Aerosol Science and Technology Morphological

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Morphological Characterization of Fumed Silica Aggregates David Boldridge

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Cabot Microelectronics Corporation , Aurora, Illinois, USA Published online: 22 Jan 2010.

To cite this article: David Boldridge (2010) Morphological Characterization of Fumed Silica Aggregates, Aerosol Science and Technology, 44:3, 182-186, DOI: 10.1080/02786820903499462 To link to this article: http://dx.doi.org/10.1080/02786820903499462

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Aerosol Science and Technology, 44:182–186, 2010 Copyright © American Association for Aerosol Research ISSN: 0278-6826 print / 1521-7388 online DOI: 10.1080/02786820903499462

Morphological Characterization of Fumed Silica Aggregates David Boldridge

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Cabot Microelectronics Corporation, Aurora, Illinois, USA

Transmission electron microscopy has been used to evaluate the primary particle size distributions and aggregate structure of a fumed silica sample. The primary particle size distribution within an individual aggregate is narrow, with a geometric standard deviation of ∼1.2. The distributions of maximum diameters, projected areas, and average primary particle diameters were all skewed, and all were better described by a log-normal distribution than a normal distribution. The data confirmed the fractal description of the fumed silica aggregate structure, with a mass fractal dimension of 1.86. The fractal prefactor or lacunarity was evaluated from the aggregate volume and the maximum particle dimension, and found to have a value of 0.83 for this sample.

INTRODUCTION Fumed silica is a widely used material, finding applications in such diverse fields as semiconductor polishing (CMP), rheology control of paint, reinforcement of silicone rubber, and anti-caking of pharmaceutical powders. In each case, the open internal structure of the fumed silica particle is believed to contribute to its efficiency when compared to more compact silica morphologies. The open internal structure of fumed silica is produced as a result of the formation process, where silicon tetrachloride and other volatile silanes are oxidized in a high temperature flame aerosol process to produce small, essentially spherical primary particles which subsequently collide to form rigid, covalently bonded aggregates. The general model for formation consists of four steps (Pratsinis 1998): (1) oxidation or hydrolysis of the volatile silane to form silicon dioxide or silicic acid vapor;

Received 19 September 2009; accepted 16 November 2009. The assistance of Dr. Changhui Lei and Dr. J.G. Wen in acquiring the images is gratefully acknowledged. Portions of this work were carried out at the Center for Microanalysis of Materials, University of Illinois, which is partially supported by the U.S. Department of Energy under grant DEFG02-91-ER45439. Valuable discussions with Dr. John Parker, Dr. Edward Remsen, Dr. Michael Zimmerman, Mr. Anthony Shaw, and Mr. Thomas Weaver are gratefully acknowledged. The author appreciates permission of Cabot Microelectronics Corporation to publish this work. Address correspondence to David Boldridge, Cabot Microelectronics Corporation, 870 Commons Drive, Aurora IL 60504, USA. E-mail: david [email protected]

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(2) rapid nucleation; (3) coagulation of nuclei and/or monomer with complete sintering to form primary particles; and (4) coagulation of the primary particles with partial sintering to form aggregates. Amorphous silica sinters by a viscous flow mechanism, which is driven by reduction in surface energy and resisted by viscous forces. Sintering produces spherical primary particles until the particles become too large for the balance of surface tension and viscous resistance. Subsequent collisions produce aggregates with partial sintering producing necks between the primary particles. The viscosity of silica is a very strong function of temperature, allowing the temperature, temperature gradient, and reactant concentration to be adjusted to produce the desired primary particle and aggregate dimensions within a limited range. The chaotic nature of the flame process dictates that the values of primary particle and aggregate dimensions are averages, with a significant spread around the mean. An example of a representative fumed silica particle is shown in Figure 1. Fumed silica has been the subject of many studies, including early determinations of its fractal nature by microscopy (Forrest and Witten 1979; Forsman et al. 1987) and by scattering (Hurd 1990; Hurd and Flower 1988; Martin et al. 1986; Schaefer and Hurd 1990; Schaefer et al. 1991). The details of the structure within the aggregate, however, are less well understood. There is a general consensus that the primary particle size distribution within an aggregate is much narrower than that between aggregates, but a quantified measure of the width of the distribution is not available. This work reports the primary size distribution within the individual aggregates and across a sample of aggregates as determined from TEM images. This information, when coupled with conventional TEM image measures of aggregate dimension and projected area, allows the determination of the fractal dimension and the fractal prefactor. EXPERIMENTAL SECTION The sample used for this work was a commercial 25% solids chemical mechanical polishing (CMP) slurry. This slurry is an R aqueous dispersion of Cab-O-Sil L-90 fumed silica electrostatically stabilized at alkaline pH. This material was diluted ∼1000:1 into absolute ethanol with sonication. One drop of the dilute ethanolic dispersion was placed on a 200 mesh carbon coated copper grid and allowed to dry.


MORPHOLOGY OF FUMED SILICA AGGREGATES

FIG. 1.

Fumed silica aggregate composed of multiple primary particles.

Electron micrographs were obtained using a Jeol 2010 LaB6 microscope operated at 200 kV bias at the Center for Microanalysis of Materials, Frederick Seitz Materials Research Laboratory, University of Illinois at Urbana/Champaign. Images were R captured using a high resolution Gatan Smartscan Model 832 digital camera at a magnification of 12000 times. A 200 nm scale bar was imposed on each image using the microscope’s internal calibration. To minimize the chance of bias, every microscope field that included at least one complete particle which appeared to be an individual aggregate was accepted. An adequate number of images was acquired to allow the characterization of more than 250 individual aggregates. R Image analysis was performed using Optimas 6.2 image analysis software by Media Cybernetics. Image pre-processing consisted of applying a median 5 × 5 smoothing operation, manual selection of a threshold gray scale value, and examination to verify that the particle boundary had been accurately identified, with manual adjustments where necessary. Image selection represents the most subjective portion of this work. Aggregates that extended beyond the edge of the viewing frame were automatically rejected, as were particles where the contrast was too low to accurately separate particle from background. Particles which appeared to be undispersed clusters of aggregates were also excluded. These particles were identified as having two or more distinct regions with dramatically different primary particle sizes, typically differing in size by at least 50%. Particles of this sort are observed often in cases of poor dispersion or when TEM grids have high particle concentrations, suggesting that these particles are the result of incomplete dispersion or physical agglomeration during drying. An example of a particle which was excluded as a clus-

183

FIG. 2. Image showing cluster of multiple aggregates. Continuous regions of distinctly different primary particle size were taken as an indication of an undispersed cluster of aggregates.

ter is shown in Figure 2. On occasion, an aggregate would be identified containing one or more isolated primary particles of unusual size; these aggregates would not be excluded by this criterion. The software automatically determined the maximum dimension of the particle (dm ) and the projected area for each aggregate. Primary particle diameters were determined by manually measuring 12 well defined primaries in each aggregate. Primaries were deemed “well defined” if their edges were distinct and a chord could be readily drawn across the center of the primary particle. In an effort to reduce bias, the particle was visually divided into three roughly equal size portions, and an attempt was made to extract 4 primary particle diameters more or less evenly spaced across each portion. In some cases, 12 well defined primaries could not be identified in the image due to small aggregate size or factors such as obscuration of primary particle edges by other primaries above or below, loss of spherical outline, inability to unambiguously identify the center of the primary and multiple junctions with other primaries preventing the identification of opposing edges. In cases where 12 well defined primaries could not be identified, all well defined primaries were measured. Aggregates with less than 7 well resolved primaries were excluded from the primary particle analysis. Primary particle diameters were linked to the aggregate area and maximum dimension so that data manipulations could be executed on an aggregate by aggregate basis. RESULTS AND DISCUSSION A total of 267 particles were complete, had adequate contrast, and appeared to be individual aggregates. Of these, 237


184

D. BOLDRIDGE

FIG. 3. Distribution of average primary particle diameters. Dashed line is log-normal fit to the data, solid line is normal fit to the data.

had at least 7 measurable primary particles, 191 of which had the full complement of 12 well defined primary particles. The arithmetic average primary particle diameter was calculated for each individual aggregate with 7 or more resolvable primaries. The averages ranged from 9 nm to 55 nm. The distribution of these average diameters was approximately log-normal, with a geometric mean average diameter of 22.5 nm and a geometric standard deviation of 1.45. The distribution is shown in Figure 3 with log-normal and normal distributions superimposed for comparison. The distribution of primary particle diameters within a given aggregate was much narrower. Analysis of variance (ANOVA) demonstrated that approximately 80% of the variance was attributable to aggregate to aggregate variation, while approximately 20% of the variance was attributable to variation within a single aggregate. Assuming a log-normal distribution, this corresponded to a geometric standard deviation of approximately 1.2 for the primary particles within a single aggregate. Given the wide variation of average diameters between different aggregates, each primary particle was normalized to the average diameter for its aggregate. The distribution of the normalized population is shown in Figure 4 with a normal distribution superimposed for comparison. The normal distribution has a mean of 1 and a standard deviation of 0.17. The narrowness of the distribution precludes a clear distinction between normal and log-normal distributions. The mean primary particle diameters and aggregate projected areas can be used to calculate the number of primary particles in each aggregate, as described below. The number of primaries in each aggregate was used to calculate a number weighted distribution of primary particle diameters. The number weighted primary particle size distribution is better described by a log-normal distribution than a normal

FIG. 4. Distribution of normalized primary particle diameters with superimposed Gaussian distribution. Gaussian distribution has a mean of 1 and standard deviation of 0.17.

distribution, with a geometric mean diameter of 18.6 nm and a geometric standard deviation of 1.37. The distributions of both the aggregate maximum diameter and the aggregate projected area were better described by log-normal distributions than by normal distributions. The distribution of maximum diameters for all 267 aggregates was described by a geometric mean diameter of 213 nm and a geometric standard deviation of 1.91. To simplify the comparison, each projected area was converted to the diameter of a circle with equivalent area. The distribution of circular equivalent diameters was described by a geometric mean diameter of 132 nm with a geometric standard deviation of 1.79. The minimum number of primaries in an aggregate (Nmin ) can be calculated from the projected area of the individual aggregate and the average diameter of the primaries associated with that aggregate Nmin =

A π d¯ 2pp 4

[1]

where A is the projected area of an individual aggregate and dpp is the average primary particle diameter for that aggregate. This number of primary particles necessarily underestimated the true number due to obscuration of primaries in the back by those in the foreground. A widely used empirical method to correct for obscuration is given by Equation (2) (Medalia 1967; Medalia and Heckman 1969; Megaridis and Dobbins 1990; Oh and Sorensen 1997). A multiplier of 1.15 and a power of 1.09 (Koylu et al. 1995) has been used in this work, giving an average number of primaries of 99 per aggregate. Npp = 1.15N1.09 min

[2]

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MORPHOLOGY OF FUMED SILICA AGGREGATES

Equation (2) was used to calculate the number of primary particles for each individual aggregate. The specific surface area of the sample was then calculated according to Equation (3).

Ni π d2i

i

SSA =


i

Ni π d3i /6

[3]

A specific surface area of 100 m2 /g was calculated, in good agreement with the manufacturer’s reported value of 90 m2 /g determined by gas adsorption. The defining equation for the mass of a fractal particle is generally taken to be Npp = k0

Rg rpp

Df [4]

where Npp is the number of primary particles in the aggregate, k0 is a geometric factor, Rg is the radius of gyration, rpp is the radius of the primary particle and Df is the mass fractal dimension. Other researchers have argued that one can use the maximum dimension (dm ) instead of the radius of gyration (Adachi et al. 1998; Adachi and Ooi 1990; Oh and Sorensen 1997) in the equation Npp = k

dm dpp

access to k and Df for a population of particles formed by a single consistent mechanism. Figure 5 shows a scatter-plot of log of Npp vs. log dm /dpp . A linear fit to the slope gave the mass fractal dimension of 1.86. The geometric factor (k ) can be estimated using the intercept of the plot, giving a value of 0.83. Oh and Sorensen (1997) have shown that

Df [5]

While Equations (4) and (5) are more commonly encountered in estimations of single particle properties, they give a direct

FIG. 5. Fractal analysis of fumed silica aggregates. Mass fractal dimension is 1.86 and geometric pre-factor is 0.83.

Rg ≈ 0.69Rmax

[6]

k0 = 0.69−Df k

[7]

which implies

giving a value of 1.65 for k0 . This is very close to the value found (Oh and Sorensen 1997) for point contact between spherical primaries. CONCLUSIONS The distribution of average primary particle sizes between aggregates was reasonably described by the log-normal distribution with a geometric mean diameter of 22.5 nm and a geometric standard deviation of 1.45. The number weighted primary particle size was also reasonably described by the lognormal distribution, with a geometric mean diameter of 18.6 nm and a geometric standard deviation of 1.37. The reduction in mean particle size and narrowing of the distribution reflect a tendency toward more primaries per aggregate when the average primary particle is smaller. The distribution of primary particle sizes within a given aggregate was found to be substantially narrower than that between aggregates, with a geometric standard deviation of ∼1.20 or an arithmetic standard deviation of ∼17%. The distribution is sufficiently narrow that a meaningful distinction between log-normal and normal distributions cannot be made. If one accepts the qualitative particle formation mechanism outlined in the introduction, the narrow within-aggregate primary particle size distribution suggests that the temperature and concentration are relatively constant within the local region where an individual aggregate forms. The larger differences observed between primary particle sizes in different aggregates suggest that different regions of the flame exhibit variations in temperature and/or concentration on a more macroscopic scale. The within-aggregate primary particle size distribution is substantially narrower than the self preserving size distribution, consistent with a physical narrowing of the distribution as described by Heine and Pratsinis (2007). The fact that the number of primaries increases as primary size decreases would be consistent with a model where the reactant concentration was fairly consistent in different parts of the flame, while local variations in temperature determined the average primary particle diameter. The narrow primary particle size distribution is comparable to that found for soot generated in diesel engines (Lapuerta et al. 2009; Park et al. 2004), although these authors suggest


186

D. BOLDRIDGE

that the variation between aggregates is narrower than has been observed here. The work reported here clearly confirmed the applicability of the fractal description to the fumed silica particle. The mass fractal dimension was found to be 1.86, consistent with a formation mechanism described by diffusion limited cluster-cluster aggregation (Meakin 1984). This value compares favorably with previous microscopy studies of commercial fumed silica (Forrest and Witten 1979; Forsman et al. 1987; Narayanan et al. 2007), where values of 1.55–1.78 were reported. These authors did not compensate for partial obscuration, resulting a modest underestimation of the fractal dimension. The value found here is also consistent with that determined by the majority of the scattering studies, where values typically range from 1.7–2.0 for Cab-OSil (Hurd et al. 1987; Schaefer and Hurd 1990; Schaefer et al. 1991) and 2.0–2.2 for Aerosil (Kammler et al. 2004; Khan and Zoeller 1993; Saint-Michel et al. 2002; Wengeler and Nirschl 2007; Wengeler et al. 2007) fumed silicas. This analysis further confirmed that the pre-factor is on the order of 1, as has often been assumed for fractal aggregates. This information can be used to provide the experimental basis for confirmation of fractal aggregate formation models. It is interesting to note that the maximum dimension of the smallest aggregates (approximately 50 nm) is expected to overlap with the diameter of the largest primary particles. The degree of overlap is modest for the maximum diameter, but will be more significant for the diameter of an equivalent circle or the radius of gyration. The fact that the two domains are not completely distinct is expected to complicate the analysis of scattering data. REFERENCES Adachi, Y., Kobayashi, M., and Ooi, S. (1998). Applicability of Fractals to the Analysis of the Projection of Small Flocs. J. Coll. Interface Sci. 208:353–355. Adachi, Y., and Ooi, S. (1990). Geometrical Structure of a Floc. J. Coll. Interface Sci. 135:374–384. Forrest, S. R., and Witten, T. A. (1979). Long Range Correlations in Smoke Particle Aggregates. J. Phys. A. 12:L109–L117. Forsman, J., Harrison, J., and Rutenberg, A. (1987). Elasticity of a Percolation System: Silica Smoke. Can. J. Phys. 65:767–771. Heine, M. C., and Pratsinis, S. E. (2007). Polydispersity of Primary Particles in Agglomerates Made by Coagulation and Sintering. J. Aerosol Sci. 38:17–38. Hurd, A. J. (1990). Scattering from Gas-Phase Synthesized Particles. Mat. Res. Soc. Symp. Proc. 172:3–13. Hurd, A. J., and Flower, W. L. (1988). In Situ Growth and Structure of Fractal Silica Aggregates in a Flame. J. Coll. Interface Sci. 122:178– 192.

Hurd, A. J., Schaefer, D. W., and Martin, J. E. (1987). Surface and Mass Fractals in Vapor-Phase Aggregates. Phys. Rev. A: 35:2361–2364. Kammler, H. K., Beaucage, G., Mueller, R., and Pratsinis, S. E. (2004). Structure of Flame-Made Silica Nanoparticles by Ultra-Small-Angle X-Ray Scattering. Langmuir 20:1915–1921. Khan, S., and Zoeller, N. (1993). Dynamic Rheological Behavior of Flocculated Fumed Silica Suspensions. J. Rheol. 37:1225–1235. Koylu, U. O., Faeth, G. M., Farias, T. L., and Carvalho, M. G. (1995). Fractal and Projected Structure Properties of Soot Aggregates. Combust. Flame 100:621– 633. Lapuerta, M., Ballesteros, R., and Martos, F. (2009). The Effect of Diesel Engine Conditions on the Size and Morphology of Soot Particles. Int. J. Vehicle Des. 50:91–106. Martin, J. E., Schaefer, D. W., and Hurd, A. J. (1986). Fractal Geometry of Vapor-Phase Aggregates. Phys. Rev. A: Gen. Phys. 33:3540–3543. Meakin, P. (1984). Diffusion-Limited Aggregation in Three Dimensions: Results from a New Cluster-Cluster Aggregation Model. J. Coll. Interface Sci. 102:491–504. Medalia, A. I. (1967). Morphology of Aggregates I. Calculation of Shape and Bulkiness Factors; Application to Computer Simulated Random Flocs. J. Coll. Interface Sci. 24:393–404. Medalia, A. I., and Heckman, F. A. (1969). Morphology of Aggregates II. Size and Shape Factors of Carbon Black Aggregates from Electron Microscopy. Carbon 7:567–582. Megaridis, C. M., and Dobbins, R. A. (1990). Morphological Description of Flame-Generated Materials. Combust. Sci. Technol. 71:95–109. Narayanan, R. A., Thiyagarajan, P., Zhu, A.-J., Ash, B. J., Shofner, M. L., Schadler, L. S., Kumar, S. K., and Sternstein, S. S. (2007). Nanostructural Features in Silica-Polyvinyl Acetate Nanocomposites Characterized by Small-Angle Scattering. Polymer 48:5734–5741. Oh, C., and Sorensen, C. M. (1997). The Effect of Overlap between Monomers on the Determination of Fractal Cluster Morphology. J. Coll. Interface Sci. 193:17–25. Park, K., Kittelson, D., and McMurry, P. (2004). Structural Properties of Diesel Exhaust Particles Measured by Transmission Electron Microscopy (TEM): Relationships to Particle Mass and Mobility. Aerosol Sci. Technol. 38:881– 889. Pratsinis, S. E. (1998). Flame Aerosol Synthesis of Ceramic Powders. Prog. Energy Combust. Sci. 24:197–219. Saint-Michel, F., Pignon, F., and Magnin, A. (2002). Rhéométrie Et Structure De Dispersions De Silices Nanométriques Dans Du Polyol. Rhéologie 2:31–38. Schaefer, D. W., and Hurd, A. J. (1990). Growth and Structure of Combustion Aerosols. Fumed Silica. Aerosol Sci. Technol. 12:876–890. Schaefer, D. W., Olivier, B. J., Hurd, A. J., Beaucage, G. B., Ivie, J. J., and Herd, C. R. (1991). Structure of Combustion Aerosols. J. Aerosol Sci. 22:S447– S450. Wengeler, R., and Nirschl, H. (2007). Turbulent Hydrodynamic Stress Induced Dispersion and Fragmentation of Nanoscale Agglomerates. J. Coll. Interface Sci. 306:262–273. Wengeler, R., Wolf, F., Dingenouts, N., and Nirschl, H. (2007). Characterizing Dispersion and Fragmentation of Fractal, Pyrogenic Silica Nanoagglomerates by Small-Angle X-Ray Scattering. Langmuir 23:4148– 4154.