Separation and Purification Technology 361 (2025) 131502
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Separation and Purification Technology
journal homepage: www.elsevier.com/locate/seppur
High-precision and high-efficiency particle classification of micron powders
based on 3D printed microcyclones: Characteristics, mechanisms
and applications
Hanyu Zhang a,1 , Yifei Li a,1, Qifan Yu a , Shijie Yan a , Zhenpeng Huang a, Lei Chen a ,
Yinglei Wang b, Bin Chen b, Yuezhou Liu b, Zhishan Bai a, Bingjie Wang a,*
a
b
School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai 200237, PR China
Xi’an Modern Chemistry Research Institute, Xi’an 710065, PR China
A R T I C L E I N F O
A B S T R A C T
Editor: Luo Guangsheng
Highly monodispersed micron powders are considered to be one of the most promising materials due to their
excellent physicochemical properties. However, the current particle classification techniques are restricted by the
issues of low-precision and low-efficiency, resulting in uneven particle size distribution and large individual
performance differences, making it difficult to meet the application requirements of high-quality products. In this
study, aiming at the high-precision cyclone classification of micron powders, a visual experiment platform for
particle classification was constructed by combining with 3D printing technology. The influence laws of oper­
ation parameters, structure parameters of the cyclone, and physical properties parameters of particles on the
classification precision and efficiency were systematically studied. The research results indicated that by opti­
mizing the operating conditions, the classification precision of the target particles can be as high as 95.94%, and
the classification efficiency can reach 72.89%. In addition, the mechanism of particle cyclone classification was
revealed. The “coarse particles entrainment” in the overflow and “fine particles entrainment” in the underflow
phenomena were analyzed, and corresponding solutions were proposed. The results of this study can provide
theoretical basis and technical support for the high-precision and high-efficiency particle classification of micron
powders.
Keywords:
Micron powders
Cyclone
Particle classification
Classification mechanism
1. Introduction
In general, micron powders are ultrafine particles with particle size
ranging from 1 μm to 100 μm. Micron powders have been widely used in
electronic information, chemical engineering, materials science and
aerospace fields because of high specific surface area, high reactivity,
good dispersion, etc. [1–5]. Among them, high particle size concentra­
tion brings better thermal conductivity and fluidity to the material,
which is a key core parameter for micron powders in cutting-edge ap­
plications such as electronic information, aerospace and deep sea
exploration [6–11]. For example, in the semiconductor industry, the
narrow size distribution of micron powders (1 ~ 20 μm) is critical to the
packaging process. The precise control of particle size ensures consis­
tency and reliability in the packaging process, thereby maintaining the
high standards of quality and performance required for semiconductor
products [12]. In the aerospace field, the fabrication of lightweight
structural parts relies on laser melting technology, which requires
micron powders with a narrow size distribution (15 ~ 53 μm) for effi­
cient implementation [13,14]. In addition, granular powders used for
thermal and environmental barriers must also fall within a specific
narrow size distribution (0 ~ 30 μm) to ensure complete substrate
coverage [15]. However, micron powders prepared by traditional me­
chanical grinding have the defects of large particle size changes and
wide particle size distribution, which can not meet the application re­
quirements of high-quality products [16]. The decrease of sphericity and
fluidity of particles will affect the filling degree of particles [17,18].
Moreover, if the particle size is too small, the increase of surface energy
and specific surface area will bring the problem of electrostatic
agglomeration between particles [19,20]. Therefore, it is often neces­
sary to combine particle screening techniques to obtain powders with
* Corresponding author.
E-mail address: bjwang@ecust.edu.cn (B. Wang).
1
Hanyu Zhang and Yifei Li contributed equally to this paper.
https://doi.org/10.1016/j.seppur.2025.131502
Received 11 December 2024; Received in revised form 31 December 2024; Accepted 7 January 2025
Available online 7 January 2025
1383-5866/© 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
H. Zhang et al.
Separation and Purification Technology 361 (2025) 131502
narrow particle size distribution.
Particle classification is a technique to separate the particle mixture
into different particle size grades according to the physical properties of
the particle density, size, shape, and so on. Common particle classifi­
cation techniques, such as sieving [21], sedimentation [22], and cen­
trifugal classification [23], require dedicated equipment [24]. With the
advantage of low operating cost, the plate and frame screening machine
has been widely used for the classification of solid or semi-solid mate­
rials. However, due to the determined screen size and screen clogging
problems, classification precision is low and frequent screen replace­
ment is required. Although the structure of the sedimentation classifi­
cation equipment is simple, the shortcomings of large footprint, slow
classification speed and low classification precision cannot be ignored.
Centrifugal classification equipment mainly includes centrifugal settler
and cyclone [25]. Among them, centrifugal settler has high classification
efficiency for large particle size, but its complex structure, high main­
tenance cost and low classification precision limit its application in
micron powder classification.
Table 1
Physical property parameters of micro-powder raw materials.
Materials
Density
Average particle size
3
SiO2
SiC
2200 kg/m
3200 kg/m3
25 μm, 50 μm
25 μm
Table 2
Structural parameters of cyclones.
Structural parameters
Symbol
DN50
DN75
Diameter
Feed diameter
Overflow outlet diameter
Underflow outlet diameter
Cone angle
Overflow tube insertion depth
Straight pipe section height
D
a×b
Do
Du
θ
h0
H
50 mm
8 × 10 mm
15 mm
10 mm
6◦ ~20◦
33 mm
100 mm
75 mm
13 × 15 mm
20 mm
10 mm
15◦
50 mm
150 mm
Fig. 1. (a) Geometry, (b) 3D model and (c) 3D printed cyclones.
Fig. 2. Visual experiment platform for particle cyclone classification: (a) Process diagram; (b) Physical drawings.
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Separation and Purification Technology 361 (2025) 131502
Fig. 3. (a) Particles morphology of 25 μm SiO2 and 25 μm SiC; Initial particle size distribution: (b) 25 μm SiO2 ; (c) 25 μm SiC; (d) 50 μm SiO2.
property parameters on particle classification efficiency, without
considering the comprehensive effects of different parameters on par­
ticle classification precision and classification efficiency. Additionally,
there was a lack of in-depth research on the mechanisms and regulation
of particle cyclone classification.
To fill the research gaps mentioned above, in this study, aiming at
high-precision and high-efficiency classification of micron powders, a
visualization experiment platform for particle cyclone classification was
built in combination with 3D printing technology. The effects of oper­
ating parameters, cyclone structural characteristics, and particle prop­
erties on classification precision and efficiency were systematically
investigated. Optimal classification conditions were identified. The
mechanisms underlying particle cyclone classification were further
explored. Additionally, the “coarse particles entrainment” in the over­
flow and “fine particles entrainment” in the underflow phenomena were
analyzed. The results of this study can provide theoretical basis and
technical support for the high-precision and high-efficiency particle
classification of micron powders.
Table 3
Initial particle size distribution parameters.
Category
D50
(μm)
D90
(μm)
Target particle (0 ~
20 μm) volume ratio
(TPVR20)
Target particle (0 ~
45 μm) volume ratio
(TPVR45)
25 μm
SiO2
25 μm
SiC
50 μm
SiO2
23.75
77.48
44.84 %
71.64 %
22.88
38.87
38.30 %
−
56.17
137.3
19.70 %
40.85 %
Compared with other classification equipment, cyclones offer
numerous advantages, including high efficiency, simple structure, easy
operation, low cost, and large processing capacity, and have been widely
studied in the field of micron particle classification [26–30]. Endres
et al. [31] used a DN20 cyclone separator to classify the target particles
of 0.3 ~ 1 μm, and found that the particle classification efficiency
gradually increased with the extension of cyclic classification time. Hu
et al. [32] utilized a DN10 cyclone to classify target particles with a
particle size larger than 10 μm, and noted that the classification effi­
ciency first increased and then decreased with the increase of Reynolds
number.. Abdollahzadeh et al. [33] introduced a DN15 cyclone sepa­
rator to classify the flake target particles (>10 μm), and found that the
classification efficiency decreased with the decrease of feed speed.
Huang et al. [34] applied a DN50 cyclone separator to classify 30 ~ 50
μm target particles, and found that the classification efficiency increased
with the decrease of underflow diameter. To sum up, the current re­
ported literature only explores the effects of single parameters such as
operating parameters, equipment structure parameters and particulate
2. Materials and methodology
2.1. Materials
In this study, two types of micro-powder raw materials were pur­
chased from Bisley New Materials Suzhou Co., Ltd. for particle classifi­
cation experiments, including spherical SiO2 particles and lamellar SiC
particles The average particle sizes of the spherical SiO2 particles were
25 μm and 50 μm, and the lamellar SiC particles were 25 μm. The micropowder raw materials were used directly as the dispersed phase without
any pre-treatment, and their physical properties are listed in Table 1. In
addition, tap water (density of 998.3 kg/m3) was used throughout the
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H. Zhang et al.
Separation and Purification Technology 361 (2025) 131502
Fig. 4. Effect of inlet flow rate and split ratio on particle classification precision (25 μm SiO2).
experiment as the continuous phase.
mixing tank is maintained during the particle classification process, and
the process is completed when there is no micron powder material
residue by naked eyes at the bottom of the mixing tank. After the
experiment, the sample in storage tank 1 and storage tank 2 were fully
stirred, and then the particle size analysis was carried out.
2.2. Design of 3D printed cyclones
In this study, DN50 and DN75 cyclones were used to perform the
particle classification of the target particle size. The relevant parameters
of the cyclones refer to the cyclone design theories [35,36], as shown in
Table 2. Besides, the 3D model was established with the Solid­
Works2019 software, as shown in Fig. 1b. Considering the flexibility of
structural replacement, the model was designed in two parts: cylindrical
section and conical section. And then, the two parts were assembled by a
similarly flange structure containing sealing gasket. Besides, the inlet
tube of cyclone was designed as a gradually transition form from a circle
to a rectangle for reducing the pressure loss. In view of the difficulties of
micro-structure machining, especially machining accuracy and cost, the
cyclone used in this study was accurately manufactured by 3D printing
technology with the Somos photosensitive resin. The digital images of
3D printed cyclones are shown in Fig. 1c.
2.4. Analytical methods
Scanning electron microscopy (SEM, S-3400, Hitachi, Japan) was
used to analyze the micromorphology of micro-powder raw materials.
The particle size distribution characteristics of micron powders before
and after cyclone classification were calculated by laser particle size
analyzer (BT-9300ST, Dandong Bette Instrument Co., Ltd.).
The classification efficiency is calculated by the following formula:
E=
(β − α)(α − γ)
× 100%
α(1 − α)(β − γ)
(1)
Where, E is the classification efficiency; α is the mass fraction of the
target particles in the feed; β is the mass fraction of the target particles in
the overflow outlet; γ is the mass fraction of the target particles in the
underflow outlet.
2.3. Experimental equipment and procedure
Fig. 2 shows the visual experiment platform for particle cyclone
classification. Micro-powder raw materials and tap water were contin­
uously stirred in a mixing tank in a certain proportion until evenly
mixed. After that, the mixed liquid was pumped into the cyclone by
centrifugal pump to perform the particle classification. By changing the
frequency of the centrifugal pump and the opening of the valve at the
underflow outlet of the cyclone, the inlet flow rate and the split ratio can
be adjusted, respectively. After cyclone classification, the target parti­
cles (light phase) were collected in the storage tank 2 from the overflow
outlet, while the non-target particles (heavy phase) were imported into
the storage tank 1 from the underflow outlet. The liquid level of the
3. Results and discussion
3.1. Characterization of initial slurry
As shown in Fig. 3a, combined with optical microscopy and SEM, the
spherical structure of SiO2 particles and the lamellar structure of SiC
particles were confirmed. By mixing a certain amount of particles with
tap water in a certain proportion, the original material with a mass
concentration of 0.8 g/L was obtained. To accurately describe the par­
ticle size distribution before and after cyclone classification, the key
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H. Zhang et al.
Separation and Purification Technology 361 (2025) 131502
Fig. 5. Effect of cyclone cone angle on particle classification precision (25 μm SiO2).
evaluation parameters were used, including D50, D90 and target parti­
cle volume ratio (TPVR). Among them, D50 referred to the particle size
when the accumulation reached 50 % in the particle size distribution,
and D90 referred to the particle size when the accumulation reached 90
%. The particle size distribution of three different raw materials was
shown in Fig. 3b-3d and Table 3.
oscillation decline was more obvious. It is worth mentioning that when
the inlet flow rate was 22.5 L/min, the D50 and D90 values of non-target
particles were 41.33 μm and 87.32 μm, respectively. Nevertheless, the
corresponding TPVR20 at the underflow outlet was relatively low, only
16.93 % (Fig. 4d). When the inlet flow rate increased further, it was
difficult for the fine target particles to enter the upward swirl at the
center of the cyclone. On the contrary, with the coarse non-target par­
ticles entering the downward swirl near the inside wall, the TPVR20 at
the underflow outlet increased obviously.
3.2. Effect of operating parameters on classification precision
3.2.1. Inlet flow rate
The inlet flow rate is critical to the precision and efficiency of particle
classification [37,38]. In this study, a DN50 cyclone with a cone angle of
15◦ was used to explore the effect of inlet flow rate (17.5 ~ 35 L/min) on
the classification precision of 25 μm spherical SiO2 particles. The
experimental split ratio was set at 9/1.
As shown in Fig. 4a (black dashed line), at the overflow outlet of
cyclone, with the increase of inlet flow rate, the D50 value of target
particles slowly decreased from 10.04 μm to 7.158 μm. On the contrary,
the D90 value of target particles (solid black line in Fig. 4a) showed a
relatively obvious trend of oscillation decline. And the oscillation
amplitude of D90 decreased with the increase of inlet flow rate. Corre­
spondingly, with the increase of inlet flow rate, the TPVR20 showed an
opposite trend (Fig. 4c, black solid line). It should be noted that when
the inlet flow rate was 22.5 L/min, the D90 reached the minimum value
of only 16.84 μm. At this time, the TPVR20 reached the maximum value
of 95.94 %, indicating that the particle classification precision reached
the highest under this operating condition.
At the cyclone underflow outlet, as shown in Fig. 4b (black dashed
line), the D50 value of the non-target particles was greater than 27.5 μm.
With the increase of inlet flow rate, the D50 oscillated significantly
around 35 μm. The variation trend of D90 with the flow rate was
consistent with that at the overflow outlet, but the amplitude of
3.2.2. Split ratio
The split ratio of cyclones was defined as the ratio of the overflow to
the underflow, which is also an important operating parameter affecting
the particle cyclone classification. As shown in Fig. 4a, the change of
particle size distribution D50 at the cyclone overflow with the split ratio
could be negligible in the range of inlet flow rates of 17.5 ~ 35 L/min.
The D90 value of particles varied significantly with the change of split
ratio (maximum range up to 14.19 μm). Especially, in the inlet flow rates
range of 22.5 ~ 32.5 L/min, the D90 value gradually decreased with the
increase of split ratio. When the inlet flow rate was 22.5 L /min and the
split ratio was 9/1, the particles classification precision at the overflow
outlet reached the highest (as shown in Fig. 4c). On the contrary, at the
underflow outlet (Fig. 4b), the D50 value of particles fluctuated greatly
(maximum range reached 17.46 μm) with the increase of split ratio. By
comprehensively comparing the TPVR20 of overflow (Fig. 4c) and
underflow (Fig. 4d), it was found that the overflow classification pre­
cision reached the highest (95.94 %) and the TPVR20 at the underflow
account for a relatively low proportion (16.93 %) only when the inlet
flow rate was 22.5 L/min and the split ratio was 9/1. Therefore, under
the premise that the structure of the cyclone remains unchanged, the
subsequent particle classification experiments were carried out under
this operating condition, unless otherwise stated.
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H. Zhang et al.
Separation and Purification Technology 361 (2025) 131502
Fig. 6. Effect of cyclone diameter on particle classification precision (25 μm SiO2).
3.3. Effect of cyclone structure parameters on classification precision
16.93 %, which was lower than that with a cone angle of 20◦ . Thus,
unless otherwise stated, a cyclone with a cone angle of 15◦ was used in
subsequent particle classification experiments.
3.3.1. Cyclone cone angle
For cylindrical conical cyclone, the conical section is the main area of
cyclone classification. The change of cone angle will also affect the
particles classification precision [39–41]. In this study, DN50 cyclone
with different cone angles (6◦ ~20◦ ) was used to investigate the effect of
cone angle on particle classification precision of 25 μm spherical SiO2
particles.
As shown in Fig. 5a, although the inlet flow rate changed, the in­
fluence of the change of the cone angle on the D50 value of particles at
the overflow outlet was negligible. However, with the increase of the
inlet flow rate, the D90 value of particles at the overflow outlet showed a
decreasing trend. In particular, under the optimal operating conditions
(inlet flow rate of 22.5 L/min, split ratio of 9/1), when the cone angle
was 15◦ and 20◦ , the D90 values at the overflow outlet were similar and
reached the minimum value, which was 16.84 μm and 16.30 μm,
respectively. Correspondingly, as shown in Fig. 5c, the TPVR20 at the
overflow outlet reached 95.94 % and 96.56 %, respectively, indicating
that the target particles at the overflow had narrow size distribution and
high classification precision.
In addition, as shown in Fig. 5c, the effect of cone angle on D50 value
of particles was relatively small at the underflow outlet. Additionally, it
should be noted that under the optimal operating conditions, the D90
(87.32 μm) at the underflow outlet of cyclone with a cone angle of 15◦
was greater than that with a cone angle of 20◦ (81.56 μm). The results
showed that a cyclone with a cone angle of 15◦ resulted in less
entraining of target particles at the underflow outlet. This conclusion
could also be obtained from the TPVR20 at the underflow outlet
(Fig. 5d).
As shown in Fig. 5d, under the same operating condition the TPVR20
at the underflow outlet of cyclone with a cone angle of 15◦ was only
3.3.2. Cyclone diameter
The diameter of cyclone is also an important structure parameter
affecting the particle classification [42]. As shown in Fig. 6, The use of a
larger diameter cyclone needs to match with a larger inlet flow rate to
obtain a more stable particle size control. As shown in Fig. 6a, with the
increase of inlet flow rate, the D50 value of particles at the overflow
outlet of DN50 and DN75 cyclone showed a slow decline. In the case of
the same inlet flow rate, the D50 and D90 values at the overflow outlet
were increased by employing DN75 cyclone. It indicated that the coarse
particles, which should be moving downward and collected at the
underflow outlet, failed to enter the downward swirl at the side wall in
time, but move along with the upward swirl at the center, resulting in
the phenomenon of “coarse particles entrainment” in the overflow.
Furthermore, this result also means that the increase of cyclone diameter
is to improve the stability of particle size control at the expense of
particle classification precision.
However, compared with DN50 cyclone, the difference is that the
D90 value of particles at the overflow outlet of DN75 cyclone did not
show an obvious oscillation change with the increase of inlet flow rate,
but continued to decrease. This result can also be obtained from the
change of the TPVR20 at the overflow outlet. As shown in Fig. 6c, with
the increase of inlet flow rate, the TPVR20 at the overflow outlet of DN75
cyclone rose persistently. Under the obtained optimal operating condi­
tion (inlet flow rate of 22.5 L/min and the split ratio of 9/1), the TPVR20
at the overflow outlet of DN75 cyclone was 71.27 %, which was much
lower than that of 95.94 % at the overflow outlet of DN50 cyclone. Even
under the maximum inlet flow rate (40 L/min), the TPVR20 at the
overflow of DN75 was only 90.91 %. This change indicated that with the
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Separation and Purification Technology 361 (2025) 131502
Fig. 7. Effect of particle diameter on particle classification precision.
increase of the inlet flow, the particle size of the center upwelling flow of
the cyclone gradually decreased. In other words, the task performed by
the cyclone changed from particle classification to liquid–solid separa­
tion. It could be inferred that when a cyclone with a larger diameter is
used, the “fine particles entrainment” phenomenon at the underflow
outlet will become obvious with the increase of the inlet flow rate.
As shown in Fig. 6b and 6d, under the same operating conditions, the
D50 and D90 of particles at the underflow outlet of DN75 cyclone were
always larger than that of DN50. And the TPVR20 was always at a low
level (about 15.50 %). It should be pointed out that when the inlet flow
rate increased to 40 L/min, the proportion of fine particles at the
underflow outlet of DN75 cyclone increased, and the D90 value
decreased significantly. This result confirmed the above hypothesis.
That is, with the increase of the inlet flow rate, the cyclone will perform
the liquid–solid separation task, and the “fine particles entrainment”
phenomenon at the underflow outlet will be obvious. Therefore, for
spherical 25 μm SiO2 particles, a DN50 cyclone should be used to
perform the particle classification process.
oscillate significantly but decreased continuously when the inlet flow
rate increased. As can be seen from Fig. 7c, for the target classification
particle ranging from 0 to 20 μm, the TPVR20 at the overflow outlet can
be significantly improved by using SiO2 particles with smaller initial
average particle size. Under the optimal operating condition, the
TPVR20 of overflow by employing 50 μm SiO2 particles decreased to
73.58 % (TPVR20 of feed was 19.70 %). It was much lower than 95.94 %
of TPVR20 (TPVR20 of feed was 44.84 %) with 25 μm SiO2 particles. In
addition, it should be emphasized that changes in the initial mean par­
ticle size have relatively little effect on the TPVR45 (black dashed lines
and red dashed lines) at the overflow outlet. Specifically, when the
average particle size of initial SiO2 particles increased from 25 μm to 50
μm, TPVR45 values increased to 97.41 % and 100 % (at the overflow
outlet) from 71.64 % and 40.85 % (at the inlet), respectively. This result
can be attributed the fact that when the initial average particle size
increased, “coarse particles (>45 μm) entrainment” phenomenon
resulted in a decrease of TPVR45 at the overflow outlet.
As shown in Fig. 7b and 7d, under the same operating conditions, the
D50 and D90 values of particles at the underflow outlet during the
classification of 50 μm SiO2 were always larger than those of 25 μm SiO2,
and the TPVR were always at a low level. It indicated that with the in­
crease of the average particle size of the feed, the TPVR20 of feed
decreased, and the issue of fine particles entrainment at the underflow
outlet was alleviated. However, it should be pointed out that when
classifying 50 μm SiO2 particles, the particle size distribution at the
underflow outlet (D50: 63.51 μm, D90: 138.6 μm) under the optimal
operating condition was very close to that of the feed (D50: 56.17 μm,
D90: 137.3 μm). It indicated that in this case, most of the particles
directly entered the underflow through downward swirl, and only a
3.4. Effect of particle property parameters on classification precision
3.4.1. Particle diameter
In this study, spherical SiO2 particles with two different average sizes
were used as the classification targets. As shown in Fig. 7a, when 50 μm
SiO2 particles were used as the classification target, the D50 value of
particles at the overflow outlet tended to decrease slowly with the in­
crease of inlet flow rate, but was always higher than that of 25 μm SiO2
particles. In addition, different from the classification results of 25 μm
SiO2 particles, the D90 value of particles at the overflow outlet did not
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Separation and Purification Technology 361 (2025) 131502
Fig. 8. Comparison of the particle size distribution (d) and classification samples of 25 μm SiC particles: (a) feed, (b) overflow, and (c) underflow, respectively.
small amount of fine particles enter the overflow, resulting in low
classification efficiency. Therefore, with the increase of average particle
size of the feed, although the effect of classification precision was not
significant, the classification efficiency was obviously reduced.
changes at the inlet and outlet before and after classification (Fig. 8d), it
can be seen that increasing the inlet flow rate had a very limited effect on
improving the classification efficiency of SiC particles. Moreover, the
problem of “coarse particles entrainment” at the overflow and “fine
particles entrainment” at the underflow still cannot be avoided. The
main reason is that compared with the operating parameters, the
structural parameters such as the diameter of the cyclone have a greater
influence on the classification precision and efficiency of particles.
Therefore, the DN50 cyclone tended to achieve the goal of liquid–solid
separation rather than particle classification for 25 μm SiC particles
under these conditions.
3.4.2. Particle density
In this study, spherical SiO2 particles and lamellar SiC particles with
the same average size were used to explore the effect of particle density
on classification precision. The digital images of irregular lamellar SiC
particles were shown in Fig. 8a. The optimal operating condition for 25
μm SiO2 particles classification (inlet flow rate 22.5 L/min, split ratio 9/
1) was was used to perform the classification task of 25 μm SiC particles.
As shown in Fig. 8c1, a large number of SiC particles with different
particle sizes were collected from the underflow outlet. On the contrary,
almost no SiC particles were collected at the overflow outlet (Fig. 8b1).
It could be inferred that there was a “coarse particles entrainment”
phenomenon at the overflow outlet due to the influence of particle
density and structure. Comparing the digital images of the inlet and
outlet of the cyclone (Fig. 8d), the color of the sample at the overflow
outlet was almost transparent, while that at the underflow outlet was
darker than the feed. It can be attributed to that at a split ratio of 9/1, the
water content of the underflow sample was lower and the particle
concentration was higher than that of the feed.
The SiC particle classification task was performed again by
increasing the inlet flow without changing the cyclone structure pa­
rameters (DN50, cone angle 15◦ ) and classification operation parame­
ters (split ratio of 9/1). However, compared with Fig. 8b and 8c, it can be
seen that with the increase of inlet flow, there is almost no change in the
classification samples at the underflow outlet. In addition, by compre­
hensively comparing the particle size distribution and samples color
3.5. Particle classification results under optimal condition
The particle size distribution and samples color changes at the inlet
and outlet before and after classification under the optimal operating
conditions can be comprehensively analyzed by combining optical mi­
croscopy images (Fig. 9). It is obvious that the particles at the overflow
outlet have a narrower particle size compared to the feed. Oppositely,
the underflow sample was dominated by coarse particles with a small
amount of fine particles, indicating the exist of “fine particles entrain­
ment” phenomenon. Furthermore, according to the contrastive analysis
of particle size distribution (Fig. 9d), it could be found that the D90
value of the particles at the overflow was 16.84 μm, which was close to
the upper limit of the target particle size (20 μm). On the contrary, the
D50 value of the particles at the underflow was increased from 23.75 μm
to 41.33 μm, which was much larger than the average of the target
particle size. In combination with the microscope images of particles
collected from overflow outlet and underflow outlet (Fig. 9b and c), the
high precision of particle cyclone classification was verified. The
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Separation and Purification Technology 361 (2025) 131502
Fig. 9. Comparison of the particle size distribution (d) and classification samples of 25 μm SiO2 particles: (a) feed, (b) overflow, and (c) underflow, respectively.
Table 4
Comparison of classification precision and efficiency of different particle cyclone classification.
Cyclone diameters
(Cone angle)
Materials
(Density)
Target particle
diameters
Particle collection
Initial TPVR
Classification efficiency
Classification precision
Ref
DN20
Si, SiC
mixture
PB
(1200 kg/m3)
Spherical
aluminum
(2700 kg/m3)
Fine sand
0.3 ~ 1 μm
Underflow
≈12 %
≈22.73 %
≈40 %
[31]
>10 μm
Underflow
56.30 %
≈74.90 %
77.50 %
[32]
>10 μm
Underflow
≈80 %
74.51 %
−
[33]
30 ~ 50 μm
Underflow
≈10 %
≈57 %
−
[34]
Spherical
SiO2
(2200 kg/m3)
0 ~ 20 μm
Overflow
44.84 %
72.89 %
(1st cycle)
76.06 %
(2nd cycle)
95.94 %
(1st cycle)
90.94 %
(2nd cycle)
This study
DN10
(14◦ )
DN15
(≈7.8◦ )
DN50
(8◦ )
DN50
(15◦ )
classification precision of the target particles was as high as 95.94 %, far
exceeding the highest level reported in the reported literature (Table 4).
Meanwhile, the classification efficiency also reached 72.89 %, which
was almost the same as the highest value reported.
To further improve classification efficiency, the underflow sample
was collected and performed the multiply particle classification cycles.
As shown in Fig. 10a, with the increase of the classification cycle
numbers, the classification precision slowly decreases, while the clas­
sification efficiency increases first and then decreases. At the 2nd
classification cycle, the classification efficiency increases from 72.89 %
to 76.06 %, while the classification precision decreases from 95.94 % to
90.94 %. In addition, as shown in Fig. 10b, by increasing the classifi­
cation cycles, the cumulative volume fraction of particle size at overflow
shifted to the right as a whole, corresponding to the slow increase of D50
and D90 values. It is proved that the “coarse particles entrainment” at
the overflow outlet is slightly intensified. Considering the priority of
classification precision, it should not be sacrificed in exchange for a
small improvement in classification efficiency.
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H. Zhang et al.
Separation and Purification Technology 361 (2025) 131502
Fig. 10. (a) Comparison of the classification efficiency and classification precision of four consecutive classification cycles; (b) The changes of cumulative volume
fraction of particle size at overflow.
Fig. 11. Particle cyclone classification mechanism.
3.6. Particle cyclone classification mechanism
could effectively alleviate the “coarse particles entrainment” in the
overflow. Although the classification precision of target particles has
been improved, the classification efficiency cannot be increased. In
addition, the unavoidable “fine particles entrainment” problem at the
underflow has to be accepted. Therefore, in order to improve the clas­
sification precision, the target particles should be collected from the
overflow outlet. Taking into account high classification precision and
high classification efficiency of target particles, it is recommended to
perform multiple cyclone classification cycles. However, as a result of
the increase of inlet flow, the radial distribution area of particle size
becomes narrower and moves to the side wall after rearrangement.
Unfortunately, in this case, due to the high-speed self rotation, the
interfacial binding effect of coarse particles on fine particles distributed
in the central region of swirl field is intensified. Thus, some of the fine
particles that should enter the upward inner swirl enter the downward
outer swirl and discharge from the underflow outlet. Additionally, due
to particle interception and interface turbulence, it seems impossible for
the fine particles at the side wall to re-enter the central region through
the coarse particle layer. The above two reasons lead to the deterioration
of the issue of “fine particles entrainment” at the underflow outlet.
Especially, when the inlet flow rate is large enough that no particles can
flow out of the overflow outlet, the particle classification process is
transformed into solid–liquid separation process. Thus, in order to
ensure high classification precision, it is necessary to sacrifice the clas­
sification efficiency appropriately.
Through systematic experimental research, the particle cyclone
classification mechanism was illustrated in Fig. 11. As shown in Fig. 11,
in the three-dimensional swirl field of the cyclone, there exists a
downward outer swirl near the side wall and an upward inner swirl at
the center. Under the combined action of shear force, centrifugal force,
and gravity, particles of different sizes exhibit different migration ve­
locities and movement trajectories. Besides, the immersed particles
exhibit both rotation and revolution behaviors in the swirling field due
to the action of unbalanced shear force. As a result, coarse particles
move rapidly towards the side wall and enter the downward outer swirl.
In contrast, fine particles move towards the center and enter the upward
inner swirl. With the accumulation of rotation and revolution of parti­
cles, the displacement difference gradually emerges and amplifies. The
disordered particles with different sizes, which immersed in the
continuous phase, have been rearranged and stratified through the
integration of the three-dimensional turbulent field in the cyclone. The
result of rearrangement is that fine particles are mostly distributed in the
center of flow field, while coarse particles are more distributed near the
side walls. A barrier layer is formed between each other, preventing fine
and coarse particles from moving in opposite directions. However, due
to the influence of interface disturbance and particle layer interception
of coarse particles, the fine particles near the side wall are difficult to
escape. Thus, these fine particles can only enter the underflow outlet
with coarse particles, resulting in the “fine particles entrainment” phe­
nomenon at the underflow. On the contrary, due to the instability of the
three-dimensional turbulent field in the cyclone, few coarse particles
will randomly enter the upward inner swirl and be collected from the
overflow outlet, causing the “coarse particles entrainment” problem.
In this study, it has been proven that increasing the inlet flow rate
4. Conclusion
To achieve high-precision and high-efficiency classification of
micron powders, a visualization experiment platform for particle
cyclone classification was built in combination with 3D printing
10
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Separation and Purification Technology 361 (2025) 131502
technology. The effects of operating parameters, cyclone structural
characteristics, and particle properties on classification precision and
efficiency were systematically investigated. Not only the optimal oper­
ating conditions of the classification were obtained, but also the classi­
fication mechanism of the particle cyclone was revealed. The major
conclusions from the present study can be summarized as follows:
[4] N.W. Utomo, S. Hong, R. Sinha, et al., Solid-state polymer-particle hybrid
electrolytes: structure and electrochemical properties, Sci. Adv. 10 (27) (2024)
eado4719, https://doi.org/10.1126/sciadv.ado4719.
[5] Z. Wu, H. Niu, J. Wang, et al., A slow-release reduction material of Escherichia sp.
F1 coupled with micron iron powder achieves the remediation of
trichloroethylene-contaminated soil, J. Environ. Manage. 370 (2024) 122765,
https://doi.org/10.1016/j.jenvman.2024.122765.
[6] H. Shi, R. Mohanty, S. Chakravarty, et al., Effect of particle size and cohesion on
powder yielding and flow, Kona Powder Part. J. 35 (2018) 226–250, https://doi.
org/10.14356/kona.2018014.
[7] D. Gou, Z. Fan, S. Wu, et al., The role of HMX particle size in the combustion and
agglomeration of HTPB-based propellant, Aerosp. Sci. Technol. 136 (2023)
108170, https://doi.org/10.1016/j.ast.2023.108170.
[8] J. Ju, Y. Feng, H. Li, et al., Research advances, challenges and perspectives for
recovering valuable metals from deep-sea ferromanganese minerals: a
comprehensive review, Sep. Purif. Technol. 315 (2023) 123626, https://doi.org/
10.1016/j.seppur.2023.123626.
[9] C.-c. Nie, X.-g. Li, S.-x. Shi, et al., Eco-friendly strategy for advanced recycling
waste copper from spent lithium-ion batteries: preparation of micro-nano copper
powder, Sep. Purif. Technol. 322 (2023) 124277, https://doi.org/10.1016/j.
seppur.2023.124277.
[10] M. Wang, F. Wang, P. Wang, et al., Highly efficient and selective organic pollutants
degradation via peroxymonosulfate activation over micron-sized Co-MOF: nearly
100% singlet oxygen mechanism, Sep. Purif. Technol. 326 (2023) 124806, https://
doi.org/10.1016/j.seppur.2023.124806.
[11] G. Zhang, S. Shao, S. Li, et al., Damping or gain?-The critical roles aluminum plays
in the SRM stability, Acta Astronaut. 217 (2024) 280–292, https://doi.org/
10.1016/j.actaastro.2024.02.007.
[12] T. Li, P. Li, R. Sun, et al., Polymer-based nanocomposites in semiconductor
packaging, IET Nanodielectrics 6 (3) (2023) 147–158, https://doi.org/10.1049/
nde2.12050.
[13] L. Cordova, M. Campos, T. Tinga, Revealing the effects of powder reuse for
selective laser melting by powder characterization, JOM 71 (3) (2019) 1062–1072,
https://doi.org/10.1007/s11837-018-3305-2.
[14] D. Xu, F. Wang, S. Li, et al., Laser-induced ignition and combustion of single
micron-sized Al-Li alloy particles in high pressure Air/N2, Aerospace 10 (3) (2023)
299, https://doi.org/10.3390/aerospace10030299.
[15] Nguyen Q, Miller R A, Leissler G W. Improved Small-Particle Powders for Plasma
Spraying, 2005. https://api.semanticscholar.org/CorpusID:135575724.
[16] A.B. Spierings, M. Voegtlin, T. Bauer, et al., Powder flowability characterisation
methodology for powder-bed-based metal additive manufacturing, Prog. Addit.
Manuf. 1 (2016) 9–20, https://doi.org/10.1016/j.cep.2004.02.018.
[17] J. Zhao, J. Liu, M. Li, et al., Study on micro-energy consumption model of ultrafine
grinding coal particles, Fuel 329 (2022) 125542, https://doi.org/10.1016/j.
fuel.2022.125542.
[18] D. Mereib, U.-C.-C. Seu, M. Zakhour, et al., Fabrication of biomimetic titanium
laminated material using flakes powder metallurgy, J. Mater. Sci. 53 (10) (2018)
7857–7868, https://doi.org/10.1007/s10853-018-2086-x.
[19] Z. Shen, Q. Zhang, Hydrophobic agglomeration behavior of rhodochrosite fines Coinduced by oleic acid and shearing, Sep. Purif. Technol. 282 (2022) 120115,
https://doi.org/10.1016/j.seppur.2021.120115.
[20] P.L. Guzzo, F.B. Marinho de Barros, T.A.A. de Arruda, Effect of prolonged dry
grinding on size distribution, crystal structure and thermal decomposition of
ultrafine particles of dolostone, Powder Technol. 342 (2019) 141–148, https://doi.
org/10.1016/j.powtec.2018.09.064.
[21] Q. Cai, K. Song, P. Cai, et al., Harvesting of different microalgae through 100-μmpore-sized screen filtration assisted by cationic polyacrylamide and specific
extracellular organic matter, Sep. Purif. Technol. 280 (2022) 119918, https://doi.
org/10.1016/j.seppur.2021.119918.
[22] K. Tsamoutsoglou, V.E. Katzourakis, C.V. Chrysikopoulos, et al., Performance
evaluation and parameter estimation of the advanced primary filtration (APF)
process in wastewater treatment plants, Sep. Purif. Technol. 344 (2024) 127106,
https://doi.org/10.1016/j.seppur.2024.127106.
[23] N.H.M. dos Passos, M. Ambye-Jensen, Fractionation of proteins from alfalfa juice at
demonstration scale: effects of temperature and centrifugation strategies on
removal of green protein and recovery of the soluble white protein fraction, Sep.
Purif. Technol. 330 (2024) 125461, https://doi.org/10.1016/j.
seppur.2023.125461.
[24] L. Ji, P. Paul, B.K. Shanbhag, et al., Emerging application of hydrocyclone in
biotechnology and food processing, Sep. Purif. Technol. 309 (2023) 122992,
https://doi.org/10.1016/j.seppur.2022.122992.
[25] S.K. Tripathy, S.K. Bhoja, C. Raghu Kumar, et al., A short review on hydraulic
classification and its development in mineral industry, Powder Technol. 270
(2015) 205–220, https://doi.org/10.1016/j.powtec.2014.09.049.
[26] A.F. Concha, A.J.L. Bouso, Classification fundamentals, Fluid Mechanics
Fundamentals of Hydrocyclones and Its Applications in the Mining Industry (2021)
9–34, https://doi.org/10.1007/978-3-030-67913-2_2.
[27] Q. Zhao, D. Hou, B. Cui, et al., Development of an integrated multichannel inlet for
improved particle classification in hydrocyclones, Adv. Powder Technol. 32 (12)
(2021) 4546–4561, https://doi.org/10.1016/j.apt.2021.10.003.
[28] T. Zhang, J. Li, A. Wei, et al., Ultra-clean separation of micro-particles in lubricant
oil based on short-flow control of mini-hydrocyclone, Sep. Purif. Technol. 304
(2023) 122370, https://doi.org/10.1016/j.seppur.2022.122370.
[29] H.-P. Ma, J.-P. Li, X.-P. Hu, et al., On-site source-separation of microparticles and
reuse of coal gasification wastewater via a micro-channel separator: performance
(1) Operation parameters and cyclone structure parameters have
little influence on D50 value of the target particles at the overflow
outlet, but have a greater influence on D90 value. With the in­
crease of inlet flow rate and split ratio, D90 value of the particles
at the overflow outlet exhibited a decreasing trend.
(2) When the feed density or particle size increased, only increasing
the inlet flow rate can not improve the classification precision of
the target particles. Therefore, the cyclone tended to achieve the
goal of liquid–solid separation rather than particle classification
under these conditions.
(3) Under the optimal operating condition (inlet flow rate of 22.5 L/
min, split ratio of 9/1), the DN50 cyclone with a cone angle of 15◦
was used to perform classification of 25 μm SiO2 particles. The
classification precision was up to 95.94 % and the classification
efficiency reached 72.89 %.
(4) In the three-dimensional turbulent field inside the cyclone, coarse
particles and fine particles enter the downward outer swirl and
the upward inner swirl respectively under the action of multiforces, so as to realize the particle classification. The issue of
particles entrainment at the overflow and underflow can be
alleviated by sacrificing the particle classification precision and
efficiency, but it cannot be fundamentally solved.
CRediT authorship contribution statement
Hanyu Zhang: Investigation, Visualization, Writing – original draft.
Yifei Li: Investigation, Data curation. Qifan Yu: Formal analysis, Vali­
dation. Shijie Yan: Formal analysis. Zhenpeng Huang: Investigation,
Data curation. Lei Chen: Validation, Data curation. Yinglei Wang:
Methodology, Supervision. Bin Chen: Resources. Yuezhou Liu: Vali­
dation. Zhishan Bai: Writing – review & editing, Conceptualization.
Bingjie Wang: Writing – review & editing, Funding acquisition,
Supervision.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Acknowledgement
This work was supported by National Natural Science Foundation of
China (22478122, 22108082).
Data availability
Data will be made available on request.
References
[1] L. Li, M. He, K. Peng, et al., A novel magnetically oscillatory fluidized bed using
micron-sized magnetic particles for continuous capture of emulsified oil droplets,
Sep. Purif. Technol. 312 (2023) 123424, https://doi.org/10.1016/j.
seppur.2023.123424.
[2] H. Zhang, T. Qu, H. Wang, et al., Preparation of asymmetric Janus hollow silica
microparticle and its application on oily wastewaters, Sci. Rep. 13 (1) (2023) 4135,
https://doi.org/10.1038/s41598-023-30269-9.
[3] S. Lu, Y. Liu, J. Xu, et al., Design towards recyclable micron-sized Na2S cathode
with self-refinement mechanism, Nat. Commun. 15 (1) (2024) 9995, https://doi.
org/10.1038/s41467-024-54316-9.
11
H. Zhang et al.
Separation and Purification Technology 361 (2025) 131502
and separation mechanism, Sep. Purif. Technol. 341 (2024) 126565, https://doi.
org/10.1016/j.seppur.2024.126565.
[30] Q. Zhao, B. Cui, A. Ji, et al., Experimental and numerical study of the effect of
particle size distribution on hydrocyclone classification, Adv. Powder Technol. 35
(4) (2024) 104398, https://doi.org/10.1016/j.apt.2024.104398.
[31] E. Endres, J. Dueck, T. Neesse, Hydrocyclone classification of particles in the
micron range, Miner. Eng. 31 (2012) 42–45, https://doi.org/10.1016/j.
mineng.2011.10.013.
[32] Z. Hu, B. Wang, Z. Bai, et al., Centrifugal classification of pseudo-boehmite by
mini-hydrocyclone in continuous-carbonation preparation process, Chem. Eng.
Res. Des. 154 (2020) 203–211, https://doi.org/10.1016/j.cherd.2019.12.010.
[33] L. Abdollahzadeh, M. Habibian, R. Etezazian, et al., Study of particle’s shape
factor, inlet velocity and feed concentration on mini-hydrocyclone classification
and fishhook effect, Powder Technol. 283 (2015) 294–301, https://doi.org/
10.1016/j.powtec.2015.05.007.
[34] X. Huang, Y. Lu, G. Wu, et al., Research on the experiment of the enhancement
removal of fine sand by hydrocyclone in sewage treatment plant, Environ. Sci.
Pollut. Res. 28 (2021) 337–353, https://doi.org/10.1007/s11356-020-10493-w.
[35] D. Kelsall, A further study of the hydraulic cyclone, Chem. Eng. Sci. 2 (6) (1953)
254–272, https://doi.org/10.1016/0009-2509(53)80044-8.
[36] K. Rietema, S.I.R. Maatschappij, Performance and design of hydrocyclones—I:
general considerations, Chem. Eng. Sci. 15 (3–4) (1961) 298–302, https://doi.org/
10.1016/0009-2509(61)85033-1.
[37] L. Ni, J. Tian, C. Shen, et al., Experimental study of the separation performance of a
novel sewage hydrocyclone used in sewage source heat pump, Appl. Therm. Eng.
106 (2016) 1300–1310, https://doi.org/10.1016/j.applthermaleng.2016.06.093.
[38] J. Tian, L. Ni, T. Song, et al., An overview of operating parameters and conditions
in hydrocyclones for enhanced separations, Sep. Purif. Technol. 206 (2018)
268–285, https://doi.org/10.1016/j.seppur.2018.06.015.
[39] R. Xiang, S. Park, K. Lee, Effects of cone dimension on cyclone performance,
J. Aerosol Sci 32 (4) (2001) 549–561, https://doi.org/10.1016/S0021-8502(00)
00094-X.
[40] P. Supachart, S. Rattanaphan, P. Soison, et al., Effect of cylindrical section length
and regression model of Euler number as correlated with vortex finder and cone
lengths of a hydrocyclone, Mater. Today Proc. 5 (5) (2018) 11123–11134, https://
doi.org/10.1016/j.matpr.2018.01.031.
[41] S. Fu, Y. Qian, H. Yuan, et al., Effect of cone angles of a hydrocyclone for the
separation of waste plastics with low value of density difference, Waste Manag. 140
(2022) 183–192, https://doi.org/10.1016/j.wasman.2021.11.028.
[42] L. Ji, S. Kuang, Z. Qi, et al., Computational analysis and optimization of
hydrocyclone size to mitigate adverse effect of particle density, Sep. Purif. Technol.
174 (2017) 251–263, https://doi.org/10.1016/j.seppur.2016.10.034.
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