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Flow properties of fine powders in powder coating
Particuology 8 (2010) 19–27
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Flow properties of fine powders in powder coating Qing Huang, Hui Zhang, Jesse Zhu ∗ Particle Technology Research Centre, Department of Chemical and Biochemical Engineering, The University of Western Ontario, 1151 Richmond Street, London, Ontario, Canada N6A 5B9
a r t i c l e
i n f o
Article history: Received 27 January 2009 Accepted 8 May 2009 Keywords: Fine powders Nanoparticle Flow property Flow property characterization Powder coating
a b s t r a c t Three types of nanoparticles and their combinations were blended into a fine powder, which has been used in the powder coating industry. To study their effects on flow properties, the modified powder samples were characterized using a variety of techniques that tested the powder under different powder states ranging from dynamic to static. It was found that all three nanoparticles improved the flow properties of the powder to some degree, though the amounts of the nanoparticles needed were different depending on their physical properties. Secondly, inconsistency among these powder characterization techniques was also found. This is attributed to the different states of the powder samples during a measurement including dynamic, dynamic-static and static states. It was confirmed that characterization techniques which test the flow properties of a powder under all three states are needed to fully describe the flow properties of the powder. Finally, the effects of combinations of nanoparticles were explored, and it was found that combinations of nanoparticles can intensify, weaken or combine the effects of their component nanoparticles. The effects of nanoparticle combinations are not a simple summation of the effects of their component nanoparticles. © 2009 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.
1. Introduction Powders are used in many applications and throughout many industries such as powdered paint and pigment, ceramics, pharmaceuticals and food. Within these industries, it is often essential to characterize the flow properties of a powder to design equipment for processing such as storage, transportation, fluidization, blending and other general handling of bulk solids. In addition, within some applications such as powder coating and tablet and capsule formulation, it is crucial to monitor powder flow for product quality control. To predict flow properties of a powder, a variety of characterization techniques have been invented and developed, which have been well summarized in the reviews by Prescott and Barnum (2000) and Schwedes (2003). The most well-known powder characterization technique is probably the measurement of shear strengths under different normal stresses using a Jenike shear cell. Plotting these shear strengths against the normal stresses constructs a yield locus, from which several parameters can be generated such as angle of internal friction, yield strength at zero normal stress, principal stress and unconfined yield strength. The
∗ Corresponding author. Tel.: +1 519 661 3807; fax: +1 519 661 3498. E-mail address: [email protected] (J. Zhu).
ratio of principal stress to unconfined yield strength was employed by Jenike to categorize powders into five groups ranging from cohesive to free flowing (Jenike, 1967). Following the principle of the Jenike shear cell, different types of shear cells have been developed, such as annular shear cell, triaxial tester, Johanson indicizer and shear cell modules of FT4 Powder Rheometer. On another route, Carr (1965) proposed a series of indices that combine the results from multiple characterization techniques (angle of repose, compressibility, angle of spatula, uniformity and cohesion) into a single flowability score to classify powders into seven groups with flowability ranging from very bad to very good. These indices were later adopted by Hosokawa Micron Ltd. and implemented into their powder tester. Particularly for powder behaviors in a fluidized bed, the most popular powder classification system is the Geldart powder classification chart (Geldart, 1973). It divides powders into four groups including cohesive (C), aeratable (A), bubbly-ready (B) and spoutable (D) groups, according to their densities and sizes. However, inconsistency between different powder characterization techniques has been a source of confusion for a long time. Examples of this were available in literature (Krantz, Zhang, & Zhu, 2009; Ploof & Carson, 1994; Taylor, Ginsburg, Hickey, & Gheyas, 2000). Gradually, it was realized that powder flow behavior is so complicated that no single characterization technique is able to fully describe the flow properties of powders. Currently, it has been accepted in the powder handling and processing field that powder
1674-2001/$ – see front matter © 2009 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.
doi:10.1016/j.partic.2009.05.007
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Fig. 1. Schematic of powder coating process.
characterization techniques should match the powder application and the process equipment to be used (Krantz et al., 2009; Ploof & Carson, 1994; Prescott & Barnum, 2000; Schwedes, 2003; Taylor et al., 2000). Krantz et al. (2009) further classify powder flow characterization techniques into three groups including dynamic, dynamic-static and static according to the state of a powder sample when the characterization is carried out. And they suggested that flow property results characterized under different states are not interchangeable. To fully understand the powder flow behaviors, the flow properties have to be characterized using the techniques in all three groups. Powder coating is used to coat powdered paints directly onto a subject without using any organic solvents. In comparison with traditional liquid coating, powder coating is environmentally friendly because no volatile organic compounds (VOCs) are involved during the process. Cost is also reduced as powders are reclaimed. Fig. 1 shows a schematic of the powder coating process: First, powdered paints are discharged from a hopper of powder storage to a fluidized bed, where the powder is fluidized by air. Then, the fluidized powder is sucked out and transported to a spray gun, where the particles are electrostatically charged and sprayed onto a subject in a spray booth. At the same time, the particles which are not fixed onto the subject are recycled back to the fluidized bed. Lastly, the coated subject is moved from the booth to an oven, where particles become cross-linked and a uniform film is formed under a proper temperature. Powder coating has already been applied to coat appliances, furniture, architectural and building materials and underhood parts and primer coats in the automotive industry. In these markets, powder coating has fully or partially replaced traditional liquid coating because of the advantages mentioned above. Currently, powder size in powder coating varies mainly in a range from 30 to 60 m, leading to a thicker and rougher film than liquid coating. There has been a strong trend in recent years to reduce the powder size to improve the surface quality of the final product and further reduce the material cost (Kenny, Ueno, & Tsutsui, 1996; Nishida & Sugiyama, 1996; Takeda & Nakaya, 1999; Zhu & Zhang, 2005). Although many kinds of flow additives have been applied, it was a challenge to decrease the powder size to below 20 m (median diameter) due to their poor flow properties, leading to difficulties in the powder coating processes such as poor fluidization and uneven transportation and spray. Zhu
and Zhang (2004, 2006) developed a series of criteria during the processes of manufacturing powders, choosing and blending flow additives to control the flow properties. As a result, an innovative powder coating technology—ultrafine powder coating which uses paint powders with mean particle diameter less than 20 m, coupled with its advanced features in surface quality was ready for commercial use (Zhu & Zhang, 2005). As flow additives, nanoparticles have been widely used to adjust flow properties of fine powders for a long time, however, the mechanism is still not very clear. It has been suggested that nanoparticles act as a neutralizer of electrostatic charge (Dutta & Dullea, 1990) or nanoparticles reduce the internal friction of fine powders by rolling between them as lubricants (Hollenbach, Peleg, & Rufner, 1983; Kono, Huang, & Xi, 1989). However, the more widely accepted explanation is the reduction of the van der Waals force between two fine particles by nanoparticles. It is well known that the poor flow properties of fine powders are due to their cohesive nature resulting from their relatively larger interparticle forces (Visser, 1989). Therefore, nanoparticles, adhering onto the surface of a fine particle after being sufficiently mixed into the fine powders, decrease the van der Waals force between fine particles by either increasing their separation distance (Xu, Huang, Zhang, & Zhu, 2006) or decreasing the local radius of curvature and increasing the hardness at the contact of two fine particles (Castellanos, 2005), provided that the van der Waals force was considered as a dominant force among various interparticle forces when a powder is in a dry condition. In this study, three types of nanoparticles and their combinations were blended into a powder from the ultrafine powder coating industry. These modified powder samples were characterized using a variety of techniques that tested powder under different powder states ranging from dynamic to static. Based on the characterization results, effects of these nanoparticles on flow properties of the powder were studied. In addition, the test results from different characterization techniques were also compared due to the inconsistency reported in the literature. This study confirmed that there is no necessary relationship between any two measurements under different states. As a consequence, characterization techniques which test the flow properties of a powder under different states ranging from dynamic to static are needed to fully describe the flow properties of the powder.
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Table 1 Physical properties of the nanoparticles. Names of nanoparticles and their combinations
Component
Diameter (nm)
Density (kg/m3 )
Remark
SiO2 Al2 O3 TiO2 2(Al2 O3 )–SiO2 Al2 O3 –2(SiO2 )
SiO2 Al2 O3 TiO2 Al2 O3 :SiO2 = 2:1 Al2 O3 :SiO2 = 1:2
12 13 40 – –
2000 3270 3800 – –
Degussa Silica R972 Degussa Al2 O3 AluC – – –
2. Experiments 2.1. Experimental materials A polyurethane (PU) based powder that is used for the primer coat in the automotive industry was employed in this study. The average diameter of the particles is 19 m, measured by a Marlvern particle sizer (Malvern Mastersizer 2000). The particle size distribution was very carefully controlled during manufacturing: D10, D50 and D90 were 8, 17 and 33 m, respectively. The powder was black with an irregular shape. Three nanoparticles were chosen to adjust the flow properties of the powder according to the experience of authors in ultrafine powder coating and Zhu and Zhang’s criteria (Zhu & Zhang, 2004). Their physical properties are listed in Table 1. They were blended into the powder through Zhu and Zhang’s patented blending method to ensure uniform distribution (Zhu & Zhang, 2006). The concentration of these nanoparticles, (mass of nanoparticles)/(mass of host powder sample + mass of nanoparticles), varied in a range of 0–3.0% (w/w), which covered most application ranges of nanoparticles in powder coating. In addition, effects of combinations of nanoparticles were also explored. Al2 O3 and SiO2 were mixed in the ratios of 2:1 and 1:2 and then distributed in the PU based powder using the same blending method as those individual types of nanoparticles. The two combinations are referred to as 2(Al2 O3 )–SiO2 and Al2 O3 –2(SiO2 ) respectively in this study. 2.2. Experimental methods 2.2.1. Shear strength measurement The yield locus of the powder was established using the shear cell modules of an FT4 Powder Rheometer manufactured by Freeman Technology Ltd. A schematic of the FT4 Powder Rheometer shear cell modules is illustrated in Fig. 2. In a typical test, a powder sample was first conditioned to arrange it in a homogenized state by rotating a blade through the powder sample in a 50 mm diameter cylindrical vessel for four cycles, as shown in Fig. 2(a). Then, the vessel was split to level the powder volume at 85 mL. Following
Fig. 2. Schematic of shearing strength measurement using the FT4 Powder Rheometer.
a standard process established by Jenike (ASTM Standard D612806, 2006), as shown in Fig. 2(b), the powder was then compressed under a specified normal stress of 9 kPa by using a vented piston that allows air to escape away from the powder. Afterwards, the vented piston was replaced with a shear cell as shown in Fig. 2(c), and the powder was pre-sheared under the same normal stress by slowly rotating the shear cell to achieve a critical state so that sample preparation for each test was consistent. Normal stress was then reduced to 7 kPa, the shear strength at yield was recorded as one point on the yield locus. For the remaining measurements of shear strength under different normal stresses, a similar procedure was repeated on the same powder sample: the powder was first pre-sheared to make it reach the same critical state and then the yield shear strengths were measured at normal stresses of 6, 5, 4 and 3 kPa, respectively. This was slightly different from Jenike’s method, in which, a new sample needed to be prepared every time for measuring the shear strength under another normal stress. Clearly, this method decreased human operation of filling and packing new powder samples, and as a result, experimental errors were reduced and reproducibility was improved, as the bad reproducibility of shear strength measurements was partially attributed to human interference. For each sample, 2–3 measurements were repeated and the average was used to construct the yield locus to ensure accuracy. The yield locus was then extrapolated to 0 kPa normal stress, and this extrapolated shear strength was employed to characterize powder flow properties and is referred to as cohesion in this study. It is essentially the shear strength of the powder yield at zero normal stress estimated by extrapolation. 2.2.2. Angle of repose (AOR) Angle of repose is the largest angle at which powders can pile up. Generally, it is related to the powders’ cohesiveness and internal friction and it is widely used to characterize flow properties of powders. Measurement of the angle of repose was carried out using a PT-N Hosokawa Powder Characteristic Tester, following the standardized testing procedures of ASTM D6369-08 (ASTM Standard D6369-08, 1999). For each test, a powder sample was first loaded on a screen mounted with a vibrator. The particles were controlled to fall down through the funnel which was mounted under the screen in a slow and consistent rate by adjusting the vibration intensity. These particles would pile up on a plate which was aligned with the funnel. When the whole plate was covered with the particles, the largest angle between the powder pile surface and the horizontal plane was measured as the angle of repose. For each powder sample, 3–5 measurements were repeated and the average was used. 2.2.3. Fluidization measurement Fluidization of the powder samples was measured in a fluidized bed system as schematically shown in Fig. 3. The fluidized bed column was made of Plexiglass which was 1 foot tall with a 2-inch I.D. A porous plate was placed underneath as a gas distributor. Two pressure probes connected with a differential transducer were inserted into two ends of the column to measure the pressure dif-
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Fig. 3. Schematic of the fluidized bed.
ference. For a test, approximately 100 g of a powder was first loaded into the column and fluidized at a gas velocity of 4 cm/s until the powder reached a steady state so that large agglomerates formed during storage were broken and the powders’ “memory” for other former processes was erased as well. Then the gas flowrate was gradually reduced to the set gas velocities, and for each gas velocity, the pressure drop through the bed and the bed height were measured simultaneously when the powder was steady. 3. Results 3.1. Cohesion Fig. 4 shows cohesion values of the powder samples blended with nanoparticles Al2 O3 , SiO2 and TiO2 with respect to the concentration of nanoparticles. Overall, for all powder samples, with increasing concentrations of nanoparticles, cohesion exhibits general declining trends reflecting the improvement of the powder’s flow properties by these nanoparticles. However, for powder samples with nanoparticles SiO2 and Al2 O3 , their cohesion values increased slightly after reaching a minimum at around 1.0–1.2% (w/w). In the experience of the authors in ultrafine powder coat-
Fig. 4. Cohesion of powder samples blended with nanoparticles of Al2 O3 , SiO2 and TiO2 .
Fig. 5. Angle of repose of powder samples blended with nanoparticles of Al2 O3 , SiO2 and TiO2 .
ing application, cohesion needs to be lower than 0.4 kPa to give the powder good performance (Zhang & Zhu, 2007). In this study, a practical nanoparticle concentration is utilized to describe the concentration of nanoparticles added to powders to improve flow properties to an acceptable level used in powder coating applications. Consequently, the practical nanoparticle concentrations for SiO2 and Al2 O3 in the polyurethane powder are 0.3–3.0% (w/w) and 0.5–3.0% (w/w), respectively. It was also noticed that for the powder blended with nanoparticle TiO2 , the cohesion value decreased at a lower rate. As a consequence, their powder performance reached the acceptable level only when the concentration of TiO2 reached more than around 1.2% (w/w). The possible reasons that cause the different practical nanoparticle concentrations of the three nanoparticles will be discussed in Section 4.1. 3.2. Angle of repose (AOR) Fig. 5 shows the angle of repose values of the powder samples blended with the three nanoparticles with respect to their nanoparticle concentration. It is clear that all three nanoparticles in this study effectively decreased the polyurethane powder’s angle of repose and thereby improved its flow. However, there were some differences lying among the three nanoparticles. For the powder samples blended with Al2 O3 , as the concentration of Al2 O3 increased, the AOR decreased from 52◦ , then reached a minimum of about 34◦ at a concentration of 0.7–1.0% (w/w) of Al2 O3 , and increased slightly afterwards. For the powder samples blended with SiO2 , the AOR decreased to about 41◦ at a concentration of 1.0% (w/w) of SiO2 and then the AOR varied around 41◦ . For powder samples blended with TiO2 , similar to their cohesion values, their angle of repose values also showed a slower decrease rate with respect to the TiO2 concentration. The AOR decreased below 42◦ only when the nanoparticle concentration of TiO2 increased up to 1.2% (w/w). According to the authors’ experience, the angle of repose needs to be lower than 42◦ so that agglomeration and the formation of large clumps in the final coating will not occur during powder coating application. In this case, the practical nanoparticle concentrations for Al2 O3 , SiO2 and TiO2 are 0.3–3.0% (w/w), 0.5–3.0% (w/w) and 1.2–3.0% (w/w), respectively. Moreover, it seems that nanoparticle Al2 O3 improved the powder’s flowability the most by reducing the AORs to their lowest values.
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Fig. 6. Fluidization process of the polyurethane powder blended with nanoparticle TiO2 .
3.3. Fluidization measurement Fig. 6 shows a typical example of the fluidization of the polyurethane powder blended with nanoparticle TiO2 . Under gas velocities of 0–0.6 cm/s, the powder was not fluidized and the corresponding bed expansion ratios and pressure drops were minimal. While the gas velocity varied from 0.8 to 2.0 cm/s, the powder bed appeared as a smooth, fluid-like surface and bed height increased significantly with increasing gas velocity. As a result, both the powder bed surface and bed expansion ratio indicated that the powder became fluidized at a velocity in the range of 0.6–0.8 cm/s. This velocity corresponds to the point where the bed expansion ratio has its largest increase rate with respect to gas velocity. Accordingly, a new powder characteristic velocity was defined as the gas velocity at which the powder bed expansion has its largest increasing rate with respect to gas velocity—breakthrough fluidization gas velocity (Ubf ). As a powder fluidization characteristic gas velocity, Ubf illustrates how easily a fine powder becomes fluidized. In other words, it is difficult for a powder with a larger Ubf to become fluidized and therefore the powder has poor fluidizability. It needs to be pointed out that the traditional powder fluidization characteristic gas velocity, minimum fluidization gas velocity (Umf ), at which the pressure drop of the powder bed starts to equal its weight, showed a postponed powder initial fluidization point, as shown in the figure. Therefore, instead of Umf , Ubf was employed to characterize the fluidization of powders in this study. A detailed comparison and discussion about Umf and Ubf will be addressed in another work (Huang, Zhu, & Zhang, submitted for publication). Fig. 7 shows Ubf values of the powder samples with respect to the nanoparticle concentration. The powders with 0–0.1% (w/w) of all three nanoparticles and 0.3–0.5% (w/w) of TiO2 were too cohesive to be fluidized. Therefore, their Ubf values were not available. According to the authors’ experience on fluidization quality characterization, Ubf generally needs to be lower than 0.6 cm/s to
give a good fluidization performance for powder coatings (Zhang & Zhu, 2007). In addition, in a practical powder coating process, the gas velocity is generally controlled to be lower than 1 cm/s. Consequently, the practical nanoparticle concentrations for the polyurethane powder blended with Al2 O3 , SiO2 and TiO2 are 0.3–3.0% (w/w), 0.5–3.0% (w/w) and 1.2–3.0% (w/w), respectively. Moreover, it seems nanoparticle Al2 O3 improved the powder’s fluidization quality most by reducing the Ubf values to their lowest values. In this study, the bed expansion ratio (BER) at 0.6 cm/s was also taken as an index of fluidization quality. As mentioned above, for
Fig. 7. Breakthrough gas velocity of powder samples blended with nanoparticles of Al2 O3 , SiO2 and TiO2 .
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Fig. 8. BER at 0.6 cm/s of powder samples blended with nanoparticles of Al2 O3 , SiO2 and TiO2 .
powder coatings, powders with a good fluidization quality become fluidized at gas velocities smaller than 0.6 cm/s and generally show a significant bed expansion ratio that is larger than 1.5. On the other hand, powders with poor fluidization quality generally remain unfluidized at 0.6 cm/s, and as a result, the bed expansion ratio is minimal. Fig. 8 shows the BER at 0.6 cm/s of the powder samples with respect to the nanoparticle concentration. It is clear that powders blended with more than 0.3% (w/w) of Al2 O3 or more than 0.5% (w/w) of SiO2 showed significant BER values which are over 1.8, while powders needed more than 1.2% (w/w) TiO2 to have BER values larger than 1.7. The practical nanoparticle concentration results are consistent with those concluded from Ubf . However, in contrast, the powders blended with Al2 O3 and SiO2 exhibited nearly identical BERs at 0.6 cm/s while the Ubf of the powders with Al2 O3 showed smaller values. 4. Discussion
Fig. 9. SEM images of powder samples blended with 1% (w/w) nanoparticles of (a) Al2 O3 and (b) TiO2 .
4.1. Practical nanoparticle concentrations It is well known that nanoparticles improve the flow properties of fine powders because the interparticle forces which cause fine powders to become cohesive are reduced due to the existence of nanoparticles stuck to the surface, as shown in Fig. 9. Castellanos (2005) measured the interparticle forces between two styrenebutadiene based xerographic toner particles of 12.7 m coated with 0.05% and 0.4% (w/w) of silica nanoparticles (Aerosil R812, from Degussa) by an atomic force microscope (AFM). It was found that the interparticle forces of the powder with 0.4% (w/w) of silica were smaller than those with 0.05% (w/w) of silica. His work suggested the difference in interparticle forces is a result of different contact between two toner particles. 0.05% (w/w) of silica only covered 2.77% of surface of the toner particle, and as a result, the contact still remained as toner particle to toner particle which is polymer to polymer contact. However, 0.4% (w/w) of silica covered about 22.1% of toner particle surface, which was sufficient to convert the polymer to polymer contact to silica to silica contact. The silica to silica contact decreased the local radius of curvature and increased the hardness, both leading to decreasing the interparticle forces. In this study, shear strength, angle of repose and fluidization tests showed the three nanoparticles all improved the flow prop-
erties of the powder effectively. However, relatively more TiO2 (1.2%, w/w) was needed to achieve the improvement made by relatively less Al2 O3 and SiO2 , around 0.3–0.5% (w/w). This is probably attributed to the different physical properties of these nanoparticles, leading to different surface coverage ratios on a polyurethane particle. Fig. 9 shows a comparison of scanning electron microscope (SEM) images of a polyurethane particle blended with 1% (w/w) Al2 O3 and TiO2 . The SEM image of the particle with 1% (w/w) of SiO2 is not illustrated in this paper because it looks very similar to the image of the particle with Al2 O3 . As shown in the images, nanoparticles formed small agglomerates varying from 50 to 300 nm stuck onto the surface of the particle. It is clear that the surface coverage ratio by 1% (w/w) of TiO2 is much less than the surface coverage ratio by 1% (w/w) of Al2 O3 . However, it is difficult to quantify the surface coverage ratio from SEM images. The following process is employed to theoretically approximate the surface coverage ratio of nanoparticles over a host particle. Specific surface area of host particles (Sh , m2 /kg) is Sh =
6 , h dh
(1)
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Table 2 Surface coverage ratio vs. practical nanoparticle concentration. Nanoparticle Al2 O3 SiO2 TiO2
Practical nanoparticle concentration 0.5–3.0% (w/w) 0.5–3.0% (w/w) 1.2–3.0% (w/w)
28.8–173% 41.8–251% 23.8–59.5%
where h and dh are particle density and average diameter of the host particle. In this study, h and dh are 1570 kg/m3 and 20 × 10−6 m, respectively. The projected area of nanoparticle agglomerates on host particles (Sa , m2 /kg) is Sa =
3 , 2a da
(2)
where a and da are density and average diameter of the nanoparticle agglomerates. In this study, da was estimated by manually measuring the diameter of agglomerates on SEM images. For each nanoparticle, at least 100 measurements were made and then their average was taken. The da of Al2 O3 , SiO2 and TiO2 was 80, 90 and 200 nm, respectively. The solid fraction of agglomerates may vary from 0.2 to 0.64 depending on processing procedures and applied consolidation pressure. In this case, the nanoparticle agglomerates were formed through a milling process for which the transient consolidation pressures may be high. Therefore, a solid volume fraction of 0.5 for the agglomerates was used. a was estimated to be half of the particle density. As a result, the surface coverage ratio of nanoparticle agglomerates to host particles is Rsc =
Sa · Ma 3/(2a da ) Ma d Ma = = h h · , · Sh · Mh 4a da Mh 6/(h dh ) Mh
Surface coverage ratio
(3)
where Ma and Mh are the mass of agglomerates and host particles, respectively. Substituting the physical properties of the host particle and nanoparticles, the surface coverage ratios of 1% (w/w) Al2 O3 , SiO2 and TiO2 to polyurethane particles are 84%, 58% and 19.8%. This is consistent with what SEM images have shown and confirms the effects of physical properties of nanoparticles on the surface coverage ratio on a host particle. When examining the practical nanoparticle concentration found in this study with the surface coverage ratio theory, it was found that for all three nanoparticles, the surface coverages corresponding to the lower ends of the practical nanoparticle concentrations were all higher than 20%, as shown in Table 2. This confirmed the Castellanos’s model that when nanoparticles cover more than 20% of the surface of a host particle, the contact between two host particles become nanoparticle to nanoparticle contact, as a result, the local radius of curvature of the host particle decreased and the hardness increased, both leading to decreasing the interparticle forces. It was also noticed that 3.0% (w/w) of Al2 O3 and SiO2 are more than enough to cover 100% of the polyurethane particle according to the theoretical calculation results in Table 2. However, none of these completely covered polyurethane particles were observed under the SEM. The extra nanoparticles may exist among polyurethane particles in the form of larger agglomerates, resulting in decreasing flow properties. Therefore, it can be explained that the cohesion values of the powder samples with more than 1.2% (w/w) of Al2 O3 and SiO2 increased as shown in Fig. 4. 4.2. Powder flow property characterization techniques When comparing all the flow property characterization techniques employed in this study, it was found that each technique
agrees with the theory in terms of determining the practical nanoparticle concentrations. This is a promising result that helps to confirm the validity of each technique used. However, it was also found that they are conflicting with each other when greater details regarding improvement of flow properties are required, in terms of the different characterization methods. Both Ubf and angle of repose measurements showed nanoparticle Al2 O3 has the largest impact on powders’ flow properties by reducing Ubf and AOR of the powder samples to their lowest values, while the cohesion and BER values of the powders blended with Al2 O3 and SiO2 were similar for the powder used in this study. Similar inconsistencies among different flow property characterization methods have also been found and reported in other studies (Krantz et al., 2009; Ploof & Carson, 1994; Taylor et al., 2000). It was suggested that characterization techniques should match the application and the process equipment to be used. Krantz et al. (2009) further proposed to classify powder flow property characterizing techniques into three groups including dynamic, dynamic–static and static according to the state of a powder sample when it is measured. Flow property results characterized under different states are not interchangeable. The cohesion measurement in this study was classified into the static group because shear strengths were measured after the powder sample was pre-consolidated to a critical state and the powder sample was under normal stress during the whole measurement process. It is clear that BER at 0.6 cm/s reflects the flow properties of powders under a dynamic state because of the interaction between powders and gas in the fluidized bed. Ubf describes the point at which powder transitions from fluidized (dynamic state) to unfluidized (static state). AOR characterizes the largest piled up angle which is essentially resulting from the momentum balance between the falling down motion of a particle and resistance from the plate and other particles. As a result, Ubf and AOR characterize the flow properties of powders in the dynamic-static state. Accordingly, the results presented in this study can be explained: AOR and Ubf show nanoparticle Al2 O3 improves the flow properties of the polyurethane powder under dynamic-static state while cohesion and BER indicate similar effects of nanoparticles Al2 O3 and SiO2 on improving the flow properties of the powder under both static and dynamic states. 4.3. Effects of combinations of nanoparticles Figs. 10–13 show the cohesion, AOR, Ubf and BER values of the powders blended with the combinations of nanoparticles (2(Al2 O3 )–SiO2 and Al2 O3 –2(SiO2 )) as well as the results of the powders mixed with individual nanoparticles of Al2 O3 and SiO2 . For cohesion as shown in Fig. 10, at lower nanoparticle concentrations, it seems that the powder samples with combinations had better flow properties than those blended with individual Al2 O3 and SiO2 ; however, when the nanoparticle concentrations increased to over 1.2% (w/w), an opposite effect was found, that is, the powder samples with the combinations illustrated worse flow than those with individual nanoparticles. AOR (Fig. 11) and Ubf (Fig. 12) showed the powder samples with combinations had intermediate effects between Al2 O3 and SiO2 at lower concentrations while a higher concentration (>0.7%, w/w) of the combinations was much
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Fig. 10. Comparison of cohesion of powder samples blended with individual nanoparticles and combinations.
Fig. 12. Comparison of breakthrough fluidization gas velocity (Ubf ) of powder samples blended with individual nanoparticles and combinations.
less effective at improving the flow properties of the polyurethane powder than individual Al2 O3 and SiO2 . BER at 0.6 cm/s (Fig. 13) indicated the combinations were less effective than individual types of nanoparticles along the whole experimental range of nanoparticle concentrations. However, lower concentrations of the combinations still gave the powders an acceptable flow performance while the powder samples with higher concentrations (>1.2%, w/w) could not be fluidized at 0.6 cm/s. All of these characterization techniques indicated that the effects of nanoparticle combinations are not a simple summing up of the effects of their component nanoparticles. The effects of individual types of nanoparticles can be further combined, intensified or weakened by combining them together. On the other hand, it seems that the improvement of flow properties for the powder blended with the combinations depends on their concentration. In one aspect, the improvement of flow properties was limited for the powder blended with higher concentrations of the combinations. Generally these concentrations are excluded from the practical nanoparticle concentration ranges. For instance, according to the
Ubf results, the practical nanoparticle concentrations for combinations 2(Al2 O3 )–SiO2 and Al2 O3 –2(SiO2 ) are around 0.3–1.0% (w/w) only, and higher concentrations (>1.2%, w/w) are not included. This is probably attributed to the fact that nanoparticles are polarized, and as a result tend to form agglomerates with a chain-like structure. Al2 O3 and SiO2 might be oppositely polarized and it is possible for them to form large agglomerates that weaken the improvement of flow properties, when their concentrations are high. In another aspect, although the powder blended with lower concentrations of the nanoparticle combinations showed intensified, weakened or combined improvement of the flow properties in comparison with the powder samples mixed with individual types of nanoparticles according to various powder characterization techniques, their flow properties are far better than the acceptable level for powder coating application. Therefore, with a lower concentration, the combinations of nanoparticles still can be applied in practice. In particular, if the amount of a certain nanoparticle is constrained due to some reasons other than flow properties (i.e. too much titanium oxide leads to coating with a white color, which is not desired
Fig. 11. Comparison of angle of repose (AOR) of powder samples blended with individual nanoparticles and combinations.
Fig. 13. Comparison of bed expansion ratio (BER) at 0.6 cm/s of powder samples blended with individual nanoparticles and combinations.
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for a clear coat), while this largest acceptable concentration of this nanoparticle is still insufficient for the practical concentration of this nanoparticle, it is an alternative to combine this nanoparticle with another nanoparticle to make a combination that could greatly improve the flow properties. 5. Conclusions Polyurethane powder from the powder coating industry was blended with three types of nanoparticles to modify its flow properties. A term of “practical nanoparticle concentration” was used to identify a range of nanoparticle concentrations, in which the nanoparticles improve the flow properties of fine powders to an acceptable level for industrial applications. According to the experience of the authors in powder coating, a variety of powder characterization techniques indicated that the practical nanoparticle concentrations for the polyurethane powder are: 0.5–3.0% (w/w) of Al2 O3 , 0.5–3.0% (w/w) of SiO2 and 1.2–3.0% (w/w) of TiO2 . The relatively higher concentration needed for nanoparticle TiO2 is probably attributed to its larger particle size leading to larger agglomerates and consequently less surface coverage ratio of the agglomerates. The effects of combinations of Al2 O3 and SiO2 were also studied. It was found that the effects of nanoparticle combinations are not a simple summing up of the effects of their component nanoparticles. On the one hand, a higher concentration of the combinations reduces the improvement of the flow properties. This could be attributed to the formation of large agglomerates by the nanoparticles with higher concentrations. On the other hand, combinations of the nanoparticles at lower concentrations still effectively improve the flow properties of the powder, so they can still be used for practical applications. Several characterization techniques were employed to illustrate the flow properties of the powder samples modified by the nanoparticles. They gave consistent results of powder flow properties in terms of determining the practical nanoparticle concentrations. However, inconsistency was found among the four characterization methods in terms of illustrating the improvement of flow properties. This is because the various states of the powder during a measurement, including dynamic, dynamic-static and static states. Because there is no necessary relationship between any two measurements under different states, characterization techniques which test the flow properties of a powder under all three states are needed to fully describe the flow properties of the powder.
27
References ASTM Standard D6128-06. (2006). Standard test method for shear testing of bulk solids using the jenike shear cell. West Conshohocken, PA, USA: ASTM International. ASTM Standard D6369-08. (1999). Standard test method for bulk solids characterization by Carr indices. West Conshohocken, PA, USA: ASTM International. Carr, R. L. (1965). Evaluating flow properties of solids. Chemical Engineering, 72(2), 163–168. Castellanos, A. (2005). The relationship between attractive interparticle forces and bulk behaviour in dry and uncharged fine powders. Advances in Physics, 54, 263–376. Dutta, A., & Dullea, L. V. (1990). A comparative evaluation of negatively and positively charged submicron particles as flow conditioners for a cohesive powder. AIChE Symposium Series, 86, 26–40. Geldart, D. (1973). Types of gas fluidization. Powder Technology, 7(5), 285– 292. Hollenbach, A. M., Peleg, M., & Rufner, R. (1983). Interparticle surface affinity and the bulk properties of conditioned powders. Powder Technology, 35(1), 51– 62. Huang, Q., Zhu, J., & Zhang, H. (submitted for publication). Characterization of gas solid fluidization properties of fine powders—A new characteristic fluidization gas velocity. Jenike, A. W. (1967). Quantitative design of mass-flow bins. Powder Technology, 1(4), 237–244. Kenny, J. C., Ueno, T., & Tsutsui, K. (1996). Analytical approach for high quality appearance powder coatings. Journal of Coatings Technology, 68(855), 35– 43. Kono, H. O., Huang, C. C., & Xi, M. (1989). The effect of flow conditioners on the tensile strength of cohesive powder structures. AIChE Symposium Series: Fluidization and Fluid Particle Systems: Fundamentals and Applications, 85(270), 44– 48. Krantz, M., Zhang, H., & Zhu, J. (2009). A new approach to powder characterization: Static and dynamic testing. Powder Technology, 194, 239–249. Nishida, K., & Sugiyama, T. (1996). Powder coatings. U.S. Patent No. 5,567,521. Ploof, D. A., & Carson, J. W. (1994). Quality control tester to measure relative flowability of powders. Bulk Solids Handling, 14(1), 127–132. Prescott, J. K., & Barnum, R. A. (2000). On powder flowability. Pharmaceutical Technology, 24(10), 60. 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 236. Schwedes, J. (2003). Review on testers for measuring flow properties of bulk solids. Granular Matter, 5(1), 1–43. Takeda, H., & Nakaya, I. (1999). Powder coating composition and application method therefor. U.S. Patent No. 5,948,866. Taylor, M. K., Ginsburg, J., Hickey, A. J., & Gheyas, F. (2000). Composite method to quantify powder flow as a screening method in early tablet or capsule formulation development. AAPS PharmSciTech, 1(3), 20–30. Visser, J. (1989). Van der Waals and other cohesive forces affecting powder fluidization. Powder Technology, 58(1), 1–10. Xu, C., Huang, Q., Zhang, H., & Zhu, J. (2006). Improving fluidizability of cohesive particles by surface coating with flow conditioners. In The Fifth World Congress on Particle Technology Orlando, FL, United States. Zhang, H., & Zhu, J. (2007). Internal report to a major automobile manufacturer. Zhu, J., & Zhang, H. (2004). Fluidization additives to fine powders. U.S. Patent No. 6,833,185. Zhu, J., & Zhang, H. (2005). Ultrafine powder coatings: An innovation. Powder Coating, 16(7), 39–47. Zhu, J., & Zhang, H. (2006). Method and apparatus for uniformly dispensing additive particles in fine powders. U.S. Patent, Applied, November 20, 2006.
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Particuology journal homepage: www.elsevier.com/locate/partic
Flow properties of fine powders in powder coating Qing Huang, Hui Zhang, Jesse Zhu ∗ Particle Technology Research Centre, Department of Chemical and Biochemical Engineering, The University of Western Ontario, 1151 Richmond Street, London, Ontario, Canada N6A 5B9
a r t i c l e
i n f o
Article history: Received 27 January 2009 Accepted 8 May 2009 Keywords: Fine powders Nanoparticle Flow property Flow property characterization Powder coating
a b s t r a c t Three types of nanoparticles and their combinations were blended into a fine powder, which has been used in the powder coating industry. To study their effects on flow properties, the modified powder samples were characterized using a variety of techniques that tested the powder under different powder states ranging from dynamic to static. It was found that all three nanoparticles improved the flow properties of the powder to some degree, though the amounts of the nanoparticles needed were different depending on their physical properties. Secondly, inconsistency among these powder characterization techniques was also found. This is attributed to the different states of the powder samples during a measurement including dynamic, dynamic-static and static states. It was confirmed that characterization techniques which test the flow properties of a powder under all three states are needed to fully describe the flow properties of the powder. Finally, the effects of combinations of nanoparticles were explored, and it was found that combinations of nanoparticles can intensify, weaken or combine the effects of their component nanoparticles. The effects of nanoparticle combinations are not a simple summation of the effects of their component nanoparticles. © 2009 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.
1. Introduction Powders are used in many applications and throughout many industries such as powdered paint and pigment, ceramics, pharmaceuticals and food. Within these industries, it is often essential to characterize the flow properties of a powder to design equipment for processing such as storage, transportation, fluidization, blending and other general handling of bulk solids. In addition, within some applications such as powder coating and tablet and capsule formulation, it is crucial to monitor powder flow for product quality control. To predict flow properties of a powder, a variety of characterization techniques have been invented and developed, which have been well summarized in the reviews by Prescott and Barnum (2000) and Schwedes (2003). The most well-known powder characterization technique is probably the measurement of shear strengths under different normal stresses using a Jenike shear cell. Plotting these shear strengths against the normal stresses constructs a yield locus, from which several parameters can be generated such as angle of internal friction, yield strength at zero normal stress, principal stress and unconfined yield strength. The
∗ Corresponding author. Tel.: +1 519 661 3807; fax: +1 519 661 3498. E-mail address: [email protected] (J. Zhu).
ratio of principal stress to unconfined yield strength was employed by Jenike to categorize powders into five groups ranging from cohesive to free flowing (Jenike, 1967). Following the principle of the Jenike shear cell, different types of shear cells have been developed, such as annular shear cell, triaxial tester, Johanson indicizer and shear cell modules of FT4 Powder Rheometer. On another route, Carr (1965) proposed a series of indices that combine the results from multiple characterization techniques (angle of repose, compressibility, angle of spatula, uniformity and cohesion) into a single flowability score to classify powders into seven groups with flowability ranging from very bad to very good. These indices were later adopted by Hosokawa Micron Ltd. and implemented into their powder tester. Particularly for powder behaviors in a fluidized bed, the most popular powder classification system is the Geldart powder classification chart (Geldart, 1973). It divides powders into four groups including cohesive (C), aeratable (A), bubbly-ready (B) and spoutable (D) groups, according to their densities and sizes. However, inconsistency between different powder characterization techniques has been a source of confusion for a long time. Examples of this were available in literature (Krantz, Zhang, & Zhu, 2009; Ploof & Carson, 1994; Taylor, Ginsburg, Hickey, & Gheyas, 2000). Gradually, it was realized that powder flow behavior is so complicated that no single characterization technique is able to fully describe the flow properties of powders. Currently, it has been accepted in the powder handling and processing field that powder
1674-2001/$ – see front matter © 2009 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.
doi:10.1016/j.partic.2009.05.007
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Fig. 1. Schematic of powder coating process.
characterization techniques should match the powder application and the process equipment to be used (Krantz et al., 2009; Ploof & Carson, 1994; Prescott & Barnum, 2000; Schwedes, 2003; Taylor et al., 2000). Krantz et al. (2009) further classify powder flow characterization techniques into three groups including dynamic, dynamic-static and static according to the state of a powder sample when the characterization is carried out. And they suggested that flow property results characterized under different states are not interchangeable. To fully understand the powder flow behaviors, the flow properties have to be characterized using the techniques in all three groups. Powder coating is used to coat powdered paints directly onto a subject without using any organic solvents. In comparison with traditional liquid coating, powder coating is environmentally friendly because no volatile organic compounds (VOCs) are involved during the process. Cost is also reduced as powders are reclaimed. Fig. 1 shows a schematic of the powder coating process: First, powdered paints are discharged from a hopper of powder storage to a fluidized bed, where the powder is fluidized by air. Then, the fluidized powder is sucked out and transported to a spray gun, where the particles are electrostatically charged and sprayed onto a subject in a spray booth. At the same time, the particles which are not fixed onto the subject are recycled back to the fluidized bed. Lastly, the coated subject is moved from the booth to an oven, where particles become cross-linked and a uniform film is formed under a proper temperature. Powder coating has already been applied to coat appliances, furniture, architectural and building materials and underhood parts and primer coats in the automotive industry. In these markets, powder coating has fully or partially replaced traditional liquid coating because of the advantages mentioned above. Currently, powder size in powder coating varies mainly in a range from 30 to 60 m, leading to a thicker and rougher film than liquid coating. There has been a strong trend in recent years to reduce the powder size to improve the surface quality of the final product and further reduce the material cost (Kenny, Ueno, & Tsutsui, 1996; Nishida & Sugiyama, 1996; Takeda & Nakaya, 1999; Zhu & Zhang, 2005). Although many kinds of flow additives have been applied, it was a challenge to decrease the powder size to below 20 m (median diameter) due to their poor flow properties, leading to difficulties in the powder coating processes such as poor fluidization and uneven transportation and spray. Zhu
and Zhang (2004, 2006) developed a series of criteria during the processes of manufacturing powders, choosing and blending flow additives to control the flow properties. As a result, an innovative powder coating technology—ultrafine powder coating which uses paint powders with mean particle diameter less than 20 m, coupled with its advanced features in surface quality was ready for commercial use (Zhu & Zhang, 2005). As flow additives, nanoparticles have been widely used to adjust flow properties of fine powders for a long time, however, the mechanism is still not very clear. It has been suggested that nanoparticles act as a neutralizer of electrostatic charge (Dutta & Dullea, 1990) or nanoparticles reduce the internal friction of fine powders by rolling between them as lubricants (Hollenbach, Peleg, & Rufner, 1983; Kono, Huang, & Xi, 1989). However, the more widely accepted explanation is the reduction of the van der Waals force between two fine particles by nanoparticles. It is well known that the poor flow properties of fine powders are due to their cohesive nature resulting from their relatively larger interparticle forces (Visser, 1989). Therefore, nanoparticles, adhering onto the surface of a fine particle after being sufficiently mixed into the fine powders, decrease the van der Waals force between fine particles by either increasing their separation distance (Xu, Huang, Zhang, & Zhu, 2006) or decreasing the local radius of curvature and increasing the hardness at the contact of two fine particles (Castellanos, 2005), provided that the van der Waals force was considered as a dominant force among various interparticle forces when a powder is in a dry condition. In this study, three types of nanoparticles and their combinations were blended into a powder from the ultrafine powder coating industry. These modified powder samples were characterized using a variety of techniques that tested powder under different powder states ranging from dynamic to static. Based on the characterization results, effects of these nanoparticles on flow properties of the powder were studied. In addition, the test results from different characterization techniques were also compared due to the inconsistency reported in the literature. This study confirmed that there is no necessary relationship between any two measurements under different states. As a consequence, characterization techniques which test the flow properties of a powder under different states ranging from dynamic to static are needed to fully describe the flow properties of the powder.
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Table 1 Physical properties of the nanoparticles. Names of nanoparticles and their combinations
Component
Diameter (nm)
Density (kg/m3 )
Remark
SiO2 Al2 O3 TiO2 2(Al2 O3 )–SiO2 Al2 O3 –2(SiO2 )
SiO2 Al2 O3 TiO2 Al2 O3 :SiO2 = 2:1 Al2 O3 :SiO2 = 1:2
12 13 40 – –
2000 3270 3800 – –
Degussa Silica R972 Degussa Al2 O3 AluC – – –
2. Experiments 2.1. Experimental materials A polyurethane (PU) based powder that is used for the primer coat in the automotive industry was employed in this study. The average diameter of the particles is 19 m, measured by a Marlvern particle sizer (Malvern Mastersizer 2000). The particle size distribution was very carefully controlled during manufacturing: D10, D50 and D90 were 8, 17 and 33 m, respectively. The powder was black with an irregular shape. Three nanoparticles were chosen to adjust the flow properties of the powder according to the experience of authors in ultrafine powder coating and Zhu and Zhang’s criteria (Zhu & Zhang, 2004). Their physical properties are listed in Table 1. They were blended into the powder through Zhu and Zhang’s patented blending method to ensure uniform distribution (Zhu & Zhang, 2006). The concentration of these nanoparticles, (mass of nanoparticles)/(mass of host powder sample + mass of nanoparticles), varied in a range of 0–3.0% (w/w), which covered most application ranges of nanoparticles in powder coating. In addition, effects of combinations of nanoparticles were also explored. Al2 O3 and SiO2 were mixed in the ratios of 2:1 and 1:2 and then distributed in the PU based powder using the same blending method as those individual types of nanoparticles. The two combinations are referred to as 2(Al2 O3 )–SiO2 and Al2 O3 –2(SiO2 ) respectively in this study. 2.2. Experimental methods 2.2.1. Shear strength measurement The yield locus of the powder was established using the shear cell modules of an FT4 Powder Rheometer manufactured by Freeman Technology Ltd. A schematic of the FT4 Powder Rheometer shear cell modules is illustrated in Fig. 2. In a typical test, a powder sample was first conditioned to arrange it in a homogenized state by rotating a blade through the powder sample in a 50 mm diameter cylindrical vessel for four cycles, as shown in Fig. 2(a). Then, the vessel was split to level the powder volume at 85 mL. Following
Fig. 2. Schematic of shearing strength measurement using the FT4 Powder Rheometer.
a standard process established by Jenike (ASTM Standard D612806, 2006), as shown in Fig. 2(b), the powder was then compressed under a specified normal stress of 9 kPa by using a vented piston that allows air to escape away from the powder. Afterwards, the vented piston was replaced with a shear cell as shown in Fig. 2(c), and the powder was pre-sheared under the same normal stress by slowly rotating the shear cell to achieve a critical state so that sample preparation for each test was consistent. Normal stress was then reduced to 7 kPa, the shear strength at yield was recorded as one point on the yield locus. For the remaining measurements of shear strength under different normal stresses, a similar procedure was repeated on the same powder sample: the powder was first pre-sheared to make it reach the same critical state and then the yield shear strengths were measured at normal stresses of 6, 5, 4 and 3 kPa, respectively. This was slightly different from Jenike’s method, in which, a new sample needed to be prepared every time for measuring the shear strength under another normal stress. Clearly, this method decreased human operation of filling and packing new powder samples, and as a result, experimental errors were reduced and reproducibility was improved, as the bad reproducibility of shear strength measurements was partially attributed to human interference. For each sample, 2–3 measurements were repeated and the average was used to construct the yield locus to ensure accuracy. The yield locus was then extrapolated to 0 kPa normal stress, and this extrapolated shear strength was employed to characterize powder flow properties and is referred to as cohesion in this study. It is essentially the shear strength of the powder yield at zero normal stress estimated by extrapolation. 2.2.2. Angle of repose (AOR) Angle of repose is the largest angle at which powders can pile up. Generally, it is related to the powders’ cohesiveness and internal friction and it is widely used to characterize flow properties of powders. Measurement of the angle of repose was carried out using a PT-N Hosokawa Powder Characteristic Tester, following the standardized testing procedures of ASTM D6369-08 (ASTM Standard D6369-08, 1999). For each test, a powder sample was first loaded on a screen mounted with a vibrator. The particles were controlled to fall down through the funnel which was mounted under the screen in a slow and consistent rate by adjusting the vibration intensity. These particles would pile up on a plate which was aligned with the funnel. When the whole plate was covered with the particles, the largest angle between the powder pile surface and the horizontal plane was measured as the angle of repose. For each powder sample, 3–5 measurements were repeated and the average was used. 2.2.3. Fluidization measurement Fluidization of the powder samples was measured in a fluidized bed system as schematically shown in Fig. 3. The fluidized bed column was made of Plexiglass which was 1 foot tall with a 2-inch I.D. A porous plate was placed underneath as a gas distributor. Two pressure probes connected with a differential transducer were inserted into two ends of the column to measure the pressure dif-
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Fig. 3. Schematic of the fluidized bed.
ference. For a test, approximately 100 g of a powder was first loaded into the column and fluidized at a gas velocity of 4 cm/s until the powder reached a steady state so that large agglomerates formed during storage were broken and the powders’ “memory” for other former processes was erased as well. Then the gas flowrate was gradually reduced to the set gas velocities, and for each gas velocity, the pressure drop through the bed and the bed height were measured simultaneously when the powder was steady. 3. Results 3.1. Cohesion Fig. 4 shows cohesion values of the powder samples blended with nanoparticles Al2 O3 , SiO2 and TiO2 with respect to the concentration of nanoparticles. Overall, for all powder samples, with increasing concentrations of nanoparticles, cohesion exhibits general declining trends reflecting the improvement of the powder’s flow properties by these nanoparticles. However, for powder samples with nanoparticles SiO2 and Al2 O3 , their cohesion values increased slightly after reaching a minimum at around 1.0–1.2% (w/w). In the experience of the authors in ultrafine powder coat-
Fig. 4. Cohesion of powder samples blended with nanoparticles of Al2 O3 , SiO2 and TiO2 .
Fig. 5. Angle of repose of powder samples blended with nanoparticles of Al2 O3 , SiO2 and TiO2 .
ing application, cohesion needs to be lower than 0.4 kPa to give the powder good performance (Zhang & Zhu, 2007). In this study, a practical nanoparticle concentration is utilized to describe the concentration of nanoparticles added to powders to improve flow properties to an acceptable level used in powder coating applications. Consequently, the practical nanoparticle concentrations for SiO2 and Al2 O3 in the polyurethane powder are 0.3–3.0% (w/w) and 0.5–3.0% (w/w), respectively. It was also noticed that for the powder blended with nanoparticle TiO2 , the cohesion value decreased at a lower rate. As a consequence, their powder performance reached the acceptable level only when the concentration of TiO2 reached more than around 1.2% (w/w). The possible reasons that cause the different practical nanoparticle concentrations of the three nanoparticles will be discussed in Section 4.1. 3.2. Angle of repose (AOR) Fig. 5 shows the angle of repose values of the powder samples blended with the three nanoparticles with respect to their nanoparticle concentration. It is clear that all three nanoparticles in this study effectively decreased the polyurethane powder’s angle of repose and thereby improved its flow. However, there were some differences lying among the three nanoparticles. For the powder samples blended with Al2 O3 , as the concentration of Al2 O3 increased, the AOR decreased from 52◦ , then reached a minimum of about 34◦ at a concentration of 0.7–1.0% (w/w) of Al2 O3 , and increased slightly afterwards. For the powder samples blended with SiO2 , the AOR decreased to about 41◦ at a concentration of 1.0% (w/w) of SiO2 and then the AOR varied around 41◦ . For powder samples blended with TiO2 , similar to their cohesion values, their angle of repose values also showed a slower decrease rate with respect to the TiO2 concentration. The AOR decreased below 42◦ only when the nanoparticle concentration of TiO2 increased up to 1.2% (w/w). According to the authors’ experience, the angle of repose needs to be lower than 42◦ so that agglomeration and the formation of large clumps in the final coating will not occur during powder coating application. In this case, the practical nanoparticle concentrations for Al2 O3 , SiO2 and TiO2 are 0.3–3.0% (w/w), 0.5–3.0% (w/w) and 1.2–3.0% (w/w), respectively. Moreover, it seems that nanoparticle Al2 O3 improved the powder’s flowability the most by reducing the AORs to their lowest values.
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Fig. 6. Fluidization process of the polyurethane powder blended with nanoparticle TiO2 .
3.3. Fluidization measurement Fig. 6 shows a typical example of the fluidization of the polyurethane powder blended with nanoparticle TiO2 . Under gas velocities of 0–0.6 cm/s, the powder was not fluidized and the corresponding bed expansion ratios and pressure drops were minimal. While the gas velocity varied from 0.8 to 2.0 cm/s, the powder bed appeared as a smooth, fluid-like surface and bed height increased significantly with increasing gas velocity. As a result, both the powder bed surface and bed expansion ratio indicated that the powder became fluidized at a velocity in the range of 0.6–0.8 cm/s. This velocity corresponds to the point where the bed expansion ratio has its largest increase rate with respect to gas velocity. Accordingly, a new powder characteristic velocity was defined as the gas velocity at which the powder bed expansion has its largest increasing rate with respect to gas velocity—breakthrough fluidization gas velocity (Ubf ). As a powder fluidization characteristic gas velocity, Ubf illustrates how easily a fine powder becomes fluidized. In other words, it is difficult for a powder with a larger Ubf to become fluidized and therefore the powder has poor fluidizability. It needs to be pointed out that the traditional powder fluidization characteristic gas velocity, minimum fluidization gas velocity (Umf ), at which the pressure drop of the powder bed starts to equal its weight, showed a postponed powder initial fluidization point, as shown in the figure. Therefore, instead of Umf , Ubf was employed to characterize the fluidization of powders in this study. A detailed comparison and discussion about Umf and Ubf will be addressed in another work (Huang, Zhu, & Zhang, submitted for publication). Fig. 7 shows Ubf values of the powder samples with respect to the nanoparticle concentration. The powders with 0–0.1% (w/w) of all three nanoparticles and 0.3–0.5% (w/w) of TiO2 were too cohesive to be fluidized. Therefore, their Ubf values were not available. According to the authors’ experience on fluidization quality characterization, Ubf generally needs to be lower than 0.6 cm/s to
give a good fluidization performance for powder coatings (Zhang & Zhu, 2007). In addition, in a practical powder coating process, the gas velocity is generally controlled to be lower than 1 cm/s. Consequently, the practical nanoparticle concentrations for the polyurethane powder blended with Al2 O3 , SiO2 and TiO2 are 0.3–3.0% (w/w), 0.5–3.0% (w/w) and 1.2–3.0% (w/w), respectively. Moreover, it seems nanoparticle Al2 O3 improved the powder’s fluidization quality most by reducing the Ubf values to their lowest values. In this study, the bed expansion ratio (BER) at 0.6 cm/s was also taken as an index of fluidization quality. As mentioned above, for
Fig. 7. Breakthrough gas velocity of powder samples blended with nanoparticles of Al2 O3 , SiO2 and TiO2 .
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Fig. 8. BER at 0.6 cm/s of powder samples blended with nanoparticles of Al2 O3 , SiO2 and TiO2 .
powder coatings, powders with a good fluidization quality become fluidized at gas velocities smaller than 0.6 cm/s and generally show a significant bed expansion ratio that is larger than 1.5. On the other hand, powders with poor fluidization quality generally remain unfluidized at 0.6 cm/s, and as a result, the bed expansion ratio is minimal. Fig. 8 shows the BER at 0.6 cm/s of the powder samples with respect to the nanoparticle concentration. It is clear that powders blended with more than 0.3% (w/w) of Al2 O3 or more than 0.5% (w/w) of SiO2 showed significant BER values which are over 1.8, while powders needed more than 1.2% (w/w) TiO2 to have BER values larger than 1.7. The practical nanoparticle concentration results are consistent with those concluded from Ubf . However, in contrast, the powders blended with Al2 O3 and SiO2 exhibited nearly identical BERs at 0.6 cm/s while the Ubf of the powders with Al2 O3 showed smaller values. 4. Discussion
Fig. 9. SEM images of powder samples blended with 1% (w/w) nanoparticles of (a) Al2 O3 and (b) TiO2 .
4.1. Practical nanoparticle concentrations It is well known that nanoparticles improve the flow properties of fine powders because the interparticle forces which cause fine powders to become cohesive are reduced due to the existence of nanoparticles stuck to the surface, as shown in Fig. 9. Castellanos (2005) measured the interparticle forces between two styrenebutadiene based xerographic toner particles of 12.7 m coated with 0.05% and 0.4% (w/w) of silica nanoparticles (Aerosil R812, from Degussa) by an atomic force microscope (AFM). It was found that the interparticle forces of the powder with 0.4% (w/w) of silica were smaller than those with 0.05% (w/w) of silica. His work suggested the difference in interparticle forces is a result of different contact between two toner particles. 0.05% (w/w) of silica only covered 2.77% of surface of the toner particle, and as a result, the contact still remained as toner particle to toner particle which is polymer to polymer contact. However, 0.4% (w/w) of silica covered about 22.1% of toner particle surface, which was sufficient to convert the polymer to polymer contact to silica to silica contact. The silica to silica contact decreased the local radius of curvature and increased the hardness, both leading to decreasing the interparticle forces. In this study, shear strength, angle of repose and fluidization tests showed the three nanoparticles all improved the flow prop-
erties of the powder effectively. However, relatively more TiO2 (1.2%, w/w) was needed to achieve the improvement made by relatively less Al2 O3 and SiO2 , around 0.3–0.5% (w/w). This is probably attributed to the different physical properties of these nanoparticles, leading to different surface coverage ratios on a polyurethane particle. Fig. 9 shows a comparison of scanning electron microscope (SEM) images of a polyurethane particle blended with 1% (w/w) Al2 O3 and TiO2 . The SEM image of the particle with 1% (w/w) of SiO2 is not illustrated in this paper because it looks very similar to the image of the particle with Al2 O3 . As shown in the images, nanoparticles formed small agglomerates varying from 50 to 300 nm stuck onto the surface of the particle. It is clear that the surface coverage ratio by 1% (w/w) of TiO2 is much less than the surface coverage ratio by 1% (w/w) of Al2 O3 . However, it is difficult to quantify the surface coverage ratio from SEM images. The following process is employed to theoretically approximate the surface coverage ratio of nanoparticles over a host particle. Specific surface area of host particles (Sh , m2 /kg) is Sh =
6 , h dh
(1)
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Table 2 Surface coverage ratio vs. practical nanoparticle concentration. Nanoparticle Al2 O3 SiO2 TiO2
Practical nanoparticle concentration 0.5–3.0% (w/w) 0.5–3.0% (w/w) 1.2–3.0% (w/w)
28.8–173% 41.8–251% 23.8–59.5%
where h and dh are particle density and average diameter of the host particle. In this study, h and dh are 1570 kg/m3 and 20 × 10−6 m, respectively. The projected area of nanoparticle agglomerates on host particles (Sa , m2 /kg) is Sa =
3 , 2a da
(2)
where a and da are density and average diameter of the nanoparticle agglomerates. In this study, da was estimated by manually measuring the diameter of agglomerates on SEM images. For each nanoparticle, at least 100 measurements were made and then their average was taken. The da of Al2 O3 , SiO2 and TiO2 was 80, 90 and 200 nm, respectively. The solid fraction of agglomerates may vary from 0.2 to 0.64 depending on processing procedures and applied consolidation pressure. In this case, the nanoparticle agglomerates were formed through a milling process for which the transient consolidation pressures may be high. Therefore, a solid volume fraction of 0.5 for the agglomerates was used. a was estimated to be half of the particle density. As a result, the surface coverage ratio of nanoparticle agglomerates to host particles is Rsc =
Sa · Ma 3/(2a da ) Ma d Ma = = h h · , · Sh · Mh 4a da Mh 6/(h dh ) Mh
Surface coverage ratio
(3)
where Ma and Mh are the mass of agglomerates and host particles, respectively. Substituting the physical properties of the host particle and nanoparticles, the surface coverage ratios of 1% (w/w) Al2 O3 , SiO2 and TiO2 to polyurethane particles are 84%, 58% and 19.8%. This is consistent with what SEM images have shown and confirms the effects of physical properties of nanoparticles on the surface coverage ratio on a host particle. When examining the practical nanoparticle concentration found in this study with the surface coverage ratio theory, it was found that for all three nanoparticles, the surface coverages corresponding to the lower ends of the practical nanoparticle concentrations were all higher than 20%, as shown in Table 2. This confirmed the Castellanos’s model that when nanoparticles cover more than 20% of the surface of a host particle, the contact between two host particles become nanoparticle to nanoparticle contact, as a result, the local radius of curvature of the host particle decreased and the hardness increased, both leading to decreasing the interparticle forces. It was also noticed that 3.0% (w/w) of Al2 O3 and SiO2 are more than enough to cover 100% of the polyurethane particle according to the theoretical calculation results in Table 2. However, none of these completely covered polyurethane particles were observed under the SEM. The extra nanoparticles may exist among polyurethane particles in the form of larger agglomerates, resulting in decreasing flow properties. Therefore, it can be explained that the cohesion values of the powder samples with more than 1.2% (w/w) of Al2 O3 and SiO2 increased as shown in Fig. 4. 4.2. Powder flow property characterization techniques When comparing all the flow property characterization techniques employed in this study, it was found that each technique
agrees with the theory in terms of determining the practical nanoparticle concentrations. This is a promising result that helps to confirm the validity of each technique used. However, it was also found that they are conflicting with each other when greater details regarding improvement of flow properties are required, in terms of the different characterization methods. Both Ubf and angle of repose measurements showed nanoparticle Al2 O3 has the largest impact on powders’ flow properties by reducing Ubf and AOR of the powder samples to their lowest values, while the cohesion and BER values of the powders blended with Al2 O3 and SiO2 were similar for the powder used in this study. Similar inconsistencies among different flow property characterization methods have also been found and reported in other studies (Krantz et al., 2009; Ploof & Carson, 1994; Taylor et al., 2000). It was suggested that characterization techniques should match the application and the process equipment to be used. Krantz et al. (2009) further proposed to classify powder flow property characterizing techniques into three groups including dynamic, dynamic–static and static according to the state of a powder sample when it is measured. Flow property results characterized under different states are not interchangeable. The cohesion measurement in this study was classified into the static group because shear strengths were measured after the powder sample was pre-consolidated to a critical state and the powder sample was under normal stress during the whole measurement process. It is clear that BER at 0.6 cm/s reflects the flow properties of powders under a dynamic state because of the interaction between powders and gas in the fluidized bed. Ubf describes the point at which powder transitions from fluidized (dynamic state) to unfluidized (static state). AOR characterizes the largest piled up angle which is essentially resulting from the momentum balance between the falling down motion of a particle and resistance from the plate and other particles. As a result, Ubf and AOR characterize the flow properties of powders in the dynamic-static state. Accordingly, the results presented in this study can be explained: AOR and Ubf show nanoparticle Al2 O3 improves the flow properties of the polyurethane powder under dynamic-static state while cohesion and BER indicate similar effects of nanoparticles Al2 O3 and SiO2 on improving the flow properties of the powder under both static and dynamic states. 4.3. Effects of combinations of nanoparticles Figs. 10–13 show the cohesion, AOR, Ubf and BER values of the powders blended with the combinations of nanoparticles (2(Al2 O3 )–SiO2 and Al2 O3 –2(SiO2 )) as well as the results of the powders mixed with individual nanoparticles of Al2 O3 and SiO2 . For cohesion as shown in Fig. 10, at lower nanoparticle concentrations, it seems that the powder samples with combinations had better flow properties than those blended with individual Al2 O3 and SiO2 ; however, when the nanoparticle concentrations increased to over 1.2% (w/w), an opposite effect was found, that is, the powder samples with the combinations illustrated worse flow than those with individual nanoparticles. AOR (Fig. 11) and Ubf (Fig. 12) showed the powder samples with combinations had intermediate effects between Al2 O3 and SiO2 at lower concentrations while a higher concentration (>0.7%, w/w) of the combinations was much
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Fig. 10. Comparison of cohesion of powder samples blended with individual nanoparticles and combinations.
Fig. 12. Comparison of breakthrough fluidization gas velocity (Ubf ) of powder samples blended with individual nanoparticles and combinations.
less effective at improving the flow properties of the polyurethane powder than individual Al2 O3 and SiO2 . BER at 0.6 cm/s (Fig. 13) indicated the combinations were less effective than individual types of nanoparticles along the whole experimental range of nanoparticle concentrations. However, lower concentrations of the combinations still gave the powders an acceptable flow performance while the powder samples with higher concentrations (>1.2%, w/w) could not be fluidized at 0.6 cm/s. All of these characterization techniques indicated that the effects of nanoparticle combinations are not a simple summing up of the effects of their component nanoparticles. The effects of individual types of nanoparticles can be further combined, intensified or weakened by combining them together. On the other hand, it seems that the improvement of flow properties for the powder blended with the combinations depends on their concentration. In one aspect, the improvement of flow properties was limited for the powder blended with higher concentrations of the combinations. Generally these concentrations are excluded from the practical nanoparticle concentration ranges. For instance, according to the
Ubf results, the practical nanoparticle concentrations for combinations 2(Al2 O3 )–SiO2 and Al2 O3 –2(SiO2 ) are around 0.3–1.0% (w/w) only, and higher concentrations (>1.2%, w/w) are not included. This is probably attributed to the fact that nanoparticles are polarized, and as a result tend to form agglomerates with a chain-like structure. Al2 O3 and SiO2 might be oppositely polarized and it is possible for them to form large agglomerates that weaken the improvement of flow properties, when their concentrations are high. In another aspect, although the powder blended with lower concentrations of the nanoparticle combinations showed intensified, weakened or combined improvement of the flow properties in comparison with the powder samples mixed with individual types of nanoparticles according to various powder characterization techniques, their flow properties are far better than the acceptable level for powder coating application. Therefore, with a lower concentration, the combinations of nanoparticles still can be applied in practice. In particular, if the amount of a certain nanoparticle is constrained due to some reasons other than flow properties (i.e. too much titanium oxide leads to coating with a white color, which is not desired
Fig. 11. Comparison of angle of repose (AOR) of powder samples blended with individual nanoparticles and combinations.
Fig. 13. Comparison of bed expansion ratio (BER) at 0.6 cm/s of powder samples blended with individual nanoparticles and combinations.
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for a clear coat), while this largest acceptable concentration of this nanoparticle is still insufficient for the practical concentration of this nanoparticle, it is an alternative to combine this nanoparticle with another nanoparticle to make a combination that could greatly improve the flow properties. 5. Conclusions Polyurethane powder from the powder coating industry was blended with three types of nanoparticles to modify its flow properties. A term of “practical nanoparticle concentration” was used to identify a range of nanoparticle concentrations, in which the nanoparticles improve the flow properties of fine powders to an acceptable level for industrial applications. According to the experience of the authors in powder coating, a variety of powder characterization techniques indicated that the practical nanoparticle concentrations for the polyurethane powder are: 0.5–3.0% (w/w) of Al2 O3 , 0.5–3.0% (w/w) of SiO2 and 1.2–3.0% (w/w) of TiO2 . The relatively higher concentration needed for nanoparticle TiO2 is probably attributed to its larger particle size leading to larger agglomerates and consequently less surface coverage ratio of the agglomerates. The effects of combinations of Al2 O3 and SiO2 were also studied. It was found that the effects of nanoparticle combinations are not a simple summing up of the effects of their component nanoparticles. On the one hand, a higher concentration of the combinations reduces the improvement of the flow properties. This could be attributed to the formation of large agglomerates by the nanoparticles with higher concentrations. On the other hand, combinations of the nanoparticles at lower concentrations still effectively improve the flow properties of the powder, so they can still be used for practical applications. Several characterization techniques were employed to illustrate the flow properties of the powder samples modified by the nanoparticles. They gave consistent results of powder flow properties in terms of determining the practical nanoparticle concentrations. However, inconsistency was found among the four characterization methods in terms of illustrating the improvement of flow properties. This is because the various states of the powder during a measurement, including dynamic, dynamic-static and static states. Because there is no necessary relationship between any two measurements under different states, characterization techniques which test the flow properties of a powder under all three states are needed to fully describe the flow properties of the powder.
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References ASTM Standard D6128-06. (2006). Standard test method for shear testing of bulk solids using the jenike shear cell. West Conshohocken, PA, USA: ASTM International. ASTM Standard D6369-08. (1999). Standard test method for bulk solids characterization by Carr indices. West Conshohocken, PA, USA: ASTM International. Carr, R. L. (1965). Evaluating flow properties of solids. Chemical Engineering, 72(2), 163–168. Castellanos, A. (2005). The relationship between attractive interparticle forces and bulk behaviour in dry and uncharged fine powders. Advances in Physics, 54, 263–376. Dutta, A., & Dullea, L. V. (1990). A comparative evaluation of negatively and positively charged submicron particles as flow conditioners for a cohesive powder. AIChE Symposium Series, 86, 26–40. Geldart, D. (1973). Types of gas fluidization. Powder Technology, 7(5), 285– 292. Hollenbach, A. M., Peleg, M., & Rufner, R. (1983). Interparticle surface affinity and the bulk properties of conditioned powders. Powder Technology, 35(1), 51– 62. Huang, Q., Zhu, J., & Zhang, H. (submitted for publication). Characterization of gas solid fluidization properties of fine powders—A new characteristic fluidization gas velocity. Jenike, A. W. (1967). Quantitative design of mass-flow bins. Powder Technology, 1(4), 237–244. Kenny, J. C., Ueno, T., & Tsutsui, K. (1996). Analytical approach for high quality appearance powder coatings. Journal of Coatings Technology, 68(855), 35– 43. Kono, H. O., Huang, C. C., & Xi, M. (1989). The effect of flow conditioners on the tensile strength of cohesive powder structures. AIChE Symposium Series: Fluidization and Fluid Particle Systems: Fundamentals and Applications, 85(270), 44– 48. Krantz, M., Zhang, H., & Zhu, J. (2009). A new approach to powder characterization: Static and dynamic testing. Powder Technology, 194, 239–249. Nishida, K., & Sugiyama, T. (1996). Powder coatings. U.S. Patent No. 5,567,521. Ploof, D. A., & Carson, J. W. (1994). Quality control tester to measure relative flowability of powders. Bulk Solids Handling, 14(1), 127–132. Prescott, J. K., & Barnum, R. A. (2000). On powder flowability. Pharmaceutical Technology, 24(10), 60. 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 236. Schwedes, J. (2003). Review on testers for measuring flow properties of bulk solids. Granular Matter, 5(1), 1–43. Takeda, H., & Nakaya, I. (1999). Powder coating composition and application method therefor. U.S. Patent No. 5,948,866. Taylor, M. K., Ginsburg, J., Hickey, A. J., & Gheyas, F. (2000). Composite method to quantify powder flow as a screening method in early tablet or capsule formulation development. AAPS PharmSciTech, 1(3), 20–30. Visser, J. (1989). Van der Waals and other cohesive forces affecting powder fluidization. Powder Technology, 58(1), 1–10. Xu, C., Huang, Q., Zhang, H., & Zhu, J. (2006). Improving fluidizability of cohesive particles by surface coating with flow conditioners. In The Fifth World Congress on Particle Technology Orlando, FL, United States. Zhang, H., & Zhu, J. (2007). Internal report to a major automobile manufacturer. Zhu, J., & Zhang, H. (2004). Fluidization additives to fine powders. U.S. Patent No. 6,833,185. Zhu, J., & Zhang, H. (2005). Ultrafine powder coatings: An innovation. Powder Coating, 16(7), 39–47. Zhu, J., & Zhang, H. (2006). Method and apparatus for uniformly dispensing additive particles in fine powders. U.S. Patent, Applied, November 20, 2006.