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water suspensions in high shear mixer
Powder Technology 173 (2007) 203 – 210 www.elsevier.com/locate/powtec
Effect of energy density, pH and temperature on de-aggregation in nano-particles/water suspensions in high shear mixer A.W. Pacek ⁎, P. Ding, A.T. Utomo School of Chemical Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom Received 22 May 2006; received in revised form 16 November 2006; accepted 3 January 2007 Available online 16 January 2007
Abstract The effect of energy input, pH and temperature on de-aggregation of hydrophilic silicon dioxide powder (particle size 12 nm) in a high shear mixer was investigated. It has been found that de-aggregation is a two step process. Initially, at low energy input very large aggregates (3–1000 μm) are gradually broken into smaller secondary aggregates (2–100 μm) of a single modal size distributions. As the energy input increases primary aggregates (0.03–1 μm) are eroded from the secondary aggregates leading to bimodal size distributions with the first mode between 0.03 μm and 1 μm corresponding to the primary aggregates and the second mode between 2 μm and 100 μm corresponding to the secondary aggregates. At a sufficiently high energy density all secondary aggregates are broken into primary aggregates however, even at the highest energy density employed the primary aggregates could not be broken into single nano-particles. The temperature and the pH affect deaggregation kinetics but do not alter de-aggregation pattern. Increasing pH at low temperature speeds up de-aggregation, whilst increasing pH at high temperature slows down de-aggregation process. © 2007 Elsevier B.V. All rights reserved. Keywords: Silica nano-particles; Suspensions; De-aggregation; Primary aggregates; Secondary aggregates; Energy density
1. Introduction Nano-particles such as titanium, aluminium or silicon oxides have wide industrial applications in manufacturing of pigments, fillers, ceramics, catalysts as well as electromagnetic and optical devices. In the majority of those applications nano-particles are processed as suspensions in different aqueous solutions and frequently the quality of final products depends on the properties of these suspensions characterized by the particle size, size distribution, shape and morphology [1]. Therefore, nano-particles which are often supplied in the form of dry powders have to be re-dispersed in aqueous solutions to give homogenous, stable dispersions and it is essential that the aggregates inherently present in dry nano-powders are broken into primary nano-particles during dispersion process. Large aggregates suspended in a fluid are broken when the hydrodynamic forces (determined by the flow field) exceed the cohesive bonds between particles or smaller aggregates. The strength of individual bonds depends on the type of nano⁎ Corresponding author. E-mail address: [email protected] (A.W. Pacek). 0032-5910/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.powtec.2007.01.006
particles (van der Waals interactions), surface properties (wettability, electrostatic interactions) and the nature of liquid bridges within an aggregate [2]. Aggregates can be broken by normal or shear stress by erosion, e.g., a gradual removal of small fragments from the aggregates periphery or by bulk rupture, e.g., an abrupt breakage of the aggregates into a number of relatively large fragments [3]. The mechanism of breakage depends on the size of the aggregates and the energy intensity [4]. It has been postulated that as erosion occurs at low energy intensity and as the energy intensity increases particles are broken by fragmentation [5]. As the aggregates become smaller during deaggregation, surface forces become more important than mass forces and for aggregates smaller than 1 μm, surface forces are more than one million times larger than mass force [6]. Therefore, breakage of large aggregates is relatively simple, whereas breakage of aggregates smaller than 1 μm might be very difficult and it has been suggested that the particles smaller than 10 to 100 nm cannot be broken by mechanical action [7]. Dispersion and de-aggregation of nano-powders in liquids can be carried out in ball mills, ultrasonic processors and rotor– stator high shear mixers. The kinetic of de-aggregation in a high
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shear mixer is relatively easy to control and such devices are frequently used in industry to produce different types of suspensions. Therefore, in this work the effect of energy input, pH and temperature on de-aggregation of silica nano-particles in a Silverson high shear mixer has been investigated and the results are discussed below. 2. Experimental 2.1. Materials A hydrophilic fumed silicon dioxide (Aerosil® 200 V from Degussa) was supplied as a dry powder. The primary particles have density of 2200 kg/m3, SiO2 content higher than 99.8%, specific surface area of 200 m2/g and the average size of 12 nm (manufacturer's data). The powder was dispersed in de-ionized, double distilled water and when necessary pH was adjusted with H2SO4 or NaOH.
potential powder was dispersed in liquid and suspension was sheared in a high shear mixer at 8000 rpm for 1 h. Particles/aggregates size distributions were measured by particle size analyzer Mastersizer 2000 (Malvern Instruments) designed for particles in the size range between 20 nm and 2 mm which gives particles size as a diameter of the spheres with the volume equal to the volume of measured particles. In this instrument Mie theory was employed to calculate particle size distributions from the scattered laser light using the refractive index of silica particles of 1.46. The accuracy of the measurements was estimated by measuring average size and size distributions of calibrated standard particles (supplied by Duke Scientific) of 10.3 ± 0.05 μm and 97 ± 3 nm and for both sizes experimental error was well bellow 5%. The size and shape of aggregates/particles were also analyzed with an Environmental Scanning Electron Microscope (ESEM, Philips XL30). Conductivity of suspension was measured using InLab 730® conductivity probe from Metler Toledo and pH was measured using Jenway 3020 pH meter.
2.2. Nano-particles/aggregates/suspension characterization 2.3. Experimental rig Zeta potential of particles/primary aggregates was measured by Zetamaster (Malvern Instruments) designed for the particles in size range 20 nm to 3 μm. To ensure that the particles/aggregates were in the required size range, prior to the measurements of zeta
Experiments were carried out in a high shear, rotor–stator mixer (L4R from Silverson) with rotor diameter of 0.028 m, rotor height of 0.015 m and a gap between the rotor and the
Fig. 1. Transient volume distribution functions at different energy dissipation rates: (a) fragmentation of large secondary aggregates at 4.8 W kg− 1; (b) fragmentation of secondary aggregates at 21.7 W kg− 1; (c) fragmentation of secondary aggregates at 89.9 W kg− 1 during first 20 min of processing; (d) erosion of primary aggregates from the surface of secondary aggregates at 89.9 W kg− 1 after longer processing time. 5% Aerosil in water, pH 4, t = 20 °C.
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stator of 0.5 mm. To improve macro-mixing in the vessel the rotor–stator was mounted off centre in a sealed, stainless steel jacketed vessel (diameter 0.1 m, height 0.15 m) fitted with sampling port. The measured power number of this mixer is equal to 1.7 and it is in a good agreement with the literature data [8]. The temperature was controlled by the water bath and was measured by the platinum thermocouple. 2.4. Procedure In all experiments the same procedure was followed. Silica powder was pre-dispersed in water in a glass stirred vessel fitted with Rushton turbine, pH was adjusted to required value (4, 7 or 9) and suspension was stirred for 30 min. After that time the aggregate size distribution was measured in situ using video– computer–microscope system [9] and in a diluted sample using Mastersizer 2000. Next the dispersion was transferred to the high shear mixer from which the air was completely excluded and the temperature of the water bath was set either to 20 °C, 50 °C or 70 °C. The rotor speed was set to the required value (3000, 5000 or 8000 rpm) and at each speed the dispersion was sheared for 3 or 4 h. Small samples of suspension were taken every 10 min and particles/aggregates size distributions were measured. 3. Results and discussion 3.1. The effect of energy input on kinetics of de-aggregation Transient volume distributions functions measured at three different speeds (different energy dissipation rates) over the period of 3 h are summarised in Fig. 1. Fig. 1a and b illustrates the evolution of aggregate size at energy dissipation rate of 4.8 W kg− 1 (3000 rpm) and 21.7 W kg− 1 (5000 rpm) respectively and Fig. 1c and d illustrates the evolution of aggregate size distributions at energy density of 89.9 W kg− 1 (8000 rpm). From Fig. 1 it appears that de-aggregation of silica powder can be seen as a two step process. In the first step, at a relatively low energy input (short processing time at 8000 rpm or low energy dissipation rates at 3000 and 5000 rpm) large aggregates (between 5 and 500 μm) of initially dry powder shown in Fig. 2a are broken into a smaller, secondary aggregates with the size between 3 and 80 μm. The transient, single modal volume distribution functions shown in Fig 1a, b and c are approximately self preserving and as the time progresses they are gradually shifting towards the smaller size aggregates. The shape of volume distribution functions indicates that in this step fragmentation of secondary aggregates is the main mechanism of de-aggregation [5]. It is worth to stress that the smallest aggregates obtained in this step are of the order of 2 μm which is in a good agreement with literature information that the aggregates/particles larger than 1 μm are relatively easy to break. In the second step, at sufficiently high energy input (processing at 8000 rpm for approximately 30 min or more) large secondary aggregates start breaking into a small primary aggregates clearly seen in Fig. 2b. The transient volume distributions functions (Fig. 1d) become bimodal, with the first
Fig. 2. Images of silica aggregates: (a) secondary aggregates in dispersion charged into high shear mixer after premixing with Rushton turbine (time 0 in high shear mixer), width of the image is equal to 1.1 mm; (b) primary aggregates in the same dispersion after 4 h of shearing at 8000 rpm.
mode ranging from approximately 50 nm to 0.9 μm (primary aggregates) and the second mode ranging from 3 μm to 80 μm (secondary aggregates). Increase of energy input leads to a gradual reduction of volume fractions of the aggregates in the second mode and an increase of the volume fractions of aggregates in the first mode. The widths of both modes practically do not change and the median diameters are practically constant. It has to be stressed here that even at the highest investigated energy density (1500 kJ kg− 1) reported here and at even higher energy density during ultrasonication (of the order of 4300 kJ kg− 1), the aggregates in the first mode (primary aggregates) could not be broken into a single nanoparticles. The qualitative results obtained with Mastersizer 2000 were confirmed by ESEM analysis and the ESEM image in Fig. 2b clearly shows many primary aggregates in the range 50 nm to 100 nm and practically no single nano-particle (of the order of 12 nm). Similar behaviour of polymer latex nano-particles suspended in surfactant solutions was recently reported by the authors [10] who postulated that the single nano-particles in the closepacked primary aggregates are close enough to enter into the primary energy well leading to irreversible aggregation. The same explanation can be offered here. The median diameters of the secondary aggregates as a function of the processing time and as a function of the energy density are shown in Fig. 3. The median diameters at the two
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Fig. 3. Median diameters (d50) of the secondary aggregates at pH 4 and T = 20 °C as function of: (a) processing time and (b) energy density at different energy dissipation rates: ε = 4.8 W kg− 1 ( ), ε = 21.7 W kg− 1 (○), ε = 89.9 W kg− 1 (▾). Solid lines — linear regression.
•
lowest energy dissipation rates were calculated from the size distributions of all aggregates having single modal size distributions. At the highest energy dissipation rate where size distributions become bimodal (see Fig. 1d) median diameter was calculated from the second mode only e.g., median diameter in the latter case corresponds to the population of the secondary aggregates only. The reduction of the median diameter (or any other mean diameter) during de-aggregation process can be described in terms of a processing time and the energy dissipation rate or in terms of the energy density (size–energy model). For high shear mixer the energy dissipation rate was calculated from: e ¼ Pod N 3 d D5 d q
ð1Þ
and the energy density from: E ¼ ed t
ð2Þ
Experimental median diameters plotted as a function of time (Fig. 3a) in the log–log coordinates fall at three distinctive, nearly parallel straight lines with the slope ranging from 0.23 to 0.20 with coefficient of determination (r2) larger than 0.98. The reduction of the aggregates mean diameters with time during grinding/de-aggregation is frequently described by the so called size–energy model [5,7]: dav ¼ Cd E −a
ð3Þ
This simple experimental model developed in the mid-1950s is still used to calculate the mean (usually median) particle size in a different type of grinding devices. The exponent α can be interpreted as a reduction rate of the median diameter and constant C as a size of aggregate at unit specific energy input. This simple two parameter model allows the quick prediction of the mean size in a given grinding device and it also allows easy comparison between different types of grinding devices. The major disadvantage of this model is that it does not allow the prediction of size distributions and it can be used to model grinding processes where size distributions are single modal. Considering that the alternative way of predicting the mean
particle size and size distribution is the solution of the population balance model (integro-differential equation usually convertible to a set of differential equations) which is rather complex and time consuming, it is not surprising that this model has been successfully used in literature to describe the grinding of solid particles [5,7] where α of the order of 0.68–0.72 was obtained. It has also been adopted to correlate average drop size with energy input during liquid/liquid emulsification in different types of high shear mixers and in those processes in which α of the order of 0.4 was reported [11]. Fig. 3b shows that by plotting experimental d50 as a function of energy density in log–log coordinates all data points can be collapsed (with engineering accuracy) into a straight line with an average slope α = 0.26 and r2 = 0.91 which indicates that that first step of de-aggregation of silica powder (when size distributions are single modal) can also be described by this model. Whilst the breakage of secondary aggregates having the single modal size distributions can be analyzed in terms of rather simple size–energy model, the analysis of de-aggregation when size distributions become bimodal is more complex. In case of bimodal size distributions the mean sizes cannot be used to describe the changes of the population of the aggregates and in principle the evolution of bimodal size distribution with time should be analyzed within the framework of population balance model [12]. Whilst such analysis is currently performed and the results will be reported soon, the whole process of deaggregation can be qualitatively discussed in terms of transient aggregate size distributions (Fig. 1) or it can be quantified in terms of breakage of the secondary aggregates (Fig 3) combined with the analysis of the increase of a cumulative mass fraction of the primary aggregates (Fig 4). The volume fraction of the primary aggregates and d90 as a function of the processing time at 8000 rpm are summarised in Fig. 4. It is interesting to note that primary aggregates are generated only after a certain amount of energy has been put into the dispersion (or after a certain processing time, see Fig. 1c and d). As the volume fraction of the primary aggregates levels at an approximately 0.95, d90 falls below 1 μm and stays practically constant indicating that practically all the secondary
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Fig. 4. Fraction of aggregates smaller than 1 μm (primary aggregates) and d90 of whole population of aggregates as a function of processing time at pH 4, T = 20 °C and ε = 89.9 W kg− 1.
aggregates were broken. Until the secondary aggregates are present in the suspension the volume fraction of primary aggregates increases linearly with the processing time at the rate of the order of 0.01 s− 1. After d90 falls below 1 μm the rate of increase of the volume fraction of the primary aggregates reduces to approximately 0.0005. It is worth to notice that even after 4 h of processing at 8000 rpm nano-particles were not observed in the suspension (see Fig. 1d) which indicates that the primary aggregates were not broken. Similar pattern of breakage of silica aggregates built from 0.3 μm nano-particles was reported by Kusters et al. [4] where, based on similar evidence as presented above, authors concluded that silica aggregates are broken by erosion in the whole range of the energy density. In our case it appears that at low energy density fragmentation of the large aggregates occurs and at a high energy density primary aggregates are sheared from surface of the secondary aggregates.
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the analysis of aggregate size/size distributions (Figs. 5b and 6) shows that this is the case only at a low temperature. At 20 °C an increase of pH from 4 to 9 speeds up de-aggregation as can be seen from an increase of median size reduction rate α from 0.23 at pH 4 to 0.29 at pH 9. Also, at pH 9 after 10 min 30% of the secondary aggregates were broken into the primary aggregates, whereas at pH 4 after 30 min only 21% of the secondary aggregates were broken however, in both cases after 4 h of shearing 95% of the secondary aggregates were broken into the primary aggregates. Fig. 6a also shows that even when the breakage rate was highest (at pH 9, t = 20 °C) the primary aggregates (size range 50–100 nm) were not broken into a single nano-particles (average size 12 nm). As the temperature increased to 50 °C, the opposite effect of pH on breakage kinetics was observed and at pH 4 (small repulsive forces) de-aggregation was faster than at the higher pH. Further increase of temperature to 70 °C leads to a further, very strong reduction of the breakage rate at the highest pH (more than 4 h was needed to break 90% of secondary aggregates, α = 0.19) but at pH 4 the breakage rate increased (90% of secondary aggregates were broken within 55 min, α = 0.27). Whilst, as discussed above and as shown in Fig. 5b, both the temperature and the pH have a strong effect on the kinetics of deaggregation and breakage rate, the effect of both those parameters on the final size distribution of the primary aggregates is negligible. There is a pronounced difference between the transient volume distributions functions shown in Fig. 6a and b with much faster shift towards smaller particles in Fig. 6a (pH 9, t = 20 °C)
3.2. The effect of pH and temperature on kinetics of de-aggregation The change of pH of the suspension leads to a change of electrostatic charges on the aggregates surfaces which in turns affects the repulsive forces between them and might alter the kinetics of de-aggregation. To assess and to quantify the effect of pH on de-aggregation kinetics the relation between pH and the charge of the particles surface, zeta potential at different pH has been measured and the results are summarised in Fig. 5a. The combined effect of pH and temperature on the kinetics of de-aggregation is summarised in Fig. 5b and typical transient distributions during de-aggregation at pH 4 and pH 9 are compared in Fig. 6. The higher the absolute values of zeta potential, the larger the electrostatic repulsive forces and the separation of the particles becomes easier. In investigated system zeta potentials at pH 7 and higher are of the order of − 40 mV and zeta potential at pH 4 is approximately − 15 mV which indicates that the repulsive forces at pH 9 are larger than at pH 4. This suggest that deaggregation at pH 9 and pH 7 should be faster than at pH 4 but
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Fig. 5. (a) Zeta potential of primary aggregates as a function of pH for 1% ( ) and 5% (○) w/w silica in water; (b) the effect of pH and temperature on time necessary to break 95% of secondary aggregates into aggregates smaller than 1 μm; ( ) pH 4, (○) pH 7, (▾) pH 9.
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which Stern potential can be replaced with the measured zeta potential [15,17]: eWs 2 1 jh 1−tanh ð6Þ VE ¼ 2Z 2 nkT 2 kT j Hydration energy (VH) has an effective range of few nanometers and exponentially decays with distance [18]. It can be calculated from the experimental correlation [15]: VH ¼ V1 d e−h=h1 þ V2 d e−h=h2
ð7Þ
where: V1/2πR = 0.14 J/m2, h1 = 0.057 nm, V2/2πR = 5.4 × 10− 3 J/m2 and h2 = 0.48 nm are experimentally determined parameters [14]. All components of the interaction energy between two silica particles of the same diameter were calculated from Eqs. (4)–(7) and the results are summarised in Fig. 7a for the particles separated by more than 1 nm and in Fig. 7b for the particles separated by less than 1 nm. In both cases it is clear that the interactions between the silica particles in water are dominated by the repulsive hydration force. The repulsive electrostatic energies at different pH (see insert in Fig. 7a) are two orders of magnitude
Fig. 6. Transient volume distribution functions in 5% Aerosil/water suspension at rotor speed of 8000 rpm at the following processing conditions: (a) pH 9, T = 20 °C after processing times of: 0 min ( ), 10 min (○), 20 min (▾), 240 min (▽); (b) pH 4, T = 20 °C after processing times of: 0 min ( ), 20 min (○), 50 min (▾), 240 min (▽).
•
•
than in Fig. 6b (pH 4, t = 20 °C), but again the final volume distributions (after 240 min) are practically identical. In principle the effect of pH and temperature on the interactions between particles/aggregates can be analyzed using extended DLVO (Derjaguin–Landau–Verwey–Overbeek) model. In this model the interaction force between two spheres of the same radii (R) is expressed in terms of the van der Waals energy (VA), the electrostatic energy (VE) and the hydration energy (VH) between two flat surfaces [13,14]: F ¼ pRðVA þ VE þ VH Þ
ð4Þ
Attractive van der Waals energy (VA) between two flat surfaces can be calculated from [13]: VA ¼ −
A 12ph2
ð5Þ
where values of Hamaker constant for silica–silica in water reported in literature [14–16] are in the range 0.46 × 10− 20 J to 1.02 × 10− 20 J. The electrostatic repulsive energy (VE) depends on the charge on particles surface and the properties of the liquid. For constant surface potential it can be estimated from Eq. (6) in
Fig. 7. Components of interaction energy between particles: VA — attractive van der Waals energy, VE — repulsive electrostatic energy, VH – repulsive hydration energy; (a) particles separated by more than 1 nm (aggregates); (b) particles separated by less than 1 nm (single particles).
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lower than van der Waals and hydration energies and they are practically negligible both at shorter and at longer separation distances. It is possible to postulate that as the surface of aggregates is not very smooth the shortest distance separating them is probably larger than 1 nm and at such a distance the repulsive hydration energy does not allow entry into the deep primary well. This means that the secondary aggregates which are built from the primary aggregates are relatively weak and can be broken at low energy input as discussed above. Very strong repulsive hydration interactions also explain exceptional stability of the suspensions of primary silica aggregates even at pH corresponding to the iso-electric point [11,14]. On the other hand, the distance between smooth, small single particles (of the order of 12 nm) can be below 0.1 nm which allows them to aggregate irreversibly by entering the primary well. It is also possible that the strength of these very small, primary aggregates might depend on the solubility of silica in water. As the pH and temperature increase from 20 °C to 70 °C the solubility of silica increases drastically [19] which might lead to a faster conversion of the liquid bridges containing relatively large amounts of dissolved silica into very strong solid silica bridges. Theoretically predicted effect of pH on the electrostatic repulsive forces is very small and it does not allow explanation of rather strong effect of pH on de-aggregation kinetics at elevated temperature observed experimentally. This confirms the literature suggestions that electrostatic interactions within DLVO model have serious limitations in the case of silica nano-particles.
Nomenclature A Hamaker constant C Constant defined by Eq. (3) d32 Sauter mean diameter d90- 90% (volume) of aggregates below d90% D Rotor diameter e Electron charge E Energy density h Distance between particles kB Boltzman constant N Rotor speed Po Power number V Interaction energy R Particle radius r2 Coefficient of determination t Time t95% Time necessary to break 95% of secondary aggregates below 1 μm T Absolute temperature z Valency α Constant defined by Eq. (3) ψ Electrostatic potential ε Average energy dissipation rate κ Inverse Debye length ρ Liquid density
4. Conclusions
This work is a part of the PROFORM (“Transforming Nanoparticles into Sustainable Consumer Products Through Advanced Product and Process Formulation” EC Reference NMP4-CT-2004-505645) project which is partially funded by the 6th Framework Programme of EC. The contents of this paper reflect only the authors' view. The authors gratefully acknowledge the useful discussions held with other partners of the Consortium: Bayer Technology Services GmbH; BHR Group Limited; Centre for Computational Continuum Mechanics (C3M); Karlsruhe University, Inst. of Food Process Eng; Loughborough University, Department of Chemical Eng; Poznan University of Technology, Inst. of Chemical Technology and Eng; Rockfield Software Limited; Unilever UK Port Sunlight, Warsaw University of Technology, Department of Chemical and Process Eng.
This study has revealed the two stage mechanism of deaggregation of large (+ 50 μm) secondary aggregates made of silica nano-particles (size 12 nm) and the effect of the temperature and pH on the kinetics of de-aggregation and size distributions of the primary aggregates. In the first stage, at low energy density, the large aggregates of single modal size distributions are broken by fracture and the transient size distributions slowly shift towards smaller sizes as energy density increases. The median size of the aggregates at this stage is well correlated by the size–energy model. At higher energy density, the mechanism of breakage changes into erosion of primary aggregates (50 nm to 1 μm) from the surface of the secondary aggregates. This leads to bimodal volume distributions with the first mode between 50 nm and 1 μm and the second mode between 3 and 80 μm. At a sufficiently high energy density, the secondary aggregates disappear leaving only small primary aggregates in the suspension. The effect of pH on the kinetics of de-aggregation depends on the temperature; at low temperatures the increase of pH increases the de-aggregation rate but as the temperature increases the increase of pH leads to a significant reduction of de-aggregation rate. Even at the most favourable conditions (low temperature, high pH, high energy density), breakage of the primary aggregates which were smaller than 1 μm into single nanoparticles was not observed. This implies that primary aggregation is an irreversible process, e.g., primary aggregates cannot be broken into single silica oxide nano-particles.
[J] [m] [m] [m] [C] [kJ kg− 1] [m] [J K− 1] [s− 1] [–] [J/m2] [m] [–] [min] [min] [K] [–] [–] [V] [W kg− 1] [m− 1] [kg m− 3]
Acknowledgement
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Effect of energy density, pH and temperature on de-aggregation in nano-particles/water suspensions in high shear mixer A.W. Pacek ⁎, P. Ding, A.T. Utomo School of Chemical Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom Received 22 May 2006; received in revised form 16 November 2006; accepted 3 January 2007 Available online 16 January 2007
Abstract The effect of energy input, pH and temperature on de-aggregation of hydrophilic silicon dioxide powder (particle size 12 nm) in a high shear mixer was investigated. It has been found that de-aggregation is a two step process. Initially, at low energy input very large aggregates (3–1000 μm) are gradually broken into smaller secondary aggregates (2–100 μm) of a single modal size distributions. As the energy input increases primary aggregates (0.03–1 μm) are eroded from the secondary aggregates leading to bimodal size distributions with the first mode between 0.03 μm and 1 μm corresponding to the primary aggregates and the second mode between 2 μm and 100 μm corresponding to the secondary aggregates. At a sufficiently high energy density all secondary aggregates are broken into primary aggregates however, even at the highest energy density employed the primary aggregates could not be broken into single nano-particles. The temperature and the pH affect deaggregation kinetics but do not alter de-aggregation pattern. Increasing pH at low temperature speeds up de-aggregation, whilst increasing pH at high temperature slows down de-aggregation process. © 2007 Elsevier B.V. All rights reserved. Keywords: Silica nano-particles; Suspensions; De-aggregation; Primary aggregates; Secondary aggregates; Energy density
1. Introduction Nano-particles such as titanium, aluminium or silicon oxides have wide industrial applications in manufacturing of pigments, fillers, ceramics, catalysts as well as electromagnetic and optical devices. In the majority of those applications nano-particles are processed as suspensions in different aqueous solutions and frequently the quality of final products depends on the properties of these suspensions characterized by the particle size, size distribution, shape and morphology [1]. Therefore, nano-particles which are often supplied in the form of dry powders have to be re-dispersed in aqueous solutions to give homogenous, stable dispersions and it is essential that the aggregates inherently present in dry nano-powders are broken into primary nano-particles during dispersion process. Large aggregates suspended in a fluid are broken when the hydrodynamic forces (determined by the flow field) exceed the cohesive bonds between particles or smaller aggregates. The strength of individual bonds depends on the type of nano⁎ Corresponding author. E-mail address: [email protected] (A.W. Pacek). 0032-5910/$ - see front matter © 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.powtec.2007.01.006
particles (van der Waals interactions), surface properties (wettability, electrostatic interactions) and the nature of liquid bridges within an aggregate [2]. Aggregates can be broken by normal or shear stress by erosion, e.g., a gradual removal of small fragments from the aggregates periphery or by bulk rupture, e.g., an abrupt breakage of the aggregates into a number of relatively large fragments [3]. The mechanism of breakage depends on the size of the aggregates and the energy intensity [4]. It has been postulated that as erosion occurs at low energy intensity and as the energy intensity increases particles are broken by fragmentation [5]. As the aggregates become smaller during deaggregation, surface forces become more important than mass forces and for aggregates smaller than 1 μm, surface forces are more than one million times larger than mass force [6]. Therefore, breakage of large aggregates is relatively simple, whereas breakage of aggregates smaller than 1 μm might be very difficult and it has been suggested that the particles smaller than 10 to 100 nm cannot be broken by mechanical action [7]. Dispersion and de-aggregation of nano-powders in liquids can be carried out in ball mills, ultrasonic processors and rotor– stator high shear mixers. The kinetic of de-aggregation in a high
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shear mixer is relatively easy to control and such devices are frequently used in industry to produce different types of suspensions. Therefore, in this work the effect of energy input, pH and temperature on de-aggregation of silica nano-particles in a Silverson high shear mixer has been investigated and the results are discussed below. 2. Experimental 2.1. Materials A hydrophilic fumed silicon dioxide (Aerosil® 200 V from Degussa) was supplied as a dry powder. The primary particles have density of 2200 kg/m3, SiO2 content higher than 99.8%, specific surface area of 200 m2/g and the average size of 12 nm (manufacturer's data). The powder was dispersed in de-ionized, double distilled water and when necessary pH was adjusted with H2SO4 or NaOH.
potential powder was dispersed in liquid and suspension was sheared in a high shear mixer at 8000 rpm for 1 h. Particles/aggregates size distributions were measured by particle size analyzer Mastersizer 2000 (Malvern Instruments) designed for particles in the size range between 20 nm and 2 mm which gives particles size as a diameter of the spheres with the volume equal to the volume of measured particles. In this instrument Mie theory was employed to calculate particle size distributions from the scattered laser light using the refractive index of silica particles of 1.46. The accuracy of the measurements was estimated by measuring average size and size distributions of calibrated standard particles (supplied by Duke Scientific) of 10.3 ± 0.05 μm and 97 ± 3 nm and for both sizes experimental error was well bellow 5%. The size and shape of aggregates/particles were also analyzed with an Environmental Scanning Electron Microscope (ESEM, Philips XL30). Conductivity of suspension was measured using InLab 730® conductivity probe from Metler Toledo and pH was measured using Jenway 3020 pH meter.
2.2. Nano-particles/aggregates/suspension characterization 2.3. Experimental rig Zeta potential of particles/primary aggregates was measured by Zetamaster (Malvern Instruments) designed for the particles in size range 20 nm to 3 μm. To ensure that the particles/aggregates were in the required size range, prior to the measurements of zeta
Experiments were carried out in a high shear, rotor–stator mixer (L4R from Silverson) with rotor diameter of 0.028 m, rotor height of 0.015 m and a gap between the rotor and the
Fig. 1. Transient volume distribution functions at different energy dissipation rates: (a) fragmentation of large secondary aggregates at 4.8 W kg− 1; (b) fragmentation of secondary aggregates at 21.7 W kg− 1; (c) fragmentation of secondary aggregates at 89.9 W kg− 1 during first 20 min of processing; (d) erosion of primary aggregates from the surface of secondary aggregates at 89.9 W kg− 1 after longer processing time. 5% Aerosil in water, pH 4, t = 20 °C.
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stator of 0.5 mm. To improve macro-mixing in the vessel the rotor–stator was mounted off centre in a sealed, stainless steel jacketed vessel (diameter 0.1 m, height 0.15 m) fitted with sampling port. The measured power number of this mixer is equal to 1.7 and it is in a good agreement with the literature data [8]. The temperature was controlled by the water bath and was measured by the platinum thermocouple. 2.4. Procedure In all experiments the same procedure was followed. Silica powder was pre-dispersed in water in a glass stirred vessel fitted with Rushton turbine, pH was adjusted to required value (4, 7 or 9) and suspension was stirred for 30 min. After that time the aggregate size distribution was measured in situ using video– computer–microscope system [9] and in a diluted sample using Mastersizer 2000. Next the dispersion was transferred to the high shear mixer from which the air was completely excluded and the temperature of the water bath was set either to 20 °C, 50 °C or 70 °C. The rotor speed was set to the required value (3000, 5000 or 8000 rpm) and at each speed the dispersion was sheared for 3 or 4 h. Small samples of suspension were taken every 10 min and particles/aggregates size distributions were measured. 3. Results and discussion 3.1. The effect of energy input on kinetics of de-aggregation Transient volume distributions functions measured at three different speeds (different energy dissipation rates) over the period of 3 h are summarised in Fig. 1. Fig. 1a and b illustrates the evolution of aggregate size at energy dissipation rate of 4.8 W kg− 1 (3000 rpm) and 21.7 W kg− 1 (5000 rpm) respectively and Fig. 1c and d illustrates the evolution of aggregate size distributions at energy density of 89.9 W kg− 1 (8000 rpm). From Fig. 1 it appears that de-aggregation of silica powder can be seen as a two step process. In the first step, at a relatively low energy input (short processing time at 8000 rpm or low energy dissipation rates at 3000 and 5000 rpm) large aggregates (between 5 and 500 μm) of initially dry powder shown in Fig. 2a are broken into a smaller, secondary aggregates with the size between 3 and 80 μm. The transient, single modal volume distribution functions shown in Fig 1a, b and c are approximately self preserving and as the time progresses they are gradually shifting towards the smaller size aggregates. The shape of volume distribution functions indicates that in this step fragmentation of secondary aggregates is the main mechanism of de-aggregation [5]. It is worth to stress that the smallest aggregates obtained in this step are of the order of 2 μm which is in a good agreement with literature information that the aggregates/particles larger than 1 μm are relatively easy to break. In the second step, at sufficiently high energy input (processing at 8000 rpm for approximately 30 min or more) large secondary aggregates start breaking into a small primary aggregates clearly seen in Fig. 2b. The transient volume distributions functions (Fig. 1d) become bimodal, with the first
Fig. 2. Images of silica aggregates: (a) secondary aggregates in dispersion charged into high shear mixer after premixing with Rushton turbine (time 0 in high shear mixer), width of the image is equal to 1.1 mm; (b) primary aggregates in the same dispersion after 4 h of shearing at 8000 rpm.
mode ranging from approximately 50 nm to 0.9 μm (primary aggregates) and the second mode ranging from 3 μm to 80 μm (secondary aggregates). Increase of energy input leads to a gradual reduction of volume fractions of the aggregates in the second mode and an increase of the volume fractions of aggregates in the first mode. The widths of both modes practically do not change and the median diameters are practically constant. It has to be stressed here that even at the highest investigated energy density (1500 kJ kg− 1) reported here and at even higher energy density during ultrasonication (of the order of 4300 kJ kg− 1), the aggregates in the first mode (primary aggregates) could not be broken into a single nanoparticles. The qualitative results obtained with Mastersizer 2000 were confirmed by ESEM analysis and the ESEM image in Fig. 2b clearly shows many primary aggregates in the range 50 nm to 100 nm and practically no single nano-particle (of the order of 12 nm). Similar behaviour of polymer latex nano-particles suspended in surfactant solutions was recently reported by the authors [10] who postulated that the single nano-particles in the closepacked primary aggregates are close enough to enter into the primary energy well leading to irreversible aggregation. The same explanation can be offered here. The median diameters of the secondary aggregates as a function of the processing time and as a function of the energy density are shown in Fig. 3. The median diameters at the two
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Fig. 3. Median diameters (d50) of the secondary aggregates at pH 4 and T = 20 °C as function of: (a) processing time and (b) energy density at different energy dissipation rates: ε = 4.8 W kg− 1 ( ), ε = 21.7 W kg− 1 (○), ε = 89.9 W kg− 1 (▾). Solid lines — linear regression.
•
lowest energy dissipation rates were calculated from the size distributions of all aggregates having single modal size distributions. At the highest energy dissipation rate where size distributions become bimodal (see Fig. 1d) median diameter was calculated from the second mode only e.g., median diameter in the latter case corresponds to the population of the secondary aggregates only. The reduction of the median diameter (or any other mean diameter) during de-aggregation process can be described in terms of a processing time and the energy dissipation rate or in terms of the energy density (size–energy model). For high shear mixer the energy dissipation rate was calculated from: e ¼ Pod N 3 d D5 d q
ð1Þ
and the energy density from: E ¼ ed t
ð2Þ
Experimental median diameters plotted as a function of time (Fig. 3a) in the log–log coordinates fall at three distinctive, nearly parallel straight lines with the slope ranging from 0.23 to 0.20 with coefficient of determination (r2) larger than 0.98. The reduction of the aggregates mean diameters with time during grinding/de-aggregation is frequently described by the so called size–energy model [5,7]: dav ¼ Cd E −a
ð3Þ
This simple experimental model developed in the mid-1950s is still used to calculate the mean (usually median) particle size in a different type of grinding devices. The exponent α can be interpreted as a reduction rate of the median diameter and constant C as a size of aggregate at unit specific energy input. This simple two parameter model allows the quick prediction of the mean size in a given grinding device and it also allows easy comparison between different types of grinding devices. The major disadvantage of this model is that it does not allow the prediction of size distributions and it can be used to model grinding processes where size distributions are single modal. Considering that the alternative way of predicting the mean
particle size and size distribution is the solution of the population balance model (integro-differential equation usually convertible to a set of differential equations) which is rather complex and time consuming, it is not surprising that this model has been successfully used in literature to describe the grinding of solid particles [5,7] where α of the order of 0.68–0.72 was obtained. It has also been adopted to correlate average drop size with energy input during liquid/liquid emulsification in different types of high shear mixers and in those processes in which α of the order of 0.4 was reported [11]. Fig. 3b shows that by plotting experimental d50 as a function of energy density in log–log coordinates all data points can be collapsed (with engineering accuracy) into a straight line with an average slope α = 0.26 and r2 = 0.91 which indicates that that first step of de-aggregation of silica powder (when size distributions are single modal) can also be described by this model. Whilst the breakage of secondary aggregates having the single modal size distributions can be analyzed in terms of rather simple size–energy model, the analysis of de-aggregation when size distributions become bimodal is more complex. In case of bimodal size distributions the mean sizes cannot be used to describe the changes of the population of the aggregates and in principle the evolution of bimodal size distribution with time should be analyzed within the framework of population balance model [12]. Whilst such analysis is currently performed and the results will be reported soon, the whole process of deaggregation can be qualitatively discussed in terms of transient aggregate size distributions (Fig. 1) or it can be quantified in terms of breakage of the secondary aggregates (Fig 3) combined with the analysis of the increase of a cumulative mass fraction of the primary aggregates (Fig 4). The volume fraction of the primary aggregates and d90 as a function of the processing time at 8000 rpm are summarised in Fig. 4. It is interesting to note that primary aggregates are generated only after a certain amount of energy has been put into the dispersion (or after a certain processing time, see Fig. 1c and d). As the volume fraction of the primary aggregates levels at an approximately 0.95, d90 falls below 1 μm and stays practically constant indicating that practically all the secondary
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Fig. 4. Fraction of aggregates smaller than 1 μm (primary aggregates) and d90 of whole population of aggregates as a function of processing time at pH 4, T = 20 °C and ε = 89.9 W kg− 1.
aggregates were broken. Until the secondary aggregates are present in the suspension the volume fraction of primary aggregates increases linearly with the processing time at the rate of the order of 0.01 s− 1. After d90 falls below 1 μm the rate of increase of the volume fraction of the primary aggregates reduces to approximately 0.0005. It is worth to notice that even after 4 h of processing at 8000 rpm nano-particles were not observed in the suspension (see Fig. 1d) which indicates that the primary aggregates were not broken. Similar pattern of breakage of silica aggregates built from 0.3 μm nano-particles was reported by Kusters et al. [4] where, based on similar evidence as presented above, authors concluded that silica aggregates are broken by erosion in the whole range of the energy density. In our case it appears that at low energy density fragmentation of the large aggregates occurs and at a high energy density primary aggregates are sheared from surface of the secondary aggregates.
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the analysis of aggregate size/size distributions (Figs. 5b and 6) shows that this is the case only at a low temperature. At 20 °C an increase of pH from 4 to 9 speeds up de-aggregation as can be seen from an increase of median size reduction rate α from 0.23 at pH 4 to 0.29 at pH 9. Also, at pH 9 after 10 min 30% of the secondary aggregates were broken into the primary aggregates, whereas at pH 4 after 30 min only 21% of the secondary aggregates were broken however, in both cases after 4 h of shearing 95% of the secondary aggregates were broken into the primary aggregates. Fig. 6a also shows that even when the breakage rate was highest (at pH 9, t = 20 °C) the primary aggregates (size range 50–100 nm) were not broken into a single nano-particles (average size 12 nm). As the temperature increased to 50 °C, the opposite effect of pH on breakage kinetics was observed and at pH 4 (small repulsive forces) de-aggregation was faster than at the higher pH. Further increase of temperature to 70 °C leads to a further, very strong reduction of the breakage rate at the highest pH (more than 4 h was needed to break 90% of secondary aggregates, α = 0.19) but at pH 4 the breakage rate increased (90% of secondary aggregates were broken within 55 min, α = 0.27). Whilst, as discussed above and as shown in Fig. 5b, both the temperature and the pH have a strong effect on the kinetics of deaggregation and breakage rate, the effect of both those parameters on the final size distribution of the primary aggregates is negligible. There is a pronounced difference between the transient volume distributions functions shown in Fig. 6a and b with much faster shift towards smaller particles in Fig. 6a (pH 9, t = 20 °C)
3.2. The effect of pH and temperature on kinetics of de-aggregation The change of pH of the suspension leads to a change of electrostatic charges on the aggregates surfaces which in turns affects the repulsive forces between them and might alter the kinetics of de-aggregation. To assess and to quantify the effect of pH on de-aggregation kinetics the relation between pH and the charge of the particles surface, zeta potential at different pH has been measured and the results are summarised in Fig. 5a. The combined effect of pH and temperature on the kinetics of de-aggregation is summarised in Fig. 5b and typical transient distributions during de-aggregation at pH 4 and pH 9 are compared in Fig. 6. The higher the absolute values of zeta potential, the larger the electrostatic repulsive forces and the separation of the particles becomes easier. In investigated system zeta potentials at pH 7 and higher are of the order of − 40 mV and zeta potential at pH 4 is approximately − 15 mV which indicates that the repulsive forces at pH 9 are larger than at pH 4. This suggest that deaggregation at pH 9 and pH 7 should be faster than at pH 4 but
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Fig. 5. (a) Zeta potential of primary aggregates as a function of pH for 1% ( ) and 5% (○) w/w silica in water; (b) the effect of pH and temperature on time necessary to break 95% of secondary aggregates into aggregates smaller than 1 μm; ( ) pH 4, (○) pH 7, (▾) pH 9.
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which Stern potential can be replaced with the measured zeta potential [15,17]: eWs 2 1 jh 1−tanh ð6Þ VE ¼ 2Z 2 nkT 2 kT j Hydration energy (VH) has an effective range of few nanometers and exponentially decays with distance [18]. It can be calculated from the experimental correlation [15]: VH ¼ V1 d e−h=h1 þ V2 d e−h=h2
ð7Þ
where: V1/2πR = 0.14 J/m2, h1 = 0.057 nm, V2/2πR = 5.4 × 10− 3 J/m2 and h2 = 0.48 nm are experimentally determined parameters [14]. All components of the interaction energy between two silica particles of the same diameter were calculated from Eqs. (4)–(7) and the results are summarised in Fig. 7a for the particles separated by more than 1 nm and in Fig. 7b for the particles separated by less than 1 nm. In both cases it is clear that the interactions between the silica particles in water are dominated by the repulsive hydration force. The repulsive electrostatic energies at different pH (see insert in Fig. 7a) are two orders of magnitude
Fig. 6. Transient volume distribution functions in 5% Aerosil/water suspension at rotor speed of 8000 rpm at the following processing conditions: (a) pH 9, T = 20 °C after processing times of: 0 min ( ), 10 min (○), 20 min (▾), 240 min (▽); (b) pH 4, T = 20 °C after processing times of: 0 min ( ), 20 min (○), 50 min (▾), 240 min (▽).
•
•
than in Fig. 6b (pH 4, t = 20 °C), but again the final volume distributions (after 240 min) are practically identical. In principle the effect of pH and temperature on the interactions between particles/aggregates can be analyzed using extended DLVO (Derjaguin–Landau–Verwey–Overbeek) model. In this model the interaction force between two spheres of the same radii (R) is expressed in terms of the van der Waals energy (VA), the electrostatic energy (VE) and the hydration energy (VH) between two flat surfaces [13,14]: F ¼ pRðVA þ VE þ VH Þ
ð4Þ
Attractive van der Waals energy (VA) between two flat surfaces can be calculated from [13]: VA ¼ −
A 12ph2
ð5Þ
where values of Hamaker constant for silica–silica in water reported in literature [14–16] are in the range 0.46 × 10− 20 J to 1.02 × 10− 20 J. The electrostatic repulsive energy (VE) depends on the charge on particles surface and the properties of the liquid. For constant surface potential it can be estimated from Eq. (6) in
Fig. 7. Components of interaction energy between particles: VA — attractive van der Waals energy, VE — repulsive electrostatic energy, VH – repulsive hydration energy; (a) particles separated by more than 1 nm (aggregates); (b) particles separated by less than 1 nm (single particles).
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lower than van der Waals and hydration energies and they are practically negligible both at shorter and at longer separation distances. It is possible to postulate that as the surface of aggregates is not very smooth the shortest distance separating them is probably larger than 1 nm and at such a distance the repulsive hydration energy does not allow entry into the deep primary well. This means that the secondary aggregates which are built from the primary aggregates are relatively weak and can be broken at low energy input as discussed above. Very strong repulsive hydration interactions also explain exceptional stability of the suspensions of primary silica aggregates even at pH corresponding to the iso-electric point [11,14]. On the other hand, the distance between smooth, small single particles (of the order of 12 nm) can be below 0.1 nm which allows them to aggregate irreversibly by entering the primary well. It is also possible that the strength of these very small, primary aggregates might depend on the solubility of silica in water. As the pH and temperature increase from 20 °C to 70 °C the solubility of silica increases drastically [19] which might lead to a faster conversion of the liquid bridges containing relatively large amounts of dissolved silica into very strong solid silica bridges. Theoretically predicted effect of pH on the electrostatic repulsive forces is very small and it does not allow explanation of rather strong effect of pH on de-aggregation kinetics at elevated temperature observed experimentally. This confirms the literature suggestions that electrostatic interactions within DLVO model have serious limitations in the case of silica nano-particles.
Nomenclature A Hamaker constant C Constant defined by Eq. (3) d32 Sauter mean diameter d90- 90% (volume) of aggregates below d90% D Rotor diameter e Electron charge E Energy density h Distance between particles kB Boltzman constant N Rotor speed Po Power number V Interaction energy R Particle radius r2 Coefficient of determination t Time t95% Time necessary to break 95% of secondary aggregates below 1 μm T Absolute temperature z Valency α Constant defined by Eq. (3) ψ Electrostatic potential ε Average energy dissipation rate κ Inverse Debye length ρ Liquid density
4. Conclusions
This work is a part of the PROFORM (“Transforming Nanoparticles into Sustainable Consumer Products Through Advanced Product and Process Formulation” EC Reference NMP4-CT-2004-505645) project which is partially funded by the 6th Framework Programme of EC. The contents of this paper reflect only the authors' view. The authors gratefully acknowledge the useful discussions held with other partners of the Consortium: Bayer Technology Services GmbH; BHR Group Limited; Centre for Computational Continuum Mechanics (C3M); Karlsruhe University, Inst. of Food Process Eng; Loughborough University, Department of Chemical Eng; Poznan University of Technology, Inst. of Chemical Technology and Eng; Rockfield Software Limited; Unilever UK Port Sunlight, Warsaw University of Technology, Department of Chemical and Process Eng.
This study has revealed the two stage mechanism of deaggregation of large (+ 50 μm) secondary aggregates made of silica nano-particles (size 12 nm) and the effect of the temperature and pH on the kinetics of de-aggregation and size distributions of the primary aggregates. In the first stage, at low energy density, the large aggregates of single modal size distributions are broken by fracture and the transient size distributions slowly shift towards smaller sizes as energy density increases. The median size of the aggregates at this stage is well correlated by the size–energy model. At higher energy density, the mechanism of breakage changes into erosion of primary aggregates (50 nm to 1 μm) from the surface of the secondary aggregates. This leads to bimodal volume distributions with the first mode between 50 nm and 1 μm and the second mode between 3 and 80 μm. At a sufficiently high energy density, the secondary aggregates disappear leaving only small primary aggregates in the suspension. The effect of pH on the kinetics of de-aggregation depends on the temperature; at low temperatures the increase of pH increases the de-aggregation rate but as the temperature increases the increase of pH leads to a significant reduction of de-aggregation rate. Even at the most favourable conditions (low temperature, high pH, high energy density), breakage of the primary aggregates which were smaller than 1 μm into single nanoparticles was not observed. This implies that primary aggregation is an irreversible process, e.g., primary aggregates cannot be broken into single silica oxide nano-particles.
[J] [m] [m] [m] [C] [kJ kg− 1] [m] [J K− 1] [s− 1] [–] [J/m2] [m] [–] [min] [min] [K] [–] [–] [V] [W kg− 1] [m− 1] [kg m− 3]
Acknowledgement
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