading effect. In general, Sauerbrey equation can be used to characterize the frequency change of the sensor caused by mass loading

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Fig. 9. Frequency shift of the SAW sensor after evaporation of different mass concentration NaCl solutions (0.1 ␮L).

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Fig. 11. Optical micrographs of the PM size distribution collected by putting a silicon wafer at one of the major flow outlets.

Fig. 12. The changes of SAW response before and after coating the adhesive film.

Fig. 10. (a) Schematic and (b) photograph of the experimental setup for validating the aerosol sensor performance.

[41]. But the formula is only applicable for bulk piezoelectric substrate such as quartz and LiNbO3 . In this work, the Rayleigh wave was excited on the AlN/Si substrate which is a complex substrate. Thus, we used NaCl solutions to calibrate the sensor mass sensitivity. Fig. 9 shows frequency shift of the SAW sensor as a function of mass concentration of NaCl solutions after their evaporation. The measured sensor sensitivity is 33.82 kHz/␮g. 3. Experimental The experimental setup for validating the aerosol sensor performance is shown in Fig. 10. Firstly, we used fans to make the dried silicon dioxide particles generate in suspension. Then the suspended particles were introduced into the virtual impactor by a micro-pump which is adjusted by a pulse-width modulation (PWM) generator. As the flow rate has a significant effect on parti-

cle collection efficiency, the inlet flow rate of the VI was controlled by an electronic flowmeter. And a SAW sensor was put at one of the major flow outlets to detect the PM1 mass. In addition, the sensor surface was coated a layer of glycerol film to enhance surface adhesion for capturing particles. The frequency shift of the SAW device cause by PM deposition was measured using a network analyzer (Agilent E5061B). Furthermore, the test was conducted in a clean room at a constant temperature of 25 ◦ to decrease the temperature effect on measurement accuracy. 4. Results and discussion The size distribution of PM in the major flow has been firstly characterized to verify the classification performance of the VI, as shown in Fig. 11. The suspended silicon dioxide particles with diameter in the range of 0.1–4 ␮m were bubbled into the virtual impactor for 5 min and the PM in one of the major flow were collected by putting a silicon wafer at its outlet. The measurement results reveal that most of the particles in the major flow are PM1 particles, indicating that the virtual impactor has a good classification performance. As the sensor surface is smooth, it is hard to capture particles through the physical deposition method. Therefore, a layer of glycerol film was coated on the sensor surface to enhance surface adhesion. Fig. 12 shows SAW responses before and after coating the

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Fig. 13. Resonant frequency changes of the SAW device with and without adhesive film in response to test time at the same PM concentrations.

Fig. 14. PM1 mass on the sensor surface with and without adhesive film in response to test time at the same PM concentrations.

adhesive film. When the sensor surface is coated with a layer of the adhesive film, the resonant frequency of the SAW device decreases, but the resonant amplitude is not obviously attenuated, which is favorable for PM1 detection. Then the detection performance of the aerosol sensor was evaluated. During the experiment, the resonant frequency of the SAW device was recorded every 10 min and the test lasted 50 min every time. Fig. 13 shows SAW frequency changes with and without adhesive film versus test time at the same PM concentrations. It has been shown that the frequency of the SAW device decreases linearly as the test lasts. Moreover, the linear fitting sensitivity of the SAW sensor with adhesive film (151.5 Hz/min) is larger than that without adhesive film (74.2 Hz/min). The PM1 mass deposited on the sensor surface can be calculated based on the measured mass sensitivity of the SAW sensor (33.82 kHz/␮g). Fig. 14 shows the PM1 mass on the sensor surface with and without adhesive film versus test time. From which, we can see that the PM1 mass on the sensor surface linearly increases with the test time. And the PM1 mass concentration can be calculated using the slope of the PM1 mass relative to test time divided by the VI inlet flow rate. When the particles concentration in the PM generating chamber are the same, the measured PM1 concentrations with and without adhesive film are respectively 20.45 ␮g/m3

Fig. 15. Resonant frequency changes of the SAW sensor versus test time under different PM concentrations.

Fig. 16. Resonant frequency changes of the SAW sensor versus test time at the same PM concentrations.

and 9.95 ␮g/m3 , which implys that the glycerol adhesive film can significantly improve the sensor surface adhesion for particles capture. The measurement accuracy of the aerosol sensor has also been characterized, as shown in Fig. 15. The measurement results reveal that the resonant frequency of the SAW device dynamically responds to PM1 mass concentration. The measured initial PM1 concentration is 20.45 ␮g/m3 . If the particles amount in the PM generating chamber is raised to 2 and 3 times of the initial level, the measured corresponding PM1 concentrations are 43.12 ␮g/m3 and 59.36 ␮g/m3 , which are respectively about 5.43% and 3.24% deviation from the theoretical values. Therefore, the aerosol sensor has excellent measurement accuracy. Fig. 16 shows resonant frequency variations of the SAW sensor versus test time at the same PM concentrations. The PM concentrations keep constant by introducing the same amount of particles into the PM generating chamber. It has been shown that the frequency of the SAW sensor decreases linearly as the test continues. The measured PM1 concentration in the 1 st experiment is 21.15 ␮g/m3 . The measured PM1 concentrations in the 2nd and 3rd experiment are 20.82 ␮g/m3 and 19.86 ␮g/m3 , respectively. If we use the first measured PM1 concentration as a reference, the measured PM1 concentrations in the 2nd and 3rd experiment are

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Acknowledgements This work is supported by the Zhejiang Provincial Natural Science Foundation of China (LZ19E050002) and the National Natural Science Foundation of China (51875521 and 51575487).

References

Fig. 17. Frequency shift changes per minute of the SAW device as a function of PM1 mass concentration.

respectively about 1.6% and 6.1% deviation from the reference, indicating a good repeatability of the sensor. We also investigated the detection performance of the aerosol sensor under different PM concentrations to validate the sensor reliability and adaptability, as shown in Fig. 17. The results show that the frequency shift per minute of the SAW sensor presents a good linearity with the PM1 concentration. Thus, we can determine the PM1 mass concentration according to the measured frequency shift per minute of the SAW sensor. The fitted linear sensitivity of the aerosol sensor is 7.446 Hz/min per ␮g/m3 , which is about 133.4 times higher than that of the QCM in our previous work [29]. When more particles accumulate on the sensor surface, the SAW sensor sensitivity will be worse, which then influences the measurement accuracy. Therefore, the sensor surface needs to be cleaned after several tests. The particles are removed from the sensor surface by an ultrasonic cleaning method. After the cleaning, the resonant frequency of the SAW sensor presents a slight drop, but the resonant amplitude keeps almost constant. Therefore, the aerosol sensor can realize the repeated measurements.

5. Conclusions and further work An aerosol sensor for PM1 classification and detection has been proposed and demonstrated. The system is based on a VI for separating PM and a SAW sensor for detecting the PM1 mass. CFD simulation is used to optimize VI structure and when the air flow distribution ratio between the major flow and minor flow is 9:1, the VI presents good collection efficiency. 3D printing techniques is utilized to fabricate the VI because they can improve the flow channel dimensional precision and avoid the assembly tolerance. After classification, in the major flow, most of the particles are smaller than 1 ␮m, indicating a good classification performance of the VI. The good performances of the sensor in measurement accuracy and repeatability have also been demonstrated through measuring the sensor responses at the same and different PM concentrations. In addition, the sensor shows high sensitivity of 7.446 Hz/min per ␮g/m3 , which is about 133.4 times higher than that of the QCM in our previous work [29]. Therefore, the proposed aerosol sensor has great potential for application in PM1 monitoring. Further work will be focused on the integration of the SAW sensor with standard CMOS circuitry to obtain a low-cost, low-power, smart and portable aerosol sensor for the real-time monitoring of PM1 concentration.

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Yinshen Wang received the B.Eng. degree from Zhejiang Sci-tech University, Hangzhou, China in 2014, and the M.Eng. degree from Zhejiang University, Hangzhou, China in 2018. His research mainly focuses on the microelectromechanical system (MEMS) sensor and actuators. Weixin Liu is currently pursuing the B.Eng. degree in School of Mechanical Engineering, Zhejiang University, Hangzhou, China. His research interests include PM classification and detection. Dongyang Chen received the B.Eng. degree from Xiamen University, Xiamen, China, in 2015. Now he is pursuing the pH.D. degree in the School of Mechanical Engineering, Zhejiang University, Hangzhou, China. His research interests include microelectromechanical systems (MEMS) based resonators and resonant sensors.

Changju Wu received the M. Eng. degree and pH.D. degree from Zhejiang University, Hangzhou, China, in 2003 and 2006, respectively. In October 2006, he joined the Royal Institute of Technology (KTH, Sweden) as a post-doc researcher. In October 2007, he joined the College of Inforomation Scienece and Engineering Zhejiang University, China. Since December 2010, he has been an associate professor. His research interests include MEMS design and processes, self-adaption cooling and active flow control.

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

Biographies measurement.

Yong Wang received the B.Eng. degree from Southwest jiaotong University, Chengdu, China, in 2015. He is currently pursuing the pH.D. degree in School of Mechanical Engineering, Zhejiang University, Hangzhou, China. His research interests include microelectromechanical systems (MEMS) design and process, Bio-MEMS, surface acoustic wave (SAW) based microfluidics.