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Growth mechanisms of silicalite-1
Studies in Surface Science and Catalysis, volume 154 E. van Steen, L.H. Callanan and M. Claeys (Editors) © 2004 Elsevier B.V. All rights reserved.
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GROWTH MECHANISMS OF SILICALITE-1 Rimer, J . D . \ Kragten, D.D.^ Tsapatsis, M.^, Lobo, R.^ and Vlachos, D / ^Department of Chemical Engineering, Center for Catalytic Science and Technology, University of Delaware, 150 Academy St., Newark, DE 19716, USA. ^Department of Chemical Engineering, University of Massachusetts, Amherst, MA, USA. ABSTRACT In-situ seeded dynamic light scattering growth experiments for silicalite-1 were performed for various solution pH's and electrolyte concentrations. The pH was controlled using ratios of TPAOH (Pr4NOH) and TPABr (Pr4NBr) while maintaining a constant TPA^ concentration. The electrolyte concentration was varied through the addition of NaCl, CsCl, and CaC^. The growth rate was found to decrease linearly with increasing pH and was unaffected by the presence of electrolytes, but at high enough salt concentrations the silicalite-1 particles experience a critical coagulation concentration. The growth rates were simulated using a transport model that incorporates a DLVO interparticle potential to account for the energy barrier for particle aggregation. The model is based on the assumption that the subcolloidal particles present in the solution directly add to the crystal surface. It was shown that the model can predict the pH growth rates; however, the model overpredicts the screening effects of electrolytes in solution. Preliminary computations indicate that Stem-layer stabilization, due to the large size of adsorbing TPA, may be involved in the stability of the seeds and subcolloidal particles. This is the first time the predictive capability of the silicalite-1 growth model has been tested for a wide range of reaction conditions. Keywords: silicalite-1, crystal growth, dynamic light scattering, DLVO INTRODUCTION Within the past decade, developments in nanotechnology created opportunities for use of zeolites in novel applications, such as microreactors, optical electronics, and thin-film membranes. With this shift toward smaller scales comes the need to develop predictive and quantitative models for the growth and morphology of single crystal zeolites as a means to control both the structural and surface properties of these materials. Silicalite-1 has become the standard model for analyzing zeolite growth due in large part to its straightforward synthesis, its variable framework composition, and its interconnected network of channels, which makes this material ideal for applications such as selective thin-film membranes. However, despite the numerous investigations of the nucleation and growth of this zeolite, the understanding of its growth mechanism is still incomplete. Silicalite-1 nucleation and growth have been studied by several groups using small-angle X-ray scattering (SAXS), small-angle neutron scattering (SANS), and dynamic light scattering (DLS) [1,2,3]. It has been shown that small colloidal particles (~3 nm), oftentimes referred to as subcolloidal particles, are present in the synthesis mixture. These particles are stable and have been shown to be present throughout both the nucleation and growth processes. In a recent report we have provided evidence that suggests the shape of the subcolloidal particles is ellipsoidal [4]. Experimental and simulated X-ray diffraction (XRD) patterns and input NMR simulated annealing calculations also suggest that extracted subcolloidal particles do not have the MFI topology characteristic of silicalite-1, and appear to be disordered [5]. Several mechanisms for silicalite-1 growth have been proposed, including the growth by addition of monomeric/oligomeric silica species [6] and the direct addition of subcolloidal particles to the growing crystal [7]. The former mechanism refers to an Ostwald ripening process in which the subcolloidal particles serve as a source for monomer/oligomer silica species, that upon dissolution add to the growing crystal surface. The latter mechanism proposes that the subcolloidal particles are directly involved in the growth of silicalite-1 with the requirement that the particles, once adsorbed to the surface, undergo a fast, local rearrangement to be incorporated into the crystalline network. In 1999, de Moor et al. conducted SAXS measurements that showed a decrease in subcolloidal particle concentration with the increase in crystal size, thereby suggesting a correlation between the subcolloidal particles and silicalite-1 growth [1]. While it has been widely accepted that the subcolloidal particles play a role in the growth, it was inconclusive whether the
318 primary growth unit was the subcoUoidal particle itself or some smaller species. Nikolakis et al. provided evidence in support of the direct addition of subcoUoidal particles by combining experimental results with theoretical predictions [7]. Nikolakis et al. conducted in-situ seeded growth experiments at various temperatures in a DLS cell. Using a growth model based on electrostatic and van der Waals interactions, they were able to fit both the growth rates and activation energy for silicalite-1 growth. The growth model incorporates a DLVO potential to account for the repulsive and attractive interactions between a subcoUoidal particle and a seed. Sensitivity analyses have shown that the most significant contribution to the potential are the electrostatic interactions [7], which are influenced by several parameters, such as temperature, solution pH, electrolyte concentration, and solvent composition. In particular, according to that model the surface potential of the particles has the largest influence, suggesting the pH would have a significant effect on crystal growth. Solvent composition and electrolyte concentration are incorporated through the dielectric constant and Debye length, respectively, and are less important than the surface potential. The growth model, while predicting the effects of temperature, has not been tested for a wide range of reaction conditions - namely changes in solution pH and electrolyte concentration. In addition, the model incorporates several assumptions such as uniform surface and spherical shape, and includes limitations on the range of analysis (e.g., particles of low surface potential) through the incorporation of an analytical solution to the electrostatic interactions. Here we have performed growth experiments by changing both the pH and the electrolyte concentration - the results of which are tested against theoretical calculations to analyze the predictive capability of the growth model. Finally, the possible effects of TPA adsorption on the surface of the particles are discussed. EXPERIMENTAL The silicalite-1 seeds for the growth experiments were synthesized using the procedure reported by Hedlund et al. for the production of 60-nm particles [8]. The particles were isolated by repeated washing with distilled water and ultracentrifugation, and the resulting seeds were stored in water. The growth solution containing the subcoUoidal particles was prepared as follows. A specified amount of tetrapropylammonium hydroxide (TPAOH, 40 % Alfa Aesar) was mixed with distilled water. To this solution was added tetraethylorthosilicate (TEOS, 98 % Aldrich) dropwise to produce a clear mixture of composition 10 Si02: 9 TPAOH: 9500 H2O: 40 EtOH (referred to as the C2 composition in [7]). The solution was stirred for approximately 5 hours to ensure the complete hydrolysis of the TEOS. For the pH experiments, a mixture of TPAOH and tetrapropylammonium bromide (TPABr, 98 % Aldrich) was used to produce a mixture with composition 10 Si02: X TPAOH: Y TPABr: 9500 H2O: 40 EtOH in which X+Y=9, thereby maintaining a constant concentration of TPA (except for pH 11.7 with X+Y=10.5). For the electrolyte experiments, varying amounts of sodium chloride (NaCl, 99 % Alfa Aesar), cesium chloride (CsCl, 99 % Aldrich), and calcium chloride (CaCl2, 97 % Sigma) were added to the growth solution following the complete hydrolysis of TEOS. In-situ seeded growth experiments were performed. A small amount of the silicalite-1 seeds were added to the growth solution, which was then sonified, filtered with a 0.45 jam membrane, and de-gassed under vacuum. The pH of the solution was measured with a Coming 355 pH/ion analyzer and the conductivity of the solution was obtained with a VWR Model 2052 EC Meter. The viscosity of the solution was measured using a Cannon-Ubbelohde capillary viscometer, which was first calibrated with a Cannon viscosity standard. The temperature of the solution was maintained at 66 °C using a Cannon CT-1000 constant temperature bath. The refractive index of the growth mixture was also obtained with a C.N. Wood Model RF-600 differential refractometer. To analyze the growth of the silicalite-1 seeds, their size was monitored with DLS using a Brookhaven Instruments ZetaPALS containing a 15 mW, 635 nm solid-state laser source. The growth experiments were performed at 66 °C using the viscosity and refractive index measurements as the input DLS parameters. Measurements were taken every 30 minutes over a 16-hour period. The particle size and polydispersity were calculated using the method of cumulants, in which the average particle size was obtained from 10 runs performed over 30-second time intervals. For all samples, an initial time period was allowed for thermal equilibration prior to running the experiment.
319 The size and distribution of the silicaHte-1 seeds were analyzed with a Brookhaven Instruments BI9000AT correlator and BI200SM goniometer. The samples were placed in a decalin index-matching bath and were illuminated with a 488 nm laser source (Lexel 95 2 W Ar laser). All measurements were performed at 25 °C and a scattering angle of 90°. The intensity autocorrelation function was analyzed using a CONTIN regression method. The critical coagulation concentration (CCC) for the silicalite-1 seeds was measured in a solution with composition 9 TPAOH: 9500 H2O containing no added TEOS. To these solutions were added a small amount of 60 nm seeds to generate a sample with silica composition similar to the C2 mixture. Electrolyte solutions were prepared by dissolving NaNOa, LINOB, and La(N03)3 in distilled water. The salt solutions were then added dropwise to a measured volume of sample until the solution visually became turbid, at which point the CCC was determined from the total mass of salt solution added. GROWTH MODEL Following [7], the growth model is derived from the well-known Fuchs theory of slow coagulation, which in addition to Brownian motion accounts for an energy barrier to coagulation through the parameter O. Assuming that the subcolloidal particle irreversibly adsorbs to the surface of the seed, and that both particles are spheres, the growth rate is given by dR
Dc^^ 4
dt
R" 3
3 rexp(0(/7)/^r)^^^ '
' (/z + 7? + r j '
(1)
where rs is the radius of the subcolloidal particle, R is the evolving radius of the seed, h is the closest distance between the two particles, D is the diffusivity of the subcolloidal particle, Co is the bulk concentration of subcolloidal particles, k is the Boltzmann constant, and 0(h) is the potential energy of interaction. A DLVO model is used to describe the potential energy of interaction between the subcolloidal particle and the seed. The DLVO model consists of two contributions: an attractive potential based on van der Waal interactions and a repulsive force governed by electrostatic interactions. The electrostatics are derived through the Poisson-Boltzmann equation (PBE) using the constant surface charge boundary condition. Analytical solutions to the PBE require several approximations. In the case of sphere-sphere interactions, the Hogg-Healy-Fuerstenau (HHF) model is often used [9]. The analytical form of this model is derived through two assumptions: the Debye-Huckel approximation that limits the analysis to surfaces of low potential and the Derjaguin approximation which restricts the analysis to small interparticle distances. RESULTS AND DISCUSSION Growth experiments DLS analysis of the seeds synthesized for the growth experiments indicate a -45 nm hydrodynamic diameter with a polydispersity of 0.07. The polydispersity is relatively high in comparison to previous reported syntheses [7]; however, ranges between 0.02-0.08 indicate a narrow particle size distribution. The in-situ growth experiments were performed at 66 °C, at which temperature the C2 growth mixture has a refractive index of 1.335 and a viscosity of 5.21 x 10"* Pa-s. Figure la shows the effect of the pH on the seed diameter versus time response, conducted with the following five pH values: 10.1 (X=1.8,Y=7.2), 10.3 (X=2.7,Y=6.3), 10.6 (X=4.5,Y=4.5), 11.3 (X=9,Y=0), and 11.7 (X=10.5,Y=0). As observed in Figure la, the growth rate (slope) increases with decreasing pH. Based on DLVO theory, this trend is qualitatively expected. Silicalite-1 has a negatively charged surface density that varies with solution pH, such that as the pH is reduced the magnitude of the surface potential decreases. Within the DLVO model, the surface potential has a large effect on the electrostatic contribution; therefore, a reduction of the surface potential lowers the energy barrier of the DLVO curve that the subcolloidal particles must overcome to get incorporated, thereby resulting in a higher growth rate. The growth rate as a function of pH, shown in Figure lb, indicates a distinct correlation between the two. Within the experimental error, the growth rate is linearly dependent on the pH. This result, although unexpected a priori, is captured by the growth model, as will be discussed in the Model Prediction section.
320 0.035
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Figure 1. (a) Plot of the in-situ seeded growth curves (seed diameter vs. time) for silicalite-1 at 66 °C and five different pH values. The points represent the experimental values and the solid lines are linear regression fits, (b) Plot of the growth rate (nm/min) as function of pH. The circles represent the experimental values (the error bar indicates one standard deviation measured from four experiments at pH 11.3) along with the linear regression (solid line) while the squares represent the theoretical growth model predictions. The growth curves in Figure la are linear, as reported in previous experiments where temperature was varied [7], and exhibit a relatively small degree of scatter in the data. The scattering may be due to several factors, such as the polydispersity and the slight nonspherical shape of the seeds, as evidenced by scanning electron microscopy (SEM) images (not shown). The growth experiments performed at high pU values (e.g., pH=11.3) tend to exhibit a larger degree of scattering, which is most likely attributed to the dissolution of seeds. As shown in Figure la, the growth rate at pH=l 1.7 is nearly zero and it was observed that the seeds completely dissolve as the pH of the solution approaches 12. Monitoring the particle count with DLS over the course of the growth experiment for pH 11.7 indicates a general decrease in the number of counts over time. This would indicate that a fraction of the particles dissolve at these higher pH values until some equilibrium concentration is reached. This behavior is not observed at the lower pH values, in which the particle count increases with the size of the growing crystals, and the linear fits to the data are better. 70 , , 0.035 0.03
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o Experiment • Model
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Figure 2. (a) In-situ seeded growth curves for silicalite-1 at 66 °C and pH 11.3 at four different electrolyte concentrations. The points are experimental data and the lines are linear fits to the points, (b) Growth rate (nm/min) as a function of electrolyte concentration. The circles represent experimental values (the error bar indicates one standard deviation, as measured from four experiments with no salt) while the squares represent the theoretical growth model predictions.
321 Figure 2a shows the results of growth experiments at various monovalent (NaCl, CsCl) electrolyte concentrations compared to the standard C2 composition, which contains no inorganic salts. The error bar in growth rate is -0.002 nm/min and was calculated from four experiments performed at the C2 growth condition (pH 11.3, no added salt). Within the limits of the experimental error, it is observed that the growth rate is unaffected by the presence of electrolytes up to the concentrations depicted. Growth experiments were also performed with 0.002 M CaCl2. Even small amounts of the divalent ion result in immediate aggregation or precipitation of the seeds. It is believed that the silica-calcium complex reduces the solubility of the particles, thereby causing precipitation of the seeds in solution. The concentration of CaCb used in the growth experiment is much less than that predicted for the CCC, which would suggest that electrolyte screening effects are minimal and particle aggregation unlikely. It is possible, although unlikely, that we may be observing competitive adsorption between Ca^^ and TPA^ on the silicalite1 surface. In this case, the removal of TPA from the surface would most likely destabilize the particles, leading to the aggregation of seeds. Critical coagulation concentration The critical coagulation concentration was measured using three salts: NaNOs, LiNOa, and La(N03)3. The resulting CCC values for the 60-nm silicalite-1 seeds were in the range of 0.3-0.7 M, 0.2-0.3 M, and 0.002-0.003 M, respectively. These measurements are in agreement with the Hofmeister series, which predicts the following order of CCC values: Na^>Li^>La^^. In addition, the differences between monovalent and trivalent salts are close (within a factor of 2) to the Schultz-Hardy prediction of CCC oc z"^, where z is the valence of the ion. The DLVO model for two interacting spherical seeds was used to predict the CCC with a Hamaker constant of 1 x 10"^° J. For zeta potentials in the range of-30 to -50 mV (as measured for the silicalite-1 particles [10]) it was found that the DLVO model provides CCC values consistent with experimental observations. Measurements were performed on seed solutions aged for a period of 100 hours prior to salt addition. The CCC values for Na^ and Li^ solutions were found to be 1.33-1.66 M and 0.67-0.83 M, respectively. It is observed that aging increases the stability of the particles in solution, as evidenced by the increased CCC for the seeds. Perhaps the dissolution of seeds to produce subcolloidal particles or changes in the seed surface (e.g., TPA adsorption, charge density, etc.) induces greater stability; however, further work is required to ascertain the primary contribution to the effects of solution aging in the CCC measurements. Variations are observed from measuring the CCC by solution turbidity, as evidenced by the range of values reported above. More accurate values could be measured by monitoring particle size with DLS. Nevertheless, the measurements obtained in these experiments provide insight into silicalite-1 interactions and stability - most notably that the silicalite-1 particles aggregate at high salt concentrations, as predicted by colloidal theory. Model predictions Equation (1) was used to predict the growth rates for the pH and electrolyte in-situ seeded growth experiments. The potential energy for the interaction of a 3-nm subcolloidal particle with a 60-nm seed was calculated using four primary input parameters: dielectric constant, Debye length, Hamaker constant, and surface potential. The dielectric constant is dependent on the molar ratio of water and ethanol in the growth mixture and was calculated using the Clausius-Mossotti relationship. The Debye length is a function of electrolyte concentration, which was measured experimentally through the solution conductivity. The surface potential as a function of pH and the Hamaker constant were obtained from previous electrophoresis and modeling studies by Nikolakis et al. [7,10]. The subcolloidal particle diffusivity in the growth model was calculated from the Stokes-Einstein equation while the concentration of subcolloidal particles was used as the model fitting parameter. Over the range of pH values analyzed in the growth experiments, the surface potential varies from -35 mV (at pH 10.1) to -55 mV (at pH 11.7) [10]. Using this information, along with conductivity measurements for the Debye length, the growth rates for the pH experiments were calculated and the results are shown in Figure lb. The model predictions compare relatively well to the experimentally measured growth rates, but show considerable scatter. Two aspects that must be considered are the following. Firstly, the zeta potential measurements by Nikolakis et al. show a large degree of scatter. Secondly, the zeta potential values as a function of pH used in the modeling were extrapolated from Nikolakis' data for the growth conditions used here. The slightly nonlinear behavior of the model predictions is probably associated with these uncertainties.
322 One aspect that must be considered is the fitting parameter, CQ. Due to the small size of the subcoUoidal particles, their concentration in solution is difficult to obtain experimentally. Assuming complete hydrolysis of TEOS to form subcoUoidal particles, we can obtain an upper limit for the subcoUoidal particle concentration, which for the C2 composition is -10^^ cm"^ In the temperature experiments by Nikolakis et al., the value of Co obtained in the model fitting was -10^^ cm"^ [7]. For the pH experiments, -10^ cm"^ was the value used to obtain the fitting in Figure lb. Therefore, we observe two orders of magnitude difference between the fitted parameters for the temperature and pH experiments. It is premature to identify the origins of this disparity since the variation of Co with solution conditions is unknown at this time. According to Equation (1), the growth rate is directly proportional to the subcoUoidal particle concentration. As a result, changes in the value of Co only affect the magnitude of the calculation, but do not alter the linear trend in Figure 1 b predicted by the model. Figure 2b shows the model predictions for the growth rates at varying electrolyte concentrations. The growth rate was calculated using the experimentally measured conductivity of the solution, a constant surface potential of-50 mV (as measured for pH 11.3), and a subcoUoidal particle concentration of-10^ cm' ^ As shown in Figure 2b, there is a difference between the model predictions and the experimental results. The model predicts that the growth rate increases with increasing electrolyte concentration. If we consider DLVO theory, the addition of salts to the solution results in the screening of the electrostatic repulsion, thereby lowering the energy barrier, or potential energy of interaction. It would be expected that the reduction of this barrier would result in a net increase in the growth rate, but we do not observe such behavior experimentally. Thus the model overpredicts the screening effects of electrolytes in the silicalite-1 growth solution. Interparticle forces in zeolite growth The presence of subcoUoidal particles, growing crystals, adsorbing TPA, and ionic species in the growth solution for silicalite-1 creates a system in which particle interactions are of extreme importance, yet highly complex. The current growth model, based on the DLVO interaction potential, is able to predict the trends in growth rate at varying temperature and pH, but overpredicts the screening effects of electrolytes in solution. The DLVO model contains various approximations and assumptions that give rise to inaccurate descriptions of the growth; however, the limitations of the growth model are most likely associated with its inability to account for the effects of adsorbed TPA on the surface of the silicalite-1 particles. In this section the model approximations will be discussed along with the possible effects of TPA adsorption. The DLVO theory assumes that the interacting particles are comprised of homogeneous, smooth surfaces. In the case of silicalite-1, the presence of different crystallographic planes results in surfaces that are dissimilar. Since the distribution of surface charge sites is difficult to ascertain, it is assumed that the charges are distributed equally among the surfaces. In many cases, either a constant surface charge density or constant surface potential boundary condition is used to obtain a solution for the electrostatics. Both conditions are physically incorrect, and the true solution lies somewhere between the two. An alternative to this approach is to incorporate a complexation model that accounts for the reactions at the surface of the silicalite-1 seed. The advantage to this approach is that the surface potential can then be expressed as a function of solution conditions, such as pH. These complexation models have been used to quantify the zeta potential as a function of pH for silicalite-1 and silica particles in the presence of tetraalkylammonium ion solutions using a dual-site binding adsorption model for TPA on the surface of the particles [10,11]. The variation of zeta potential with operating conditions is a first step in developing predictive models for zeolite growth. However, molecular level information about the adsorption of TPA is equally important. The adsorption of TPA can have a significant effect on the stability of the particles in solution. Claesson et al. conducted experiments to study the forces between two mica surfaces containing adsorbed quaternary ammonium ions (e.g., TPA) [12]. It was found that short-range repulsions between the surfaces are much stronger than those predicted by the DLVO theory. As the surfaces approach, a jump in the repulsive force results at a distance of approximately twice the diameter of TPA, which suggests a monolayer of adsorbed structure directing agent is preventing the mica fi'om bonding. Claesson et al. propose that the increased repulsion results from a shift in the plane of charge from the surface - a process known as Stem-layer stabilization [13]. The TPA on the surface creates a Stem layer of thickness 5 that pushes the origin of the double-layer interaction out from the surface of the particle. This can result in the complete elimination of the primary minimum in the DLVO curve, at which point the potential energy of interaction increases indefinitely as two surfaces approach one another.
323 100
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Figure 3. (a) Graphs of the standard DLVO curves for interacting spheres having radii of 2.5, 10, 20, and 30 nm. (b) Plots of the same DLVO curves with an incorporated Stem layer. A thickness of 26 = 1.8 nm was used for the adsorption of TPA^ [12]. All calculations were performed usmg a Hamaker constant of 1.5 x 10'^^ J, a Debye length of 2.9 nm, a dielectric constant of 73, and a surface potential of-30 mV. The plots in Figure 3a show that the potential energy of interaction between two equal spheres decreases with the size of the particles. The Stern layer is incorporated into the model by shifting the plane of the particle surface in the van der Waal interactions by 25. As shown in Figure 3b, the Stern layer eliminates the primary minimum in the DLVO curve and the interactions between the particles become highly repulsive at short separations. At the conditions studied, the interactions remain repulsive for surface potentials as low as -5 mV. It is possible to obtain attractive interactions at very low surface potentials and higher values of the Hamaker constant; however, variations in salt concentration (i.e., Debye length) have little affect. One key limitation of the growth model is its inability to predict the stability of the subcolloidal particles. As shown in Figure 3a, there exists virtually no energy barrier for the interaction of two subcolloidal particles, indicating the subcolloidal particles would immediately aggregate in solution. Experimentally this is not the case, and in fact we observe that the subcolloidal particles are stable over a wide range of conditions. The Stern layer model indicates that the interactions between the subcolloidal particles are repulsive, which suggests that the TPA adsorption on the surface of these particles maybe responsible for their stability. The results of the electrolyte growth experiments may be attributed to the effects of Stem-layer stabilization. As the salt content is increased, the screening effects may be effectively eliminated by the presence of TPA on the surface; and it may be the energy required to remove this monolayer of TPA that results in the increased stability of these particles. Note that the particles do experience a CCC at high sah concentrations, which is expected from colloidal theory. At lower concentrations, though, the electrolyte content does not affect the growth of the silicalite-1 particles. Perhaps at higher concentrations we observe competitive adsorption between TPA and the ions on the surface. CONCLUSIONS Growth experiments were performed in combination with simulations to test the predictive capability of a growth model for the direct addition of subcolloidal particles to a growing silicalite-1 surface reported in [7]. In-situ seeded growth experiments were performed at various solution pH's and electrolyte concentrations. It was found that the growth rate decreases linearly with increasing pH, while the presence of electrolytes has no effect on the growth rate. At higher salt concentrations the silicalite-1 particles experience a critical coagulation concentration, as expected in colloidal theory. The growth model, based on DLVO interparticie potentials, is able to predict the pH results, but overpredicts the screening effects of electrolytes in solution. The growth model has several limitations, the most prevalent being its inability to directly account for the
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adsorption of TPA on the surface of the particles (aside from the variation of the zeta potential through the complexation model). TPA can have a strong influence on the stability of the particles, and is most likely responsible for the inability of the model to predict electrolyte effects. These analyses are the first attempt at studying the predictive capability of the silicalite-1 growth model over a wide range of reaction conditions. Future developments of the model must incorporate effects such as TPA adsorption to more accurately represent silicalite-1 growth. ACKNOWLEDGEMENT Funding for this work was provided by NSF-NIRT (CTS-0103010). We acknowledge Joe Fedeyko and Kaveri Sawant for their help in the subcolloidal particle characterization, and Norman Wagner and Eric Kaler for the use of their light scattering equipment.
REFERENCES 1. de Moor, P-P.E.A., Beelen, T.P.M., van Santen, R.A., J. Phys. Chem. B, 103 (1999) 1639-1650. 2. Watson, J.N., Iton, L.E., Keir, R.I., Thomas, J.C, Dowling, T.L., White, J.W., J. Phys. Chem. B, 101 (1997) 10094-10104. 3. Schoeman, B.J., Zeolites, 18 (1997) 97-105. 4. Fedeyko, J.M., Sawant, K.R., Kragten, D.D., Vlachos, D.G., Lobo, R.F., 14* International Zeolite Conference. 5. Kragten, D.D., Fedeyko, J.M., Sawant, K.R., Rimer, J.D., Vlachos, D.G., Lobo, R.F., J. Phys. Chem. B, (2003) accepted. 6. Schoeman, B.J., Micro. & Meso. Mater., 22 (1998) 9-22. 7. Nikolakis, V., Kokkoli, E., Tirrell M., Tsapatsis M., Vlachos D.G., Chem. Mater., 12 (2000) 845-853. 8. Hedlund, J., Mintova, S., Sterte, J., Micro. & Meso. Mater., 28 (1999) 185-194. 9. Hogg, R., Healy, T.W., Fuerstenau, D.W., Trans. Faraday Soc, 62 (1966) 1638-1651. 10. Nikolakis, V., Tsapatsis, M., Vlachos, D.G., Langmuir, 19 (2003) 4619-4626. 11. Rutland, M.W., Pashley, R.M., J. Coll. & Inter. Sci., 130 (1989) 448-456. 12. Claesson, P., Horn, R.G., Pashley, R.M., J. Coll. & Inter. Sci., 100 (1984) 250-263. 13. Israelachvili, J. Intermolecular & Surface Forces. Academic Press; New York, 2002.
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GROWTH MECHANISMS OF SILICALITE-1 Rimer, J . D . \ Kragten, D.D.^ Tsapatsis, M.^, Lobo, R.^ and Vlachos, D / ^Department of Chemical Engineering, Center for Catalytic Science and Technology, University of Delaware, 150 Academy St., Newark, DE 19716, USA. ^Department of Chemical Engineering, University of Massachusetts, Amherst, MA, USA. ABSTRACT In-situ seeded dynamic light scattering growth experiments for silicalite-1 were performed for various solution pH's and electrolyte concentrations. The pH was controlled using ratios of TPAOH (Pr4NOH) and TPABr (Pr4NBr) while maintaining a constant TPA^ concentration. The electrolyte concentration was varied through the addition of NaCl, CsCl, and CaC^. The growth rate was found to decrease linearly with increasing pH and was unaffected by the presence of electrolytes, but at high enough salt concentrations the silicalite-1 particles experience a critical coagulation concentration. The growth rates were simulated using a transport model that incorporates a DLVO interparticle potential to account for the energy barrier for particle aggregation. The model is based on the assumption that the subcolloidal particles present in the solution directly add to the crystal surface. It was shown that the model can predict the pH growth rates; however, the model overpredicts the screening effects of electrolytes in solution. Preliminary computations indicate that Stem-layer stabilization, due to the large size of adsorbing TPA, may be involved in the stability of the seeds and subcolloidal particles. This is the first time the predictive capability of the silicalite-1 growth model has been tested for a wide range of reaction conditions. Keywords: silicalite-1, crystal growth, dynamic light scattering, DLVO INTRODUCTION Within the past decade, developments in nanotechnology created opportunities for use of zeolites in novel applications, such as microreactors, optical electronics, and thin-film membranes. With this shift toward smaller scales comes the need to develop predictive and quantitative models for the growth and morphology of single crystal zeolites as a means to control both the structural and surface properties of these materials. Silicalite-1 has become the standard model for analyzing zeolite growth due in large part to its straightforward synthesis, its variable framework composition, and its interconnected network of channels, which makes this material ideal for applications such as selective thin-film membranes. However, despite the numerous investigations of the nucleation and growth of this zeolite, the understanding of its growth mechanism is still incomplete. Silicalite-1 nucleation and growth have been studied by several groups using small-angle X-ray scattering (SAXS), small-angle neutron scattering (SANS), and dynamic light scattering (DLS) [1,2,3]. It has been shown that small colloidal particles (~3 nm), oftentimes referred to as subcolloidal particles, are present in the synthesis mixture. These particles are stable and have been shown to be present throughout both the nucleation and growth processes. In a recent report we have provided evidence that suggests the shape of the subcolloidal particles is ellipsoidal [4]. Experimental and simulated X-ray diffraction (XRD) patterns and input NMR simulated annealing calculations also suggest that extracted subcolloidal particles do not have the MFI topology characteristic of silicalite-1, and appear to be disordered [5]. Several mechanisms for silicalite-1 growth have been proposed, including the growth by addition of monomeric/oligomeric silica species [6] and the direct addition of subcolloidal particles to the growing crystal [7]. The former mechanism refers to an Ostwald ripening process in which the subcolloidal particles serve as a source for monomer/oligomer silica species, that upon dissolution add to the growing crystal surface. The latter mechanism proposes that the subcolloidal particles are directly involved in the growth of silicalite-1 with the requirement that the particles, once adsorbed to the surface, undergo a fast, local rearrangement to be incorporated into the crystalline network. In 1999, de Moor et al. conducted SAXS measurements that showed a decrease in subcolloidal particle concentration with the increase in crystal size, thereby suggesting a correlation between the subcolloidal particles and silicalite-1 growth [1]. While it has been widely accepted that the subcolloidal particles play a role in the growth, it was inconclusive whether the
318 primary growth unit was the subcoUoidal particle itself or some smaller species. Nikolakis et al. provided evidence in support of the direct addition of subcoUoidal particles by combining experimental results with theoretical predictions [7]. Nikolakis et al. conducted in-situ seeded growth experiments at various temperatures in a DLS cell. Using a growth model based on electrostatic and van der Waals interactions, they were able to fit both the growth rates and activation energy for silicalite-1 growth. The growth model incorporates a DLVO potential to account for the repulsive and attractive interactions between a subcoUoidal particle and a seed. Sensitivity analyses have shown that the most significant contribution to the potential are the electrostatic interactions [7], which are influenced by several parameters, such as temperature, solution pH, electrolyte concentration, and solvent composition. In particular, according to that model the surface potential of the particles has the largest influence, suggesting the pH would have a significant effect on crystal growth. Solvent composition and electrolyte concentration are incorporated through the dielectric constant and Debye length, respectively, and are less important than the surface potential. The growth model, while predicting the effects of temperature, has not been tested for a wide range of reaction conditions - namely changes in solution pH and electrolyte concentration. In addition, the model incorporates several assumptions such as uniform surface and spherical shape, and includes limitations on the range of analysis (e.g., particles of low surface potential) through the incorporation of an analytical solution to the electrostatic interactions. Here we have performed growth experiments by changing both the pH and the electrolyte concentration - the results of which are tested against theoretical calculations to analyze the predictive capability of the growth model. Finally, the possible effects of TPA adsorption on the surface of the particles are discussed. EXPERIMENTAL The silicalite-1 seeds for the growth experiments were synthesized using the procedure reported by Hedlund et al. for the production of 60-nm particles [8]. The particles were isolated by repeated washing with distilled water and ultracentrifugation, and the resulting seeds were stored in water. The growth solution containing the subcoUoidal particles was prepared as follows. A specified amount of tetrapropylammonium hydroxide (TPAOH, 40 % Alfa Aesar) was mixed with distilled water. To this solution was added tetraethylorthosilicate (TEOS, 98 % Aldrich) dropwise to produce a clear mixture of composition 10 Si02: 9 TPAOH: 9500 H2O: 40 EtOH (referred to as the C2 composition in [7]). The solution was stirred for approximately 5 hours to ensure the complete hydrolysis of the TEOS. For the pH experiments, a mixture of TPAOH and tetrapropylammonium bromide (TPABr, 98 % Aldrich) was used to produce a mixture with composition 10 Si02: X TPAOH: Y TPABr: 9500 H2O: 40 EtOH in which X+Y=9, thereby maintaining a constant concentration of TPA (except for pH 11.7 with X+Y=10.5). For the electrolyte experiments, varying amounts of sodium chloride (NaCl, 99 % Alfa Aesar), cesium chloride (CsCl, 99 % Aldrich), and calcium chloride (CaCl2, 97 % Sigma) were added to the growth solution following the complete hydrolysis of TEOS. In-situ seeded growth experiments were performed. A small amount of the silicalite-1 seeds were added to the growth solution, which was then sonified, filtered with a 0.45 jam membrane, and de-gassed under vacuum. The pH of the solution was measured with a Coming 355 pH/ion analyzer and the conductivity of the solution was obtained with a VWR Model 2052 EC Meter. The viscosity of the solution was measured using a Cannon-Ubbelohde capillary viscometer, which was first calibrated with a Cannon viscosity standard. The temperature of the solution was maintained at 66 °C using a Cannon CT-1000 constant temperature bath. The refractive index of the growth mixture was also obtained with a C.N. Wood Model RF-600 differential refractometer. To analyze the growth of the silicalite-1 seeds, their size was monitored with DLS using a Brookhaven Instruments ZetaPALS containing a 15 mW, 635 nm solid-state laser source. The growth experiments were performed at 66 °C using the viscosity and refractive index measurements as the input DLS parameters. Measurements were taken every 30 minutes over a 16-hour period. The particle size and polydispersity were calculated using the method of cumulants, in which the average particle size was obtained from 10 runs performed over 30-second time intervals. For all samples, an initial time period was allowed for thermal equilibration prior to running the experiment.
319 The size and distribution of the silicaHte-1 seeds were analyzed with a Brookhaven Instruments BI9000AT correlator and BI200SM goniometer. The samples were placed in a decalin index-matching bath and were illuminated with a 488 nm laser source (Lexel 95 2 W Ar laser). All measurements were performed at 25 °C and a scattering angle of 90°. The intensity autocorrelation function was analyzed using a CONTIN regression method. The critical coagulation concentration (CCC) for the silicalite-1 seeds was measured in a solution with composition 9 TPAOH: 9500 H2O containing no added TEOS. To these solutions were added a small amount of 60 nm seeds to generate a sample with silica composition similar to the C2 mixture. Electrolyte solutions were prepared by dissolving NaNOa, LINOB, and La(N03)3 in distilled water. The salt solutions were then added dropwise to a measured volume of sample until the solution visually became turbid, at which point the CCC was determined from the total mass of salt solution added. GROWTH MODEL Following [7], the growth model is derived from the well-known Fuchs theory of slow coagulation, which in addition to Brownian motion accounts for an energy barrier to coagulation through the parameter O. Assuming that the subcolloidal particle irreversibly adsorbs to the surface of the seed, and that both particles are spheres, the growth rate is given by dR
Dc^^ 4
dt
R" 3
3 rexp(0(/7)/^r)^^^ '
' (/z + 7? + r j '
(1)
where rs is the radius of the subcolloidal particle, R is the evolving radius of the seed, h is the closest distance between the two particles, D is the diffusivity of the subcolloidal particle, Co is the bulk concentration of subcolloidal particles, k is the Boltzmann constant, and 0(h) is the potential energy of interaction. A DLVO model is used to describe the potential energy of interaction between the subcolloidal particle and the seed. The DLVO model consists of two contributions: an attractive potential based on van der Waal interactions and a repulsive force governed by electrostatic interactions. The electrostatics are derived through the Poisson-Boltzmann equation (PBE) using the constant surface charge boundary condition. Analytical solutions to the PBE require several approximations. In the case of sphere-sphere interactions, the Hogg-Healy-Fuerstenau (HHF) model is often used [9]. The analytical form of this model is derived through two assumptions: the Debye-Huckel approximation that limits the analysis to surfaces of low potential and the Derjaguin approximation which restricts the analysis to small interparticle distances. RESULTS AND DISCUSSION Growth experiments DLS analysis of the seeds synthesized for the growth experiments indicate a -45 nm hydrodynamic diameter with a polydispersity of 0.07. The polydispersity is relatively high in comparison to previous reported syntheses [7]; however, ranges between 0.02-0.08 indicate a narrow particle size distribution. The in-situ growth experiments were performed at 66 °C, at which temperature the C2 growth mixture has a refractive index of 1.335 and a viscosity of 5.21 x 10"* Pa-s. Figure la shows the effect of the pH on the seed diameter versus time response, conducted with the following five pH values: 10.1 (X=1.8,Y=7.2), 10.3 (X=2.7,Y=6.3), 10.6 (X=4.5,Y=4.5), 11.3 (X=9,Y=0), and 11.7 (X=10.5,Y=0). As observed in Figure la, the growth rate (slope) increases with decreasing pH. Based on DLVO theory, this trend is qualitatively expected. Silicalite-1 has a negatively charged surface density that varies with solution pH, such that as the pH is reduced the magnitude of the surface potential decreases. Within the DLVO model, the surface potential has a large effect on the electrostatic contribution; therefore, a reduction of the surface potential lowers the energy barrier of the DLVO curve that the subcolloidal particles must overcome to get incorporated, thereby resulting in a higher growth rate. The growth rate as a function of pH, shown in Figure lb, indicates a distinct correlation between the two. Within the experimental error, the growth rate is linearly dependent on the pH. This result, although unexpected a priori, is captured by the growth model, as will be discussed in the Model Prediction section.
320 0.035
(b) Experiment Model
50
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Figure 1. (a) Plot of the in-situ seeded growth curves (seed diameter vs. time) for silicalite-1 at 66 °C and five different pH values. The points represent the experimental values and the solid lines are linear regression fits, (b) Plot of the growth rate (nm/min) as function of pH. The circles represent the experimental values (the error bar indicates one standard deviation measured from four experiments at pH 11.3) along with the linear regression (solid line) while the squares represent the theoretical growth model predictions. The growth curves in Figure la are linear, as reported in previous experiments where temperature was varied [7], and exhibit a relatively small degree of scatter in the data. The scattering may be due to several factors, such as the polydispersity and the slight nonspherical shape of the seeds, as evidenced by scanning electron microscopy (SEM) images (not shown). The growth experiments performed at high pU values (e.g., pH=11.3) tend to exhibit a larger degree of scattering, which is most likely attributed to the dissolution of seeds. As shown in Figure la, the growth rate at pH=l 1.7 is nearly zero and it was observed that the seeds completely dissolve as the pH of the solution approaches 12. Monitoring the particle count with DLS over the course of the growth experiment for pH 11.7 indicates a general decrease in the number of counts over time. This would indicate that a fraction of the particles dissolve at these higher pH values until some equilibrium concentration is reached. This behavior is not observed at the lower pH values, in which the particle count increases with the size of the growing crystals, and the linear fits to the data are better. 70 , , 0.035 0.03
(b)
o Experiment • Model
n
LU
£ 0.025 ^
I
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x: 0.015 No Salt 0.128MNaCl 0.163MNaCl 0.148MCSC1
I
&
0.005 200
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800
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g^ 0
1000 0
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0G
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Figure 2. (a) In-situ seeded growth curves for silicalite-1 at 66 °C and pH 11.3 at four different electrolyte concentrations. The points are experimental data and the lines are linear fits to the points, (b) Growth rate (nm/min) as a function of electrolyte concentration. The circles represent experimental values (the error bar indicates one standard deviation, as measured from four experiments with no salt) while the squares represent the theoretical growth model predictions.
321 Figure 2a shows the results of growth experiments at various monovalent (NaCl, CsCl) electrolyte concentrations compared to the standard C2 composition, which contains no inorganic salts. The error bar in growth rate is -0.002 nm/min and was calculated from four experiments performed at the C2 growth condition (pH 11.3, no added salt). Within the limits of the experimental error, it is observed that the growth rate is unaffected by the presence of electrolytes up to the concentrations depicted. Growth experiments were also performed with 0.002 M CaCl2. Even small amounts of the divalent ion result in immediate aggregation or precipitation of the seeds. It is believed that the silica-calcium complex reduces the solubility of the particles, thereby causing precipitation of the seeds in solution. The concentration of CaCb used in the growth experiment is much less than that predicted for the CCC, which would suggest that electrolyte screening effects are minimal and particle aggregation unlikely. It is possible, although unlikely, that we may be observing competitive adsorption between Ca^^ and TPA^ on the silicalite1 surface. In this case, the removal of TPA from the surface would most likely destabilize the particles, leading to the aggregation of seeds. Critical coagulation concentration The critical coagulation concentration was measured using three salts: NaNOs, LiNOa, and La(N03)3. The resulting CCC values for the 60-nm silicalite-1 seeds were in the range of 0.3-0.7 M, 0.2-0.3 M, and 0.002-0.003 M, respectively. These measurements are in agreement with the Hofmeister series, which predicts the following order of CCC values: Na^>Li^>La^^. In addition, the differences between monovalent and trivalent salts are close (within a factor of 2) to the Schultz-Hardy prediction of CCC oc z"^, where z is the valence of the ion. The DLVO model for two interacting spherical seeds was used to predict the CCC with a Hamaker constant of 1 x 10"^° J. For zeta potentials in the range of-30 to -50 mV (as measured for the silicalite-1 particles [10]) it was found that the DLVO model provides CCC values consistent with experimental observations. Measurements were performed on seed solutions aged for a period of 100 hours prior to salt addition. The CCC values for Na^ and Li^ solutions were found to be 1.33-1.66 M and 0.67-0.83 M, respectively. It is observed that aging increases the stability of the particles in solution, as evidenced by the increased CCC for the seeds. Perhaps the dissolution of seeds to produce subcolloidal particles or changes in the seed surface (e.g., TPA adsorption, charge density, etc.) induces greater stability; however, further work is required to ascertain the primary contribution to the effects of solution aging in the CCC measurements. Variations are observed from measuring the CCC by solution turbidity, as evidenced by the range of values reported above. More accurate values could be measured by monitoring particle size with DLS. Nevertheless, the measurements obtained in these experiments provide insight into silicalite-1 interactions and stability - most notably that the silicalite-1 particles aggregate at high salt concentrations, as predicted by colloidal theory. Model predictions Equation (1) was used to predict the growth rates for the pH and electrolyte in-situ seeded growth experiments. The potential energy for the interaction of a 3-nm subcolloidal particle with a 60-nm seed was calculated using four primary input parameters: dielectric constant, Debye length, Hamaker constant, and surface potential. The dielectric constant is dependent on the molar ratio of water and ethanol in the growth mixture and was calculated using the Clausius-Mossotti relationship. The Debye length is a function of electrolyte concentration, which was measured experimentally through the solution conductivity. The surface potential as a function of pH and the Hamaker constant were obtained from previous electrophoresis and modeling studies by Nikolakis et al. [7,10]. The subcolloidal particle diffusivity in the growth model was calculated from the Stokes-Einstein equation while the concentration of subcolloidal particles was used as the model fitting parameter. Over the range of pH values analyzed in the growth experiments, the surface potential varies from -35 mV (at pH 10.1) to -55 mV (at pH 11.7) [10]. Using this information, along with conductivity measurements for the Debye length, the growth rates for the pH experiments were calculated and the results are shown in Figure lb. The model predictions compare relatively well to the experimentally measured growth rates, but show considerable scatter. Two aspects that must be considered are the following. Firstly, the zeta potential measurements by Nikolakis et al. show a large degree of scatter. Secondly, the zeta potential values as a function of pH used in the modeling were extrapolated from Nikolakis' data for the growth conditions used here. The slightly nonlinear behavior of the model predictions is probably associated with these uncertainties.
322 One aspect that must be considered is the fitting parameter, CQ. Due to the small size of the subcoUoidal particles, their concentration in solution is difficult to obtain experimentally. Assuming complete hydrolysis of TEOS to form subcoUoidal particles, we can obtain an upper limit for the subcoUoidal particle concentration, which for the C2 composition is -10^^ cm"^ In the temperature experiments by Nikolakis et al., the value of Co obtained in the model fitting was -10^^ cm"^ [7]. For the pH experiments, -10^ cm"^ was the value used to obtain the fitting in Figure lb. Therefore, we observe two orders of magnitude difference between the fitted parameters for the temperature and pH experiments. It is premature to identify the origins of this disparity since the variation of Co with solution conditions is unknown at this time. According to Equation (1), the growth rate is directly proportional to the subcoUoidal particle concentration. As a result, changes in the value of Co only affect the magnitude of the calculation, but do not alter the linear trend in Figure 1 b predicted by the model. Figure 2b shows the model predictions for the growth rates at varying electrolyte concentrations. The growth rate was calculated using the experimentally measured conductivity of the solution, a constant surface potential of-50 mV (as measured for pH 11.3), and a subcoUoidal particle concentration of-10^ cm' ^ As shown in Figure 2b, there is a difference between the model predictions and the experimental results. The model predicts that the growth rate increases with increasing electrolyte concentration. If we consider DLVO theory, the addition of salts to the solution results in the screening of the electrostatic repulsion, thereby lowering the energy barrier, or potential energy of interaction. It would be expected that the reduction of this barrier would result in a net increase in the growth rate, but we do not observe such behavior experimentally. Thus the model overpredicts the screening effects of electrolytes in the silicalite-1 growth solution. Interparticle forces in zeolite growth The presence of subcoUoidal particles, growing crystals, adsorbing TPA, and ionic species in the growth solution for silicalite-1 creates a system in which particle interactions are of extreme importance, yet highly complex. The current growth model, based on the DLVO interaction potential, is able to predict the trends in growth rate at varying temperature and pH, but overpredicts the screening effects of electrolytes in solution. The DLVO model contains various approximations and assumptions that give rise to inaccurate descriptions of the growth; however, the limitations of the growth model are most likely associated with its inability to account for the effects of adsorbed TPA on the surface of the silicalite-1 particles. In this section the model approximations will be discussed along with the possible effects of TPA adsorption. The DLVO theory assumes that the interacting particles are comprised of homogeneous, smooth surfaces. In the case of silicalite-1, the presence of different crystallographic planes results in surfaces that are dissimilar. Since the distribution of surface charge sites is difficult to ascertain, it is assumed that the charges are distributed equally among the surfaces. In many cases, either a constant surface charge density or constant surface potential boundary condition is used to obtain a solution for the electrostatics. Both conditions are physically incorrect, and the true solution lies somewhere between the two. An alternative to this approach is to incorporate a complexation model that accounts for the reactions at the surface of the silicalite-1 seed. The advantage to this approach is that the surface potential can then be expressed as a function of solution conditions, such as pH. These complexation models have been used to quantify the zeta potential as a function of pH for silicalite-1 and silica particles in the presence of tetraalkylammonium ion solutions using a dual-site binding adsorption model for TPA on the surface of the particles [10,11]. The variation of zeta potential with operating conditions is a first step in developing predictive models for zeolite growth. However, molecular level information about the adsorption of TPA is equally important. The adsorption of TPA can have a significant effect on the stability of the particles in solution. Claesson et al. conducted experiments to study the forces between two mica surfaces containing adsorbed quaternary ammonium ions (e.g., TPA) [12]. It was found that short-range repulsions between the surfaces are much stronger than those predicted by the DLVO theory. As the surfaces approach, a jump in the repulsive force results at a distance of approximately twice the diameter of TPA, which suggests a monolayer of adsorbed structure directing agent is preventing the mica fi'om bonding. Claesson et al. propose that the increased repulsion results from a shift in the plane of charge from the surface - a process known as Stem-layer stabilization [13]. The TPA on the surface creates a Stem layer of thickness 5 that pushes the origin of the double-layer interaction out from the surface of the particle. This can result in the complete elimination of the primary minimum in the DLVO curve, at which point the potential energy of interaction increases indefinitely as two surfaces approach one another.
323 100
- • 0 - - r=2rjiiiTL r=10iini - • - - r=2.5 run
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60
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Figure 3. (a) Graphs of the standard DLVO curves for interacting spheres having radii of 2.5, 10, 20, and 30 nm. (b) Plots of the same DLVO curves with an incorporated Stem layer. A thickness of 26 = 1.8 nm was used for the adsorption of TPA^ [12]. All calculations were performed usmg a Hamaker constant of 1.5 x 10'^^ J, a Debye length of 2.9 nm, a dielectric constant of 73, and a surface potential of-30 mV. The plots in Figure 3a show that the potential energy of interaction between two equal spheres decreases with the size of the particles. The Stern layer is incorporated into the model by shifting the plane of the particle surface in the van der Waal interactions by 25. As shown in Figure 3b, the Stern layer eliminates the primary minimum in the DLVO curve and the interactions between the particles become highly repulsive at short separations. At the conditions studied, the interactions remain repulsive for surface potentials as low as -5 mV. It is possible to obtain attractive interactions at very low surface potentials and higher values of the Hamaker constant; however, variations in salt concentration (i.e., Debye length) have little affect. One key limitation of the growth model is its inability to predict the stability of the subcolloidal particles. As shown in Figure 3a, there exists virtually no energy barrier for the interaction of two subcolloidal particles, indicating the subcolloidal particles would immediately aggregate in solution. Experimentally this is not the case, and in fact we observe that the subcolloidal particles are stable over a wide range of conditions. The Stern layer model indicates that the interactions between the subcolloidal particles are repulsive, which suggests that the TPA adsorption on the surface of these particles maybe responsible for their stability. The results of the electrolyte growth experiments may be attributed to the effects of Stem-layer stabilization. As the salt content is increased, the screening effects may be effectively eliminated by the presence of TPA on the surface; and it may be the energy required to remove this monolayer of TPA that results in the increased stability of these particles. Note that the particles do experience a CCC at high sah concentrations, which is expected from colloidal theory. At lower concentrations, though, the electrolyte content does not affect the growth of the silicalite-1 particles. Perhaps at higher concentrations we observe competitive adsorption between TPA and the ions on the surface. CONCLUSIONS Growth experiments were performed in combination with simulations to test the predictive capability of a growth model for the direct addition of subcolloidal particles to a growing silicalite-1 surface reported in [7]. In-situ seeded growth experiments were performed at various solution pH's and electrolyte concentrations. It was found that the growth rate decreases linearly with increasing pH, while the presence of electrolytes has no effect on the growth rate. At higher salt concentrations the silicalite-1 particles experience a critical coagulation concentration, as expected in colloidal theory. The growth model, based on DLVO interparticie potentials, is able to predict the pH results, but overpredicts the screening effects of electrolytes in solution. The growth model has several limitations, the most prevalent being its inability to directly account for the
324
adsorption of TPA on the surface of the particles (aside from the variation of the zeta potential through the complexation model). TPA can have a strong influence on the stability of the particles, and is most likely responsible for the inability of the model to predict electrolyte effects. These analyses are the first attempt at studying the predictive capability of the silicalite-1 growth model over a wide range of reaction conditions. Future developments of the model must incorporate effects such as TPA adsorption to more accurately represent silicalite-1 growth. ACKNOWLEDGEMENT Funding for this work was provided by NSF-NIRT (CTS-0103010). We acknowledge Joe Fedeyko and Kaveri Sawant for their help in the subcolloidal particle characterization, and Norman Wagner and Eric Kaler for the use of their light scattering equipment.
REFERENCES 1. de Moor, P-P.E.A., Beelen, T.P.M., van Santen, R.A., J. Phys. Chem. B, 103 (1999) 1639-1650. 2. Watson, J.N., Iton, L.E., Keir, R.I., Thomas, J.C, Dowling, T.L., White, J.W., J. Phys. Chem. B, 101 (1997) 10094-10104. 3. Schoeman, B.J., Zeolites, 18 (1997) 97-105. 4. Fedeyko, J.M., Sawant, K.R., Kragten, D.D., Vlachos, D.G., Lobo, R.F., 14* International Zeolite Conference. 5. Kragten, D.D., Fedeyko, J.M., Sawant, K.R., Rimer, J.D., Vlachos, D.G., Lobo, R.F., J. Phys. Chem. B, (2003) accepted. 6. Schoeman, B.J., Micro. & Meso. Mater., 22 (1998) 9-22. 7. Nikolakis, V., Kokkoli, E., Tirrell M., Tsapatsis M., Vlachos D.G., Chem. Mater., 12 (2000) 845-853. 8. Hedlund, J., Mintova, S., Sterte, J., Micro. & Meso. Mater., 28 (1999) 185-194. 9. Hogg, R., Healy, T.W., Fuerstenau, D.W., Trans. Faraday Soc, 62 (1966) 1638-1651. 10. Nikolakis, V., Tsapatsis, M., Vlachos, D.G., Langmuir, 19 (2003) 4619-4626. 11. Rutland, M.W., Pashley, R.M., J. Coll. & Inter. Sci., 130 (1989) 448-456. 12. Claesson, P., Horn, R.G., Pashley, R.M., J. Coll. & Inter. Sci., 100 (1984) 250-263. 13. Israelachvili, J. Intermolecular & Surface Forces. Academic Press; New York, 2002.