We present a portable, inexpensive, and accurate
microelectromechanical-system-based (MEMS-based) condensation particle counter
(CPC) for sensitive and precise monitoring of airborne ultrafine particles
(UFPs) at a point of interest. A MEMS-based CPC consists of two main parts:
a MEMS-based condensation chip that grows UFPs to micro-sized droplets and a
miniature optical particle counter (OPC) that counts single grown droplets
with the light scattering method. A conventional conductive cooling-type
CPC is miniaturized through MEMS technology and three-dimensional (3-D)
printing techniques; the essential elements for growing droplets are
integrated on a single glass slide. Our system is much more compact (75 mm × 130 mm × 50 mm), lightweight (205 g),
and power-efficient (2.7 W) than commercial CPCs. In quantitative
experiments, the results indicated that our system could detect UFPs with a
diameter of 12.9 nm by growing them to micro-sized (3.1 µm) droplets.
Our system measured the UFP number concentration with high accuracy (mean
difference within 4.1 %), and the number concentration range for which
our system can count single particles is 7.99–6850 cm-3. Thus,
our system has the potential to be used for UFP monitoring in various
environments (e.g., as an air filtration system, in high-precision industries
utilizing clean rooms, and in indoor and outdoor atmospheres).
Introduction
Monitoring of airborne ultrafine particles (UFPs, which are smaller than 100 nm) is needed in various fields for human health and yield enhancement in
industrial fields (Donaldson et al., 1998; Donovan et al., 1985;
Hristozov and Malsch, 2009; Li et al., 2016; Liu et al., 2015). While UFPs
have a variety of anthropogenic and natural sources (e.g., soot agglomerates
and secondary particles from hazardous gaseous precursors), in urban areas
they are largely generated from vehicle exhaust (Kim et al., 2011;
Kittelson, 1998; Shi et al., 1999). Because of dramatic developments in
nanotechnology, engineered UFPs for commercial and research purposes have
been produced on a large scale. These incidentally and intentionally
generated UFPs are more harmful to human health than their larger
counterparts. These UFPs have a higher chance of being deposited in the
lower respiratory system and are more toxic owing to their larger
surface-to-volume ratios, which causes oxidative stress, pulmonary
inflammation, and tumor development (Hesterberg et al., 2012; Hext, 1994;
Li et al., 2003; Renwick et al., 2004). Thus, on-site monitoring is needed to
assess and minimize UFP exposure. High-precision industries with clean rooms
also require UFP monitoring to trace their sources, minimize the
contamination, and thereby increase the production yield. For instance, in
the semiconductor industry, where the minimum feature size of the
semiconductor devices approaches 7 nm, particles with diameters of a few
nanometers are critical (Neisser and Wurm, 2015). Although an
air-purification system equipped with an ultra-low particulate filter
eliminates the contaminants in the air entering the clean room, it cannot
control the UFPs internally generated during the manufacturing processes
(e.g., chemical vapor deposition (CVD), metallization, and wet etching)
(Choi et al., 2015; Manodori and Benedetti, 2009). If they are deposited
on the electrodes of a chip, they cause interruption of the current flow, making
the whole chip unusable and thereby reducing the yield of the semiconductor
production (Libman et al., 2015). In this regard, ISO 14644-12 has been
recently developed to provide guidance on how to monitor UFPs in clean rooms. To monitor
the concentration field of UFPs in these environments where the spatial and
temporal variations of UFP concentrations are enormous, portable and
low-cost sensors are required to establish simultaneous monitoring at
multiple points or dense monitoring networks.
Condensation particle counters (CPCs) are the most widely used UFP detection
instruments and are based on the heterogeneous particle condensation
technique (Stolzenburg and McMurry, 1991). They grow UFPs to micro-sized
droplets through condensation and count them by optical means. Compared to
the electrical method (measuring the number concentration of UFPs by
electrically charging them and sensing their current), CPCs provide
extremely sensitive and precise counting because they are capable of
counting individual particles (Kangasluoma et al., 2014, 2017; McMurry, 2000). Moreover, if a differential mobility analyzer
(DMA) is used as a particle size selector, CPCs can offer higher particle size
resolution than any other particle sizing instruments (Sioutas, 1999;
Stolzenburg et al., 2017). However, commercially available CPCs are bulky
and expensive; thus, they are impractical for on-site monitoring where the
UFP concentration changes continuously. Although portable CPCs (e.g., model
3007, TSI Inc., USA) are currently on the market, they are still large in
size (292 mm × 140 mm × 140 mm) and expensive
(∼ USD 10 000). Therefore, despite their advantages, CPCs are
difficult to utilize actively for on-site monitoring applications.
In the past few years, several studies have used microelectromechanical-system (MEMS) technology for UFP analysis (Hajjam et al., 2010; Kang et
al., 2012; Kim et al., 2015, 2018; Wasisto et al., 2013; Zhang
et al., 2016). Because this technology is capable of batch and micro-size
fabrications through the semiconductor manufacturing process, such chips
provide cost efficiency, compactness, and enhanced portability. Most
MEMS-based sensors developed for monitoring UFPs were based on the
electrical technique. However, these sensors exhibited low sensitivity
because their small size means that they have to operate at a small
volumetric flow rate (below 1 L min-1), and UFPs carry only a small number of
elementary charges of their limited surface area.
In this study, we developed a high-performance MEMS-based CPC that is
portable, inexpensive, and power-efficient. Our system comprises a
MEMS-based condensation chip and miniature optical particle counter (OPC).
UFPs are grown to micrometer-sized droplets on the chip, and the grown
droplets are detected by the miniature OPC. New fabrication techniques
including MEMS and 3-D printing technologies are applied to CPCs; particle
enlargement (i.e., the fundamental process of the CPC) can be realized on a
chip-scale system because the essential elements for growing droplets
(i.e., the channels, micropillar-type wick, heaters, and temperature sensors)
are integrated on a single glass slide. Accordingly, our system is far more
compact and cost-efficient than traditional CPCs, even when considering the
portable versions.
In addition to its compactness, our system also provides high degrees of
accuracy and precision. A quantitative characterization using Ag particles
proves that our system is capable of growing UFPs to micrometer-sized
droplets, counting them one by one, and thereby measuring UFP number
concentration with a high accuracy, which is comparable to a commercial OPC.
These results show that our system can potentially be used as a portable,
low-cost, and high-precision UFP sensor for various fields (e.g., assessing
UFP exposure, monitoring workplaces, and tracing particle sources in
high-precision industries with clean rooms). Moreover, when combined with the
recently developed miniature DMA, our system should also be able to perform
high-resolution on-site monitoring of the UFP size distribution
(Liu and Chen, 2016; Qi and Kulkarni, 2016).
Description of the MEMS-based CPC
Figure 1 shows the operating principle of the proposed MEMS-based CPC, which
consists of a reservoir, saturator, condenser, and miniature OPC. To
generate supersaturated vapor and hence to grow UFPs to micro-sized
droplets, our system utilizes a conductive cooling method, with butanol as
the working fluid. The saturator generates saturated vapor by heating the
wetted wall with the working fluid. This saturates the UFP-laden sample,
which is introduced into the saturator at a flow rate of 0.15 L min-1, with
working fluid vapor. The saturated sample then enters a condenser, whose
temperature (10 ∘C) is lower than that of the saturator (40 ∘C). In the condenser, the hot saturated vapor present in the
sample is cooled to reach the supersaturated state. UFPs in the
supersaturated vapor act as condensation nuclei and grow to droplets.
Schematic illustration of the MEMS-based CPC. Our system consists
of four parts: the reservoir, saturator, condenser, and miniature OPC. The
reservoir supplies the working fluid to the saturator via capillary action
by the micropillar-type wick. The saturator heats the working fluid to
generate saturated vapor. The saturated air becomes supersaturated when
cooled by the condenser. UFPs grow into micro-sized droplets in the
condenser. They are counted by the miniature OPC.
The cornerstone of our system is its compact, cost-efficient, and portable
features for the on-site monitoring of UFPs. Figure 2a shows our system with
the customized circuit. By using the MEMS technology, our system can
generate supersaturated vapor and grow droplets on a chip-scale system for
significant decreases in size, weight, and power consumption. The
circuit, whose dimensions are 90 mm × 65 mm, simultaneously reads the data
from the miniature OPC, temperature sensor, and flow sensor (model
FS1012-1020-NG, IDT Co., USA), and it controls the power of the heaters,
cooling modules, and micro pump (model 00H220H024, Nidec Co., Japan) via a
pulse-width modulation (PWM) method. In order for our system to be a
stand-alone device, the feedback loops based on the
proportional–integral–differential (PID) algorithm are implemented in the
micro control unit of the circuit, and their gains can easily be controlled
using serial communication. The heaters, temperature sensors, and wick are
monolithically integrated with the glass slide (Fig. 2b). They perform
crucial roles: supplying the working fluid to the saturator and condenser
via capillary action and generating supersaturated vapor.
(a) Optical image of the MEMS-based CPC. (b) Magnified image of
the heaters, resistive temperature sensors, and wick on the glass slide.
(c) Image from a scanning electron microscope (SEM) of the micropillar-type wick.
As shown in Fig. 2c, micropillar arrays serve as a wick with a 40 µm
diameter, 100 µm length, and 100 µm pitch. The dimensions of
the micropillar-type wick were experimentally determined to be capable of
pumping the working fluid from the reservoir and spreading it over the
entire surface of the saturator to ensure that the saturator wall is always
in the wetted condition. SU-8 is a negative-tone photoresist and was chosen
as the structural material for the micropillar-type wick because it provides
a high patterning resolution (∼ 1 µm) and outstanding
chemical/thermal stability and guarantees the high durability of our system
(Chang and Kim, 2000; Kim et al., 2003).
In order to generate supersaturated vapor with a constant saturation ratio,
the temperatures of the saturator (40 ∘C) and condenser (10 ∘C) must be controlled to the designed values. For this purpose,
the resistive heater was uniformly patterned on the inner wall of the
saturator, and a miniature thermoelectric cooling module was attached to the
outer wall of the condenser. Both temperatures were monitored with resistive
temperature sensors having an accuracy of ±0.1∘C and located at the outlet of the saturator and center of the
condenser, respectively. A customized circuit was used with the PWM method
to adjust the power for the heaters and thermoelectric cooler module and
thus to control the temperatures.
In the condenser, while the supersaturated vapor grows UFPs to droplets, the
working fluid vapor may condense on the wall and clog the channel. Thus,
like the saturator, the condenser also had micropillar-type wicks. On a
rough surface, the actual contact angle of a working fluid droplet is lower
than the contact angle on a smooth surface (Chen et al., 2013).
Thus, the micropillar array increases the wettability of the working fluid,
suppressing the droplet formation on the wall and draining the condensed
working fluid to the reservoir. While the diameter and length were the same,
the pitch of the micropillar-type wick (130 µm) was larger than that in
the saturator (100 µm).
A spacer, which included the channels and inlet/outlet connectors, was
fabricated with a 3-D printer during a single printing. The material for the
spacer was UV-curable epoxy, which has a high printing resolution (minimum
linewidth: 0.3 mm) and can endure temperatures up to 80 ∘C
(Stansbury and Idacavage, 2016). The channel of the saturator was winding
to increase the residence time and thus ensure fully saturated vapor. The
width, height, and length of the saturator channel were 6, 3, and 150 mm,
respectively, and the corresponding residence time at the given flow rate
was 1.08 s. The maximum Reynolds number in the channel at the given flow
rate was only 32, which means that the sample stream in the channel was in
the fully laminar regime.
The detection part of the commercial OPC (Innoair-615D, Innociple Co., Republic of Korea),
which provided a time resolution of 6 s, was used as the optical detector in
our system. It consists of the sensing chamber and optics (laser,
cylindrical lens, elliptic mirror, photodiode, and light trap). The
introduced droplets are arranged in a row in the acceleration nozzle (i.e.,
the outlet of the condensation chip). Then they enter the sensing chamber.
The droplets then pass through the place where the condensed thin beam is
irradiated. The mirror directs the light scattered from a droplet to the
sensing surface of the photodiode. When the laser beam passes through the
cylindrical lens, the shape of the laser beam at the focal point is not a
point but a very thin surface. In addition, the acceleration nozzle at the
chip outlet is only 0.8 mm in diameter and is located about 1.5 mm below the
point through which the beam passes. Therefore, under the condition that
there is no coincidence error (the two particles do not pass through the
viewing volume at the same time), almost all the grown micro-droplets are
counted in the optical detector.
Fabrication process
As shown in Fig. 3, the MEMS-based particle growth system consists of a
top plate, bottom plate, and 3-D-printed channel. The fabrication process was
identical for the two plates. The essential elements on the plates (heaters,
resistive temperature sensors, and the micropillar-type wick) were fabricated
through a simple photolithographic process. An electron beam evaporator was used to
deposit a thin metal layer (30/300 nm of Ti / Au) on the plates. Then, a
positive-tone photoresist was spin-coated at 3000 rpm for 30 s, and it softly
baked on a hot plate at 95 ∘C for 1 min. After baking, the
photoresist was exposed to UV light (wavelength: 365 nm, exposure dose: 55 mJ cm-2) and developed with a photoresist developer. Then, the wet
etching process was performed to define the electrodes (Fig. 3a). To
enhance the repeatability of the temperature sensors, the fabricated
electrodes were heat-treated at 300 ∘C in ambient atmosphere. The
SU-8 negative-tone photoresist was used as the material for the micropillar
arrays. A 100 µm layer of SU-8 (model 2100, Microchem Corp., USA) was
spin-coated onto the plates at 3000 rpm for 30 s and baked at 85 ∘C for 120 min. Then, it was exposed to UV light (exposure dose: 240 mJ cm-2) to define the micropillar array structure. Next, a
post-exposure bake (PEB) was conducted through a two-step ramping process on
a hot plate: 65 ∘C for 5 min and then 95 ∘C for 120 min. The exposed SU-8 was developed with an SU-8 developer (Fig. 3b).
Simplified fabrication process of the MEMS-based CPC.
The fabricated channel and plates were packaged with polymethyl methacrylate
jigs and silicon rubber gaskets (Fig. 3c). The cooling modules comprised a
thermoelectric cooler, heat sink, and mini fan. They were attached to the
outer walls of the condenser (Fig. 3d). Finally, the fabricated chip was
inserted into a card connector, which was linked to the control circuit
board.
Experimental setup
Figure 4 shows the experimental setup used to characterize the overall
performance of our system in terms of three aspects: (a) the clean air
supply system, (b) monodisperse-particle-generating system, and (c) performance comparison system. Compressed air was used as the carrier gas.
Any moisture, oil droplets, and particles in the compressed air were removed
in the clean air supply system with an oil trap, diffusion dryer, and
high-efficiency particulate air (HEPA) filter. The purified air was then
supplied to the particle generating system at a flow rate that was
accurately controlled by a mass flow controller (VIC-D200, MKP Co., Republic of Korea). Ag
particles ranging in size from 3 to 140 nm were generated by an Ag particle
generator (EP-NGS20, EcoPictures Co., Republic of Korea). The particles were electrically
charged by a soft X-ray charger (XRC-05, HCT Co., Republic of Korea). Then they were
classified according to diameter with two types of DMAs: (1) nano DMA (model
3085, TSI Co. Ltd., USA) for particles in the size range from 3 to 10 nm or
(2) long DMA (model 3081A, TSI Co. Ltd., USA) for particles in the size
range from 5 to 140 nm. Next, the number concentration of the monodisperse
Ag particles was controlled (0–24 000 cm-3) in the dilution bridge
system by adjusting the needle valve. Finally, the concentration-controlled
and monodisperse Ag particles were introduced into our system and reference
instrument, which was either a CPC (model 3772, TSI Inc., USA) or an aerosol
electrometer (model 3068B, TSI Inc., USA).
Schematic of the experimental setup for evaluating the performance
of the MEMS-based CPC.
Because of the large difference between the flow rates of the reference
instrument and our system, the following procedures were carried out to
verify that particles with the same concentration were introduced into the
two systems. First, to minimize the particle loss induced from the
turbulence at the bifurcation, a flow splitter with a very small angle of
split (model 3708, TSI Inc., USA) was used. The tubes which lead to
both systems were electrostatically dissipative to minimize the electrostatic
particle loss, and their lengths were carefully adjusted to match the
transportation times. To verify that the particles which were introduced
into both systems had the same concentrations, it was confirmed that the
counting efficiency was close to 100 % when particles with a size of 100 nm were introduced (it was assumed that they were activated and grew into
droplets with 100 % efficiency). Then, while reducing the size of the
introduced particles to 40 nm by adjusting the voltages of the DMA, it was
confirmed that the counting efficiency remained constant. Through these
procedures, it was verified that the concentrations of the particles
delivered to the two systems were the same. The loss of our system was
characterized using the counting efficiency, since it is defined as the
efficiency of the system at detecting the introduced particles, thereby
describing the overall transportation and activation efficiencies.
Results and discussionWorking-fluid transmission and evaporation
In order to characterize the wick capability of the working-fluid
transmission and evaporation, a rectangular test sample was used. The sample
included a heater and temperature sensor, and its length was equal to the
saturator maximum length of our system (38 mm).
(a) Schematic of the capillary rise experimental setup. (b) Selected video frames from the rise of the working fluid using
the micropillar-type wick. (c) The dry-out region formation as the surface
temperature increased.
To identify the supply capability of the wick, a capillary rise experiment
was performed (Fig. 5a). The sample was slowly lowered to the surface of
the working fluid. Once the bottom of the sample touched the surface (t=0 s), the rise of the working fluid on the sample was recorded. As shown in
Fig. 5b, the forefront of the working fluid rose rapidly at the beginning
and then slowed down gradually. The forefront of the working fluid reached
the endpoint of the saturator in 35 s and thus wetted the whole surface of
the sample.
In the saturator, the working fluid transported by the wick is vaporized at
elevated temperatures. When the rate of the working-fluid evaporation is
higher than that of the transportation, a dry-out region is formed. This
phenomenon must be minimized because the partial vapor pressure near the
wall must be kept close to the saturated condition. Figure 5c shows optical
images of the dry-out region formation as the surface temperature increased.
The temperature of the sample surface was increased in increments of 10 ∘C; at each temperature, the system operated for 180 s. The
dry-out region clearly did not form when the surface temperature was equal
to the designed saturator temperature (40 ∘C) or even when it
reached 70 ∘C. At 80 ∘C, the front of the working
fluid started to recede, so a dry-out region formed. At 90 ∘C,
the area of the dry-out region accounted for approximately 20 % of the
heated surface. These results demonstrate that the amount of the working
fluid supplied from the wick was greater than the rate of evaporation in the
condenser at the design temperature. Moreover, the saturator temperature can
be raised to 70 ∘C to increase the saturation ratio and hence
decrease the Kelvin diameter in order to enable the system to detect smaller
particles.
In order to obtain optical images of the dry-out region formation, a single
glass slide with patterned electrodes and micropillar array was used.
For this reason, there was no flow rate during the experiment. Although, due
to the advection, the flow rate affects the area of the dry-out region to
some extent, the vapor pressure at which the dry-out region started to form
(164.1 mmHg at 80 ∘C) was 8.7 times larger than the designed
value of the saturator (18.9 mmHg at 40 ∘C). Thus, although there
was no flow above the butanol surface when measuring the dry-out region, the
dry-out region did not occur in the saturator of the MEMS-based CPC under
operating conditions.
Droplet size distribution
Figure 6 shows the size distribution of the droplets generated from the
MEMS-based particle growth system. Monodisperse Ag particles in the size
range from 20 to 140 nm were used as a test aerosol, and their number
concentrations were fixed at around 2000 cm-3 by adjusting the valves
of the dilution bridge. The sampling time for measuring each droplet
distribution was 2 min, and the corresponding measurement uncertainty based
on Poisson statistics was 0.13 %. All the error bars at each data point
represent the standard deviations. A commercial OPC (OPC-N2, Alphasense, UK)
was used for measuring the droplet size distribution. It has been reported
that OPC-N2 was capable of not only measuring particles from 0.4 to 17 µm, but it was also capable of having moderate counting performance compared to the reference
OPC (PAS-1.108, Grimm Technologies) (Sousan et
al., 2016). The measurement errors induced from the Mie resonance were not
considered in these data. The average droplet diameter (dd,avg) was 3.1 µm when particles of 20 nm in size, slightly larger than the minimum
detectable size (12.9 nm), were introduced. Because the lower detectable
size of the optical detector in our system was 0.3 µm, the introduced
particles successfully grew into micrometer-sized droplets that were large
enough to be counted by optical means. It was noted that the mean droplet
size did not vary significantly above 40 nm. In addition, most of the grown
droplets were smaller than 10 µm, indicating that droplets tens
of micrometers in size, which could attach to the inner walls of the particle growth
system or optical detector via sedimentation, were barely generated.
Size distribution of the droplets grown from the MEMS-based
particle growth system when Ag particles of specific sizes were introduced.
Size-dependent particle counting efficiency
The counting efficiency (ηd) is defined as the efficiency of the
system at detecting the particles and describes the overall CPC performance.
It is the product of three efficiencies:
ηd=ηtrans⋅ηact⋅ηOPC,
where ηtrans is the efficiency of a particle passing through our system, ηact is the efficiency of growing droplets at the condensation chip, and ηOPC is the efficiency of counting droplets passing through the sensing volume. Because these three efficiencies are strongly
dependent on the particle size, in particular for small particles below ca.
30 nm, the counting efficiency must be characterized as a function of the
particle size.
The counting efficiency was obtained from the ratio of the concentration
measured with our system to the reference number concentration. The
reference concentration (Nref) was obtained from the electrical current
(I) measured by the aerosol electrometer (AE).
I=Nref⋅ne⋅Q,
where n is the number of elementary charges (+1) per particle, e is the
elementary charge (1.6×10-19 C), and Q is the flow rate
drawn into the AE.
Particle counting efficiency of the MEMS-based CPC as a function
of the particle size and saturator temperature. The particle size at which
the particle counting efficiency is 50 % was 12.9 nm (TS=40∘C), 17.3 (TS=35∘C), and 20.4 (TS=30∘C), respectively.
Figure 7 shows the size-dependent counting efficiency of the MEMS-based CPC.
The size range of the Ag particles was controlled so that the concentration
range was 1000–2000 cm-3. The sampling times for each data point were
300 s, and the measurement uncertainty based on Poisson statistics was 0.02 %. To evaluate the effect of the temperature difference, the counting
efficiency was characterized when the condenser temperature (Tc) was 10 ∘C and the saturator temperatures (Ts) were 30, 35, and 40 ∘C. At 40 ∘C (the design value of the saturator
temperature), the same experiments were repeated three times to confirm the
measurement reliability. When the saturator temperature was 40 ∘C, it was found that our system detected 1 % of UFPs with size 5 nm, and
the detection efficiency increased sharply above 9 nm. This was primarily
because the activation efficiency (ηact) increased when the
particle size exceeded the Kelvin diameter (2.34 nm). The transport
efficiency (ηtrans) also increased, because the diffusivity of a
particle decreases with increasing particle size. The counting efficiency
data were curve-fitted using
ηd=α+(β-α)1+dp/γδ,
where α, β, γ, and δ are fitting constants
with values of 101.96, 2.00, 12.99, and 4.70, respectively. The corresponding
minimum detectable size is defined as the size at which particles are
detected with 50 % efficiency. It was found to be 12.9 nm. The detection
efficiency was 90 % at 20.1 nm and reached 95 % at 22.9 nm. It was
close to 100 % and constant in the size range from 25 to 140 nm,
indicating that the internal particle loss in this size range was
negligible.
The minimum detectable size was higher than that of the commercial CPC
operating at the same temperature difference. Because the saturator and
condenser were close to each other and thereby have thermal interference,
the condenser temperature might be higher than its originally designed value
due to the heat transfer from the saturator to the condenser. It is expected
that this problem can be solved by increasing the thickness of the thermal
barrier between the saturator and condenser.
(a) Time series of the number concentrations with the MEMS-based
CPC and aerosol electrometer. (b) One-to-one comparison of the measured
number concentrations for both systems.
Detectable concentration range
Even if filtered air is introduced into the system, droplets may form in the
condenser via homogeneous or ion-induced nucleation. Droplets without UFP
nuclei cause false counting, which makes the system read a higher
concentration than what is actually present. This phenomenon is critical, especially in
low-concentration environments. To evaluate the false counting of our
system, it was operated for 1 h with a HEPA filter connected to its inlet.
When the temperature difference between the saturator and condenser was set
to 30 ∘C, the average number concentration and counting rate
during the measurement period (background concentration) were 0.05 cm-3
and 0.125 s-1, respectively, indicating that homogeneous nucleation
hardly occurred. Thus, the temperature profile was uniformly established
inside the condensation chip because homogeneous nucleation typically occurs
at low temperatures in regions where the local saturation ratio is high.
Owing to the low false count performance, our system can be applied to
monitoring UFPs in clean rooms of class 100 or 1000 whose background
concentrations are on the order of 1 cm-3 (Liao et al., 2018). In
these environments, the measurement uncertainty based on Poisson statistics
is expected to be 10 % in a sampling time of 40 s for a given flow rate
(0.15 L min-1= 2.5 cm3 s-1).
Time series of the number concentrations measured with our system
and the reference CPC when the concentration and size were varied.
We characterized the maximum detectable number concentration of our system
by comparing the number concentration with that of the AE (i.e., reference
number concentration). Monodisperse Ag particles with a size of 25 nm were
used as test particles, and their number concentration was increased at
intervals of 3 min. Figure 9 shows (a) the time series of number
concentrations measured with the MEMS-based CPC and an AE and (b) a
one-to-one comparison of the measured number concentrations by both systems.
As shown in Fig. 9b, relatively large fluctuations were observed at number
concentrations of < 1000 cm-3 because of the electronic noise
of the AE. However, in the number concentration range of 1000–5000 cm-3, the overall difference between the concentrations of our system
and the AE was only 4.1 %, which proves the high accuracy of our system.
When the concentrations exceeded 5000 cm-3, the deviation between the
measured and reference number concentrations gradually increased. One of the
main reasons was the coincidence error. When multiple particles
simultaneously passed through the sensing volume, the miniature OPC could
not count them separately. The maximum detectable concentration of our
system, which was defined as the number concentration at a difference of 20 %, was 7200 cm-3. Thus, when the concentration exceeded 6852 cm-3, the logarithmic function was fitted to the response curve of our
system, as expressed in Fig. 8b. When the calibration based on the fitted
curve was applied to our system, the average difference between both systems
was within 2.8 %.
Performance comparison with the reference CPC
The MEMS-based CPC was tested in parallel with a reference CPC. The
classification voltage of the DMA was changed to introduce monodisperse Ag
particles varying in concentration and size into both systems. The total
measurement time was 600 s, and the measured data were averaged in intervals
of 6 s. Figure 8 shows the measured number concentrations of our system and
the reference CPC. When particles larger than the minimum detectable size
(12.9 nm) were introduced, our system clearly showed high accuracy and
precision comparable to those of the reference CPC: a difference of 4.54 % at a low concentration (7.99 cm-3 at 28 nm) and a -9.12 %
difference at a high concentration (4544.82 cm-3 at 16 nm).
Time series of the number concentrations measured by our system
when it was tilted.
Figure 10 shows the measurement results of our system when it was tilted as
shown in the inset. Monodisperse 25 nm Ag particles were introduced, and
their concentrations were increased in steps from 0 to 4000 cm-3. Since
the measurement was carried out for about 500 s at each angle, the
measurement uncertainty of each section was below 0.01 %. When our system
was oriented perpendicular to the surface, the difference in counting
efficiencies between our system and the reference CPC was 2.04 %, which
was similar to the result of the size-dependent counting efficiency. When a
30∘ angle was applied, the difference in counting efficiency was
7.07 %. At 60∘, the measurement difference compared to the
reference CPC exceeded 10 % (16.3 %). Thus, it was found that, at a
tilt angle of 60∘ or less, the MEMS-based CPC can monitor UFPs
without significant degradation in accuracy.
The deviation of the counting efficiency induced from applying a tilt angle
can be explained by the sedimentation of droplets in the condenser. At 0∘, because the direction of gravity was identical to the
direction of the sample flow, the probability that grown droplets hit the
condenser wall via sedimentation was negligible. However, with an increasing
tilt angle, the velocity vector of a droplet perpendicular to the channel
increased, which led to a decrease in counting efficiency.
Conclusions
The MEMS-based CPC was developed for sensitive and precise monitoring of
UFPs at a particular point of interest. Our system comprises two parts: the
MEMS-based condensation chip and a miniature OPC. To achieve compactness,
the key elements for growing droplets (i.e., the saturator, reservoir, and
condenser) were integrated on a 52.5 mm × 60 mm glass slide through
a simple photolithographic process and 3-D printing. Quantitative experiments
with an AE (model 3068B, TSI Inc., USA) and CPC (model 3772, TSI Inc., USA)
demonstrated that our system can count UFPs with a size of 12.9 nm and
measure the number concentration with high accuracy (within 4.1 %
difference compared to AE) in the range of 1000–5000 cm3.
In terms of compactness and cost-efficiency, our system is superior to
conventional instruments. The physical volume of our system is only 8.5 %
of the volume of the commercially available portable CPC (e.g., model 3007,
TSI Inc., USA). Furthermore, its manufacturing cost can be minimized owing
to batch fabrication based on MEMS technology. These advantages allow the
system to be successfully applied to various fields that require UFP
monitoring. Furthermore, if combined with the recently developed miniature
DMA, our system can help realize a mini-scanning mobility particle sizer
(mini-SMPS) for an accurate and precise measurement of the UFP size
distribution at particular points of interest.
Data availability
Data are available from the authors upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/amt-12-5335-2019-supplement.
Author contributions
SJY, HBK, and YJK conceptualized the study. HBK, USH, SJY,
DHK, and SML performed the experimental work. SJY wrote the
original draft, and SJY, HBK, and DHK revised it. JaH and JuH
provided the resources. All authors discussed and commented on the
manuscript and approve of its content.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work was supported by the Samsung Research Funding & Incubation Center
of Samsung Electronics (grant no. SRFC-TA1803-05) and by the
Technology Innovation Program (grant no. 10077651, Development of IoT fusion sensor
system based on artificial intelligence) funded by the Ministry of Trade,
Industry and Energy (MOTIE, Republic of Korea).
Financial support
This work was supported by the Samsung Research Funding & Incubation Center of Samsung Electronics under Project Number SRFC-TA1803-05.
This material is based upon work supported by the Ministry of Trade, Industry and Energy (MOTIE, Republic of Korea) under Industrial Technology Innovation Program Number 10077651.
Review statement
This paper was edited by Joachim Curtius and reviewed by three anonymous referees.
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