Toward Modeling Clay Mineral Nanoparticles: The Edge Surfaces of

Article pubs.acs.org/JPCC

Toward Modeling Clay Mineral Nanoparticles: The Edge Surfaces of Pyrophyllite and Their Interaction with Water David M. S. Martins,†,‡ Marco Molinari,‡ Mário A. Gonçalves,† José P. Miraõ ,§ and Stephen C. Parker*,‡ †

Departamento de Geologia and CeGUL, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal Department of Chemistry, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom § Geoscience Department and Geophysics Centre, University of Évora, Apt. 94, 7002-554 Évora, Portugal ‡

S Supporting Information *

ABSTRACT: The basal surfaces of phyllosilicate minerals have been widely studied, whereas the edge surfaces have received little attention. However, in order to simulate complete clay particles at the atomic level, the modeling of edge surfaces becomes crucially important, and such surfaces are likely to be far more active. We used a combination of quantum and potential based techniques to evaluate the structure of the edge surfaces of pyrophyllite and their interaction in an aqueous environment. These include {110}, {100}, {010}, {1̅10}, {130}, and {1̅30}. We found that the CLAYFF force field is an effective model for reproducing the DFT results. Furthermore, the results show that, for this notorious natural hydrophobic clay, all edge surfaces show hydrophilic behavior and that the precise structure of water above these surfaces is influenced by both the presence of hydroxyl groups and under-coordinated surface Al atoms; this will impact both geological processes where natural clays are involved and processes where such clays act as primary retention barriers to the dispersion of contaminants.

1. INTRODUCTION Adsorption of cations onto mineral surfaces depends primarily on conditions such as pH, ionic strength, and the nature of cations and substrates.1−10 The adsorption process is an active mechanism in controlling the fate of metal ions in natural waters with significant influence on their concentration in solution, providing a natural way to control effects such as bioavailability and toxicity.11−14 Mineral substrates respond differently to ion adsorption, and the search for the best adsorbent is a way to provide a supply of effective low-cost materials for engineering applications in environmental remediation. Phyllosilicates participate in a vast range of geological processes from sedimentary to metamorphic, from the surface to the deep Earth environment, making them of particular interest in geology and geochemistry. Furthermore, as they are abundant minerals with versatile chemical and physical properties (diversified structural arrangements and compositions), they are valuable materials for environmental and technological applications such as catalysts15−17 and adsorbents.11,18,19 Most of these properties are related to the surface chemistry of phyllosilicates in aqueous solution due to their small particle size, large surface area, and chemically active surfaces.20 This highlights the importance of computational studies of clay minerals where atomistic insights can be gained from small (few thousand atoms) to large-scale (millions of atoms) simulations.21 Phyllosilicates are highly anisotropic crystals with the basal surface {001} dominating the crystal morphology, resulting in thinly platy crystals with modest edge surface development (Figure 1). Whereas basal surfaces may have a permanent charge due to isomorphic substitutions, and thus its charging is pH© 2014 American Chemical Society

independent, edge surfaces develop a variable charge in solution with changing pH.23,24 These variable charge edge surfaces have long been pointed out as being the most important for the formation of metal surface complexes.12,25 Besides, it was shown that acid dissolution of phyllosilicates develops mostly through edge surfaces, as unlike basal surface edge surface bonds are valence unsaturated.26 The study of edge surface structures in phyllosilicates dates back to Schofield and Samson when the first model for 1:1 phyllosilicates was proposed as a way to understand their stability as colloidal particles.27,28 Applying crystal growth principles, such as periodic bond chain theory (PBC) of Hartman and Perdock,29−31 White and Zelazny developed and extended this model to dioctahedral 2:1 phyllosilicates.24 Building on such work, Bleam and co-workers claimed insufficient detail of the edge surfaces and refined the model to include surface relaxation.23 Clearly, pyrophyllite has been used throughout as the preferred model because of its chemical simplicity and lack of isomorphic substitutions, but nevertheless retaining enough similarities to extend to other dioctahedral 2:1 phyllosilicates, including swelling clays. The refined single-crystal structure of the pyrophyllite-1Tc of Lee and Guggenhein has become a benchmark ever since.22 Simulation studies of phyllosilicate published so far have been mostly concerned with basal surfaces because modeling the edge surfaces poses additional problems much harder to overReceived: July 16, 2014 Revised: October 10, 2014 Published: October 30, 2014 27308

dx.doi.org/10.1021/jp5070853 | J. Phys. Chem. C 2014, 118, 27308−27317

The Journal of Physical Chemistry C

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the structural features of the edge surfaces. The role of clay edge surfaces on contaminant adsorption and retention is not fully understood, and it could be key for understanding the properties of clay nanoparticles. In order to establish the structural, energetics, and dynamical properties, we report on a broad selection of edge surfaces of pyrophyllite, which exploit a variety of structural features covering, therefore, a large variety of dynamical behaviors of the edge surfaces in the aqueous environment. Initially, atomistic calculations were used to generate a series of low Miller index surfaces, from which a training set was selected and further optimized with ab initio simulations. This, in turn, was used to compare and validate the force field, which then enabled us to extend the set of edge surfaces studied and to include the interaction between neighboring edge surfaces. The structure and surface energy of the edge surfaces are predicted, as is the equilibrium shape of the crystal. Interfaces between a variety of edge surfaces and water were studied using classical molecular dynamics. A detailed discussion of the results is presented in section 3 after a brief description of the methodology in section 2.

2. METHODOLOGY We applied a combination of force field based and quantum mechanical methods as part of an established procedure.38,39 Initially, DFT was used to validate the reliability of the force field, and subsequently, force field based molecular dynamics was used to evaluate the water profile at the clay pyrophyllite surfaces. 2.1. Models. The METADISE code was used to generate all simulated structures.40 Bulk pyrophyllite comprises stacking layers of composition Al4(Si8O20)(OH)4 in which octahedrally coordinated Al atoms are linked by hydroxyl groups and sandwiched between sheets of SiO4 tetrahedra (Figure 1i, ii). The distance between layers is the interlayer spacing.22 The edge surface slabs generated are based on the unit cell of pyrophyllite that is conventionally orientated and not following the montmorillonite-like;41 examples are given in Figure 1iii. We considered the basal {001} surface and the edge {010}, {1̅10}, {110}, {100}, {130}, and {1̅30} surfaces. Unlike the {001} surface, which is a perfect cleavage, the edge surfaces need saturation of the dangling bonds, requiring rebonding or hydroxylation. As mentioned in the Introduction, the edge surface composition (number of hydroxyl groups or water molecules adsorbed) varies with the environment (pH, T). In our study, we have accounted for the hydroxylation of all dangling bonds on the Si only, which we define as “low coverage” (LC), while accounting for both Si and Al we define as “high coverage” (HC). This does not include the pH effect explicitly. We have only considered the neutral surfaces of pyrophyllite to rationalize the behavior of pyrophyllite at PZC. Work has been done on the edge surfaces of pyrophyllite to understand their composition as discussed in the Introduction. Details of the surface structures will be discussed in the relevant section in the Results and Discussion. 2.2. Electronic Optimization. The basal {001} and selected edges surfaces of pyrophyllite were simulated using the DFT methodology as implemented in the VASP code.42 The edge surfaces were the {010}, {1̅10}, {110}, {100}, {130}, and {1̅30} surfaces and included different terminations. Electronic exchange and correlation were treated within the GGA-PBE functional.43 Core electrons were treated using the Blö chl projector augmented wave (PAW) method,44 whereas the valence electrons were represented by a plane-wave basis set truncated at an extended energy cutoff of 500 eV. The electronic self-

Figure 1. Structure of pyrophyllite (i) viewed along the a axis, (ii) viewed along the b axis, and (iii) staggered from the c axis, with the {130} plane in blue, {1̅30} plane in green, and {100} plane in red; schematic illustration of a hexagon in black.22 O, Si, and Al are in red, yellow, and dark pink, respectively.

come.26,32,33 The very few atomistic models dealing with edge surfaces in the literature are based on the work of White and Zelazny, and Bleam et al.26,34 Bickmore et al. focused on structure relaxation of the {010} and {110} edge surfaces of pyrophyllite with different protonation schemes.26 The outcome of the optimization structures for the different protonation schemes and comparison of the minimized energies of the structures obtained concurred with those of White and Zelazny and Bleam et al.23,24 Churakov investigated the structure and sorption properties of the {010}, {100}, {110}, and {130} edge surfaces of pyrophyllite.34 Sorption was studied for the case of low and high water presence. In the former case, the surface energies were identical, whereas, in the latter, the {110} and {100} had much lower surface energy than the {010} and {130} edge surfaces. Furthermore, Churakov studied the reactivity of the edges, determining that the highest proton affinity of the {100}, {110}, and {130} facets were on the Al-O-Si sites, whereas, in the case of the {010} facet, it is on the Al-OH groups.35 Tazi et al. also addressed the acidity of edge sites on the {010} facet.36 On inclusion of thermodynamic integration, they found that the relaxation of surface water molecules contributed to the enthalpy, resulting in the Si-OH groups being the most acidic, closely followed by Al-OH2 sites, whereas the Al-OH group did not deprotonate in water. It is also worth mentioning the work of Liu et al., who studied the interface between water and the {010} and {110} edge surfaces of 2:1 phyllosilicates, also based on PBC theory.37 The outcome showed that, at aqueous environment, the octahedral sites of the edge surfaces can adopt both the 5- and 6-fold coordination states with only a small free-energy difference. This, therefore, shows the complexity of the problem. The objective of this work is to study edge surfaces of pyrophyllite, including their structure, energetics, and their interaction with the aqueous environment. Ultimately, it provides the dynamical behavior of the solvent at the mineral− water interface, which can be linked to surface structural features, and hence the relationship between the dynamical behavior and 27309



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consistent field convergence was set to 10−5 au, whereas the forces on the ionic relaxation where set to be less than 0.01 eV/Å. 3D boundary conditions were used throughout the simulations, and no dipole correction was needed as the simulated structures are symmetric. Both standard DFT and the DFT-D2 dispersion correction method of Grimme45 were employed, and the results will be discussed in the Results and Discussion section. First, bulk pyrophyllite was simulated in a 4 × 2 × 1 supercell using the Γ point and allowing relaxation of the ions and the volume. From the minimized bulk, the basal and edge surfaces were simulated using the slab method.46 The basal {001} and the edge surfaces were generated from a 4 × 2 × 2 bulk unit cell. A TOT layer was then removed, introducing a vacuum gap of approximately 13 Å, which was sufficient to avoid interaction between images. It is worth noting that the basal {001} surface has a vacuum gap only in one direction, whereas the edge surfaces have two vacuum gaps, one between the {001} surfaces (∼13 Å in the Y direction of Figure 2) and the other between the edge

Table 1. Optimized Unit Cells for the DFT-D2 and DFT Calculationsa {010} {1̅10} {110} {100} {130} {1̅30}

DFT-D2 DFT DFT-D2 DFT DFT-D2 DFT DFT-D2 DFT DFT-D2 DFT DFT-D2 DFT

X (Å)

Z (Å)

γ (deg)

15.66 15.52 10.49 10.38 10.44 10.35 9.07 8.98 18.06 17.91 18.14 17.96

47.20 46.94 33.38 33.22 33.37 33.33 35.36 35.26 35.61 35.54 35.62 35.41

101.19 79.35 82.79 93.64 93.90 83.06 91.90 91.88 98.72 79.83 79.37 98.23

DFT-D2 have the same Y, α, and β of 19.97 Å, 90°, and 90°. DFT without the inclusion of the van der Waals corrections have the same Y, α, and β of 19.97 Å, 90°, and 90°. a

an oxygen atom from the surface and the other one saturating the Al or Si surface atoms to at least a 4-fold coordination. There was no need to modify the CLAYFF model as two OH groups introduced have the same charge of an O atom removed. The stability of the surfaces was addressed by calculating the surface energy, which is the energy required to cleave the surface in water. We followed the procedure described by Greń et al.49 and more recently by Crabtree et al.50 using a water correction factor of −2.78 eV. The water correction factor differs from that used by Greń et al., as we are using a different potential model, but it is very similar to that used by Crabtree et al., as we are using the same potential model. The difference resides only in the energy of liquid water used (the heat of vaporization of liquid water, 0.4229 eV,51 was removed from the gas phase value of −14.2769 eV, calculated using DFT). The structure and the calculated surface energies will be detailed in section 3.2. In addition to comparing the calculated structures and surface energies, we were also able to visualize their relative stability by predicting the equilibrium morphology via Wulff constructions utilizing the METADISE code.40,52 2.4. Potential Based Molecular Dynamics. The surfaces simulated using DFT were also employed in molecular dynamics simulations as implemented in the DL_POLY code.53 Three dimensional boundary conditions were applied to symmetrical slabs, which were immersed in water. Each slab was approximately 20 Å thick with a 20 × 20 Å2 surface area. The water solvent was added to both sides, but we ensured that there was at least 60 Å of water on one side to allow the water structure to relax to the bulk equilibrated structure. A vacuum gap of approximately 70 Å was used to avoid interactions between the images. Four Al atoms in the center of the slab were fixed to prevent the slab from moving during the simulations. The electrostatic interactions of the system were evaluated using the Ewald method to a precision of 10−5, and the potential cutoff was 8.5 Å. All simulations were run for a total of 4 ns at 300 K with a time step of 0.5 fs in the NVT ensemble and with the Nose Hoover thermostat. Dynamical properties of the aqueous solution were evaluated at the mineral−water interface, including the diffusion, the density, the residence time, and orientation of water at the edge surfaces of pyrophyllite, as in Crabtree et al.50 The calculations of the diffusion coefficient of water were performed by a well-

Figure 2. Schematic representation of the simulated unit cell. O, H, Si and Al in red, white, yellow, and dark pink, respectively.

surfaces (∼15 Å in the Z direction of Figure 2). The reason for choosing isolated slabs was that the interaction between the slabs is dominated by van der Waals and we wanted to model the relaxations on the edges of the slabs independently of any assumption made concerning interslab interactions. All edges surfaces were simulated using the Γ point. Convergence tests revealed that the difference in surface energy calculated using the Γ point and a greater k-points density was less than 0.02 J m−2 well below the uncertainties in the approach. Convergence in the energy was reached for 3 k-points for the {010} and 2 k-points for {1̅10}, {110}, {100}, {130}, and {1̅30}. The thickness of the slabs was 15, 16, 8, 20, 19, and 17 Å for the {010}, {1̅10}, {110}, {100}, {130}, and {1̅30}, respectively. The optimized unit cells for the DFT-D2 and DFT are summarized in Table 1. 2.3. Potential Model Energy Minimization. Several potential models have been derived for clay minerals. We have chosen the CLAYFF model, which has proven to be flexible and reliable in order to simulate clay mineral.47 The Lennard-Jones parameters were mixed as in Zeitler et al.48 The METADISE code was employed to generate the surfaces from the minimized bulk pyrophyllite. The bulk structure was cut along Miller indices to obtain a series of edge surfaces following a previously reported methodology (see Table S5, Supporting Information, for the stacking structure of species of the edge surfaces).49 All generated terminations were then hydroxylated to maintain charge neutrality; hydroxylation was achieved following adsorption of dissociative water at both the Si and Al sites. In practice, we introduce two OH groups, one substituting 27310



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Table 2. Structural Comparison of the Most Important Bond Lengths of the Bulk Structures Si−O bonds bulk

exp DFT-D2 FF

Al−O bonds

O−H bonds

Si−Otet

Si−Ooct

Al−(OH)−Al

Al−Ooct

Al−(O−H)−Al

1.612(6) 1.629(2) 1.556(2)

1.633(1) 1.648(2) 1.634(4)

1.889(18) 1.897(1) 1.984(2)

1.924(3) 1.933(2) 1.970(3)

n.a. 0.970(0) 1.026(0)

established methodology.38,54,55 Although other methodologies are available, as discussed in Boţan et al. and Liu et al.,56,57 a detailed comparison is not yet available. The former methodology was used in our study to calculate the diffusion coefficients of water with a correlation time of 12.5 ps and a slice thickness of 0.5 Å; this was sufficient to smooth the diffusion curve without losing local features. The z-density was calculated with slice thickness of 0.1 Å. The residence time of water on surface Al and OH sites was calculated within a 2.7 and 3.7 Å radii.

without vdW surface energies are underestimated compared to their DFT-D2 counterpart by approximately 0.27 J m−2 (Figure 3). Generally, the HC is more stable than the LC when the same

3. RESULTS AND DISCUSION We start by presenting a comparison of the bulk structure of the pyrophyllite crystal structure as determined via force field and electronic structure simulations (section 3.1). The use of ab initio simulations on a training set of low Miller index edge surfaces with two different water coverages allowed us to assess the performance of the force field (section 3.2.1). Being confident that it was mimicking the ab initio simulations, we were then able to extend the set of edge surfaces studied as well as study the interaction between neighboring surfaces. In section 3.3, we show the calculated crystal morphology. Finally, section 3.4 concludes the results, presenting the clay mineral−water interfaces. 3.1. Bulk Structures. The geometry-optimized bulk lattice structures are in excellent agreement with previous work on bulk pyrophyllite; a detailed comparison is provided in Table S1 (Supporting Information).26,34,58,59 As expected, the greater deviations came from the DFT calculations without van der Waals (vdW) correction and are mainly due to inaccurate representation of the c axis because the pyrophyllite layers are held together predominantly by vdW forces, as previously reported. CLAYFF has been proven to reproduce the bulk structure quite accurately while preserving a certain degree of flexibility.47 Considering the most important bond lengths, both theoretical approaches were in agreement with the experimental bulk structure. The deviations in bond length for DFT-D2 calculations averaged 0.7% and CLAYFF 2.7% (Table 2). 3.2. Structure of the Edge Surfaces. 3.2.1. Evaluation of the Potential Model: DFT vs CLAYFF. We used quantum mechanical modeling to calculate the surface structure and stability for a range of low Miller index edge surfaces of pyrophyllite stabilized by LC and HC. These were also modeled with empirical potentials. One of the problems with comparing the relative stability using a potential model, which has a different charge on the oxygen of water, hydroxide, and oxide, is that the difference in electron affinity needs to be accounted for as the composition changes. Comparison with DFT gives this single parameter, which is then used throughout. In terms of stability, as expected, the basal surface remains the most stable, and interestingly, all of the edge surfaces have reasonably small surface energies (details in Table S2, Supporting Information). The edge surfaces that were calculated to have the lowest surface energies are in agreement with the work of previous studies in terms of the structures and stability.24,26,34 However, there are some differences. The DFT

Figure 3. Surface energies for the different terminations of the symmetrical edge slabs ordered in terms of DFT-D2 energies. Purple denotes DFT results without vdW correction, brown denotes DFT-D2 results, and green denotes force field results (ran on the edge surfaces coming from DFT-D2 because the ones without vdW correction are very similar and so have been removed for clarity). *Note: there was proton transfer and two hydroxyl groups combined so that water was formed during the DFT minimization process.

termination of the surface is taken into account. This is not the case for the {110}a, where the DFT-D2 for the HC (Figure 4ii) stabilized a bridging OH (Si-OH-Al), leading to a different structure compared to the DFT without van der Waals (Figure 4i), where the bridging OH is pulled by the surface Si, leaving the surface Al atoms 4-fold coordinated. This is in contrast to the

Figure 4. Slab structure of edge surfaces as calculated with DFT of: (i) {110}a at HC without vdW correction; (ii) {110}a at HC with DFT-D2 correction; and (iii) {110}b at HC regardless of vdW correction. 27311



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Figure 5. Surface energies for the different terminations of the edge surfaces as generated from energy minimization simulations. Blue denotes low coverage, red denotes high coverage, and gray denotes the perfect cleavage {001} surface.

surfaces, we do not find a simple relationship, and we suggest that, as the surfaces have a complex structure, there is a complex relationship with stability dependent on the local geometries of the surfaces and not on the Miller index of the surface. For example, the extra stabilization obtained from the interaction with neighboring surfaces is on average 0.20 J m−2. Breaking this difference down in terms of water coverage, we see a clear dichotomy, as the average is 0.29 and 0.07 J m−2 for LC and HC, respectively. This, in turn, leads to LC surfaces being more stable. This does not apply to the {010}b and {130}b as they do not follow this trend, varying by 0.27 and 0.36 J m−2, respectively. In the case of the {010}b at the HC, the OH is no longer bridging the Al and Si surface atoms and binds to the neighboring TOT layer via H-bonding, thus allowing for a greater stabilization (Figure 6i). For the {130}b, the low stability of the LC is due to a 3-fold coordinated surface Al, which becomes 4-fold coordinated at HC (Figure 6ii).

behavior of the {110}b, which retains the higher stability of the HC because the OH is no longer bridging the Al and the Si surface atoms, leading to 5-fold coordinated surface Al and trihydroxylated surface Si (Figure 4iii). The remaining structures can be found in the Supporting Information (see Tables S4 and S5). Figure 3 shows that the surface energy potential based calculations are in excellent agreement with DFT results. There are only two exceptions in terms of relative stability, even though the structures are indistinguishable, {110}b with both LC and HC, and {1̅30}a with LC. Structural comparison of the atomic distances for the edge surfaces for both theoretical approaches showed that they were virtually identical with the bond length average deviation being 3.7%. The largest deviations (maximum of 7.8%) were found for H-bonds, which are notoriously difficult to model accurately (Table S3, Supporting Information). In the specific case of the edge surfaces where water is trapped above Al sites, the most important structural features behave similarly to all the other surfaces. The exception to this occurs for the {11̅ 0}b surface at HC, where the CLAYFF model optimizes the water location to be approximately 0.5 Å further away from the surface than that calculated by DFT. Considering the minor structural and electronic deviations, CLAYFF is an excellent model to study such systems. 3.2.2. Inclusion of the Interslab Interactions. As the potential model reproduces the DFT results, we applied it to surfaces with only vacuum in one direction. This reproduces a more natural environment where the stacked TOT layers can interact with each other. Here, we have also extended the study to the nonmirror surfaces; in the majority of these cases, they could only be hydroxylated with one HC scheme to ensure 4-fold coordinated surface Si atoms. The surface energies of these calculations are shown graphically in Figure 5, and in general, they are more stable compared to the slab energies (Figure 3). A benchmark for understanding the difference between the energy of the slabs (section 3.2.1) and of the surface can be drawn using the {001} basal surface where the stacking of TOT layers stabilizes the structure of about 0.14 J m−2. When correlating the structural features found on our edge surfaces (water coverage, CN of surface Al atoms) with the energetics, for all the different edge

Figure 6. Surface structures of edge surfaces as calculated with CLAYFF of: (i) {010}a at HC; (ii) {130}b at LC; with the black shape highlighting the features discussed.

3.3. Crystal Morphologies. One way of displaying the relative stability of different surfaces is to calculate the equilibrium morphology using the Wulff approach.49,60 Naturally occurring pyrophyllite crystals are most abundant as anhedral, with euhedral crystals being rare and varied in form.61,62 The latter crystals, however, tend to exhibit a pseudo-hexagonal habit, as has long been postulated.34,63 We predict that the morphology of well-shaped crystals of pyrophyllite, according to Wulff’s method,52 can take a range of shapes related to the pseudo-hexagonal habit, depending on the edges that are taken into account (Figure 7). 27312



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latter case can be explained by looking at the coordination of the adjacent triple hydroxylated Si atoms, which disrupts the formation of these localized adsorption sites. This is also the only case where we see that the hydroxylation of Si prevents the water molecules from reaching the surface Al sites; none of the other surfaces indeed show similar behavior, not even the {110} (E), where the OH groups of the surface Si point toward the middle of the TOT layer where the surface Al atoms are placed. In general, the z-density profiles show that the effect of the surface disappears around 10 Å above the surface. Usually, the water solvent presents a three-layer structure. The first layer corresponds to the strongly bound water, the second to the water interacting with the hydroxylation of the Si and Al surface atoms, and the third is the result of the interaction of water molecules with the second hydration layer. A clear example is the {110} (G) surface. However, things are usually more complicated as the peaks can split. This is due to the different heights of the hydroxylation of the two T and the O layers in the TOT. The {100} (D) is an example where there are three peaks (with the second centered at 0 Å) comprising the first hydration layer, corresponding to the hydration of the T, O, and T layers in the TOT slab. The second hydration layer shows a peak centered at 3 Å, which broadens, covering part of the third peak of the first hydration layer. The third hydration layer is above 5 Å and is very smooth. This can be also seen on the {100} (B), {130} (J), and {1̅30} (L). The rest of the surfaces show two peaks, sometimes interpenetrating like in the {110} (E/F) and {130} (I), sometimes well-defined as in {010} (A) and {1̅10} (K). Another striking feature that can be extrapolated by the timeaveraged plots is the undulating structure of water on some edge surfaces, which is then reflected into the z-density profiles by having water below the top OH group (at 0 Å). The water density follows smoothly the curvature of the surface, for example, on the {010} (A), {110} (G/H), {130} (J), and {1̅30} (L). The orientation order parameter of the water on the edge surfaces of pyrophyllite in Figure 8 can be used to discriminate between surfaces. If equal to 1, the orientation is perpendicular to the surface, whereas, if equal to −0.5, it is parallel, and if 0, it is disordered. The water is preferably adsorbed parallel to the surface in the {100} (B/C/D), {110} (E), {130} (J), {1̅10} (K), and {1̅30} (L), where there are surface Al that can strongly interact with the water molecules. The only exception is the {110} (F), where there is the hydroxyl layer covering the surface to interact with the water molecules. The remaining surfaces show a flat orientation of the water molecules just above the surface that rapidly becomes disordered; this includes the {010} (A), {110} (G/H), and {130} (I). There is no particular relation between the water orientation and the coordination of the surface Al atoms. Figure 9 displays the diffusion coefficients of water as a function of distance from the surface; 0 Å represents the surface hydroxyl groups. The diffusion coefficients were normalized relative to the water bulk; indeed, all values converge to the bulk value between 10 and 15 Å above the surface. As the diffusion of water shows similarity within all the edge surfaces, we will discuss only the representative {010} and {100}. None of them behave like hydrophobic {001}, where a large peak just above the surface indicates that the water exchange between the two hydration layers above the surface of pyrophyllite is fast.38 All edge surfaces, however, appear to be hydrophilic. Unlike the case of montmorillonite and muscovite where the hydrophilicity is related to the charged TOT layer, the hydrophilicity of the edge

Figure 7. Crystal morphology calculated using (i) the LC and (ii) HC surfaces.64

These calculations agree largely with the work of Churakov.34 Calculations enabled us to break down the morphology in terms of the edge surfaces; hence, we present the shapes in terms of LC and HC. The commonality is the presence of a dominating {010} and a smaller {130} edge surface. The other notable feature is that there is an obvious difference that relates to the {100} and {110} that tend to be competing as only one or the other appears. The variation in stability with the hydroxylation may also point to the fact that there is not a single observed morphology and hence is subject to kinetic control. 3.4. Clay−Water Interface. Clays in natural samples are covered in water, so understanding the clay−water interfaces is important, and we want to ascertain if there is a relationship between the dynamics and the stability of the surfaces or with the local geometries of the surface atoms, as suggested in section 3.2. Therefore, the dynamical properties of the mineral−water interface cannot only include the most stable edge surfaces. As we wanted to explore a variety of different “edge” environments to access different “edge” behavior and features we simulated in MD the structures of the edge surfaces used in DFT, which covers a wide range of local structural environments. An important topic is the structure and dynamic behavior of the water solvent above the surfaces; the z-density profiles, the diffusion coefficients, and the residence time of water can help address the water structure imposed by the surface because this will ultimately affect the adsorption behavior. Structural features of the clay−water interface can be visualized by using the z-density profile, the time-averaged density plot, and the orientation of the water molecules above the edge surfaces, which are shown in Figure 8. It is clear that the presence of the surface affects the structure of the water.38 We can rationalize it in terms of Al surface sites: depending on the surface terminations, Al ions can be in a 4-, 5-, or 6-fold coordination. Usually, 4-fold coordinated Al atoms strongly interact with water molecules as there is no steric encumbrance preventing adsorption of the molecules; this usually yields selfcontained spots in the time-averaged images just above the Al atoms and small and sharp peaks on the z-density profile, as displayed markedly by the edge {010} (A), {100} (C), and {110} (E/G). The {130} (I) has 4-fold coordinated Al atoms but does not show these kinds of localized (lattice like) adsorbed sites. The reason is that, unlike the previously mentioned surfaces where the single hydroxylated Al is anchored to the surface with three bonds, here the Al has two OH groups that span the space above the surface, impeding the formation of these localized adsorption sites. The same behavior can be seen for surfaces that have 5- or 6-fold coordinated Al atoms. For example, lattice-like adsorption sites can be seen on the {100} (B/D), {110} (H), {130} (J), {1̅10} (K), and {1̅30} (L), whereas it does not happen on the {110} (F), even though the Al is singly hydroxylated. The 27313



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Figure 8. Time-averaged water density along the b (top) and c (middle) directions; O, OH, H, Si, and Al water density in red, gray, white, yellow, dark pink, and cyan. z-density profile of OW (orange), HW (pale blue), and Al (purple) along with the orientation (pale pink) of the water molecules as a function of the distance from the edge surfaces of pyrophyllite (bottom); density on an arbitrary scale while orientation on the right side scale, and the surface top OH determine the 0 Å. A, B, C, D, E, F, G, H, I, J, K, L, and M are {010}b-HC, {100}a-HC, {100}b-HC, {100}a-LC, {110}a-HC, {110}bHC, {110}a-LC, {110}b-LC, {130}b-HC, {130}a-LC, {1̅10}b-LC, and {1̅30}a-LC, respectively. 27314



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network is somewhat unfulfilled, extending the exchange of water molecules and lowering the residence time. This is also consistent with the residence time of surfaces with Al sites that are doubly hydroxylated; τr becomes 35 ± 13 ps, considerably lower than all the others and clearly highlighting that both the coordination of the sites and the presence of hydroxyl groups can be used to explain the values. As mentioned early in the paper, there are cases where water molecules are trapped at some surface Al sites (Al-Ow). When water molecules are trapped in these sites, the residence time is considerably high, being 1650 ± 464 ps. These events are not particularly fast, having determined just 11 cases in the whole of our simulation. We can also calculate the residence time of water on the surface hydroxyl groups. It is clear that the water behaves differently depending on the position of the OH groups. τr is high when the hydroxyl groups belong to surface Al atoms (AlOH) and to surface Si atoms (Si-OH) particularly close to the surface Al atoms with and without hydroxylation, assuming values of 162 ± 29, 154 ± 47, and 173 ± 60 ps, respectively. These values are in line with those seen for the water residence time on surface Al atoms. Again, when water is trapped, τr for those adjacent OH increases to 710 ± 194 ps. However, when we consider the OH belonging to surface Si atoms that stretch into the water solvent (those that are further to the Al atoms), τr is particularly low with a value of 23 ± 2 ps. Finally, as the edge surfaces show concave patterns (when one of the T or O layers of the TOT layer is inward), the OH groups belonging to these Si and Al atoms have similar residence times (46 ± 9 and 47 ± 7 ps, respectively). The residence times of water on surface Al and OH groups of edges surface of pyrophyllite compared to that of water on the {001} surface (8 ± 1 ps) is much higher, indicating again the hydrophilicity of these surfaces.38 Furthermore, high values of residence time of water on surfaces is seen also for typically hydrophilic surfaces of goethite, an iron oxyhydroxide, where residence times of water in the first and second water layers are between 384 and 23 ps,65 and calcite, calcium carbonate, where residence times of water in the first and second water layers are between 300 and 53 ps.55 Comparison of the overall dynamical features also yielded no strong correlation with any structural or energetic feature, thus indicating that these are ruled locally rather than globally.

Figure 9. Normalized diffusion coefficients of water at the edge surfaces of pyrophyllite as a function of the distance from the surface top OH at 0 Å. Dx in blue, Dy in red, Dz in green, and Dxyz in purple. A and B are {010}b-HC and {100}a-HC, respectively.

surfaces is due to the presence of both undercoordinated surface Al atoms and hydroxyl groups. The diffusion coefficients are fairly similar across all edges; the ratios are usually smaller than 1, implying that water molecules interact with the surface and the effect of this interaction is the reduction of their mobility. Features can be seen close to the surface of some edges, for example, {010} (A) in Figure 9 and {100} (C), {110} (E/F/H), and {11̅ 0} (K) in Figure S1 (Supporting Information), usually more pronounced in Dx, suggesting that the exchange across the hydration layers above the surface is somewhat slower than the movement of water within each layer. This is in agreement with the observation of strongly interacting adsorbed water molecules with undercoordinated surface Al atoms. Other surfaces present a quite smooth diffusion, for example, {100} (B) in Figure 9 and {130} (J) and {13̅ 0} (L) in Figure S1 (Supporting Information). A further parameter that we can use to describe the behavior of water on the surface is the residence time (τr), which gives insight into its mobility. Giving the complexity of the surface structure, we have calculated τr for different adsorption sites that are seen across the surfaces studied. Water molecules reside around surface Al atoms 96 ± 35, 253 ± 55, and 179 ± 44 ps when not hydroxylated, and when 4-fold and 5-fold coordinated, respectively. It is clear that the higher residence time of water on 4-fold coordinated Al sites compared to the 5-fold is due to the lower coordination. However, there is a secondary effect that relates to the effect of hydroxylation on the residence time of water molecules, and it is the hydrogen bonding. Indeed, in the case of surface Al sites without hydroxylation, it is more likely that the extra coordination of water molecules due to hydroxyl groups adsorbed onto Al sites is missing and that the hydrogen

4. CONCLUSION We have presented a systematic study on the stable edge surfaces of pyrophyllite. Using quantum mechanical calculations, we have shown that the CLAYFF force field can be used reliably to simulate the edge surfaces of pyrophyllite. This opens the possibility of improved simulations of nanoparticles of clay minerals and adsorption of metal ions onto edge surfaces, which will impact the possibility of, for example, increasing the cationic rate of exchange. To date, we have only considered neutral surfaces with hydroxylated dangling bonds; an important challenge for the future research will be to account for charged surfaces. However, within the simulated edge surfaces, we found that the surface energies are quite similar, but we see that high coverage of hydroxyl is always stabilized over the low coverage. We found that the properties of the edge surfaces are specific to local geometries rather than the Miller index. Therefore, we cannot infer the stability of the edge surfaces a priori from simple considerations, such as coordination number, number of hydroxyl groups on the surface Si and Al atoms, and OH surface coverage. This implies that direct simulations of different 27315



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(3) Hyun, S. P.; Cho, Y. H.; Kim, S. J.; Hahn, P. S. Cu(II) Sorption Mechanism on Montmorillonite: An Electron Paramagnetic Resonance Study. J. Colloid Interface Sci. 2000, 222 (2), 254−261. (4) McBride, M. B. Copper(II) Interactions with Kaolinite - Factors Controlling Adsorption. Clays Clay Miner. 1978, 26 (2), 101−106. (5) Oday, P. A.; Brown, G. E.; Parks, G. A. X-ray-Absorption Spectroscopy of Cobalt(II) Multinuclear Surface Complexes and Surface Precipitates on Kaolinite. J. Colloid Interface Sci. 1994, 165 (2), 269−289. (6) Papelis, C.; Hayes, K. F. Distinguishing between Interlayer and External Sorption Sites of Clay Minerals Using X-ray Absorption Spectroscopy. Colloids Surf., A 1996, 107, 89−96. (7) Schlegel, M. L.; Manceau, A.; Chateigner, D.; Charlet, L. Sorption of Metal Ions on Clay Minerals I. Polarized EXAFS Evidence for the Adsorption of Co on the Edges of Hectorite Particles. J. Colloid Interface Sci. 1999, 215 (1), 140−158. (8) Strawn, D. G.; Sparks, D. L. The Use of XAFS to Distinguish between Inner- and Outer-Sphere Lead Adsorption Complexes on Montmorillonite. J. Colloid Interface Sci. 1999, 216 (2), 257−269. (9) Vasconcelos, I. F.; Bunker, B. A.; Cygan, R. T. Molecular Dynamics Modeling of Ion Adsorption to the Basal Surfaces of Kaolinite. J. Phys. Chem. C 2007, 111 (18), 6753−6762. (10) Zachara, J. M.; Smith, S. C.; McKinley, J. P.; Resch, C. T. Cadmium Sorption on Specimen and Soil Smectites in Sodium and Calcium Electrolytes. Soil Sci. Soc. Am. J. 1993, 57 (6), 1491−1501. (11) Bradl, H. B. Adsorption of Heavy Metal Ions on Soils and Soils Constituents. J. Colloid Interface Sci. 2004, 277 (1), 1−18. (12) Davis, J. A.; Kent, D. B. Surface Complexation Modeling in Aqueous Geochemistry. Rev. Mineral. 1990, 23, 177−260. (13) Hayes, K. F.; Traina, S. J. Metal Ion Speciation and Its Significance in Ecosystem Health. In Soil Chemistry and Ecosystem Health; SSSA Special Publication Number 52; Soil Science Society of America: Madison, WI, 1998; pp 45−84. (14) McBride, M. B. Environmental Chemistry of Soils; Oxford University Press: Oxford, U.K., 1994. (15) Adams, J. M.; Clement, D. E.; Graham, S. H. Low-Temperature Reaction of Alcohols to Form tert-Butyl Ethers Using Clay Catalysts. J. Chem. Res., Synop. 1981, No. 8, 254−255. (16) Adams, J. M.; Clement, D. E.; Graham, S. H. Synthesis of Methylt-Butyl Ether from Methanol and Isobutene Using a Clay Catalyst. Clays Clay Miner. 1982, 30 (2), 129−134. (17) Tudor, J.; Willington, L.; O’Hare, D.; Royan, B. Intercalation of Catalytically Active Metal Complexes in Phyllosilicates and Their Application as Propene Polymerisation Catalysts. Chem. Commun. 1996, No. 17, 2031−2032. (18) Galan, E. Properties and Applications of Palygorskite-Sepiolite Clays. Clay Miner. 1996, 31 (4), 443−453. (19) Zhu, R.; Chen, W.; Shapley, T. V.; Molinari, M.; Ge, F.; Parker, S. C. Sorptive Characteristics of Organomontmorillonite toward Organic Compounds: A Combined LFERs and Molecular Dynamics Simulation Study. Environ. Sci. Technol. 2011, 45 (15), 6504−6510. (20) Sherman, D. M. Surface Complexation Modeling: Mineral Fluid Equilbria at the Molecular Scale. In Thermodynamics and Kinetics of Water-Rock Interactions: Reviews in Mineralogy and Geochemistry; Oelkers, E. H., Schott, J., Eds.; The Mineralogical Society of America: Chantilly, VA, 2009; Vol. 70, pp 181−205. (21) Suter, J. L.; Anderson, R. L.; Christopher Greenwell, H.; Coveney, P. V. Recent Advances in Large-Scale Atomistic and Coarse-Grained Molecular Dynamics Simulation of Clay Minerals. J. Mater. Chem. 2009, 19 (17), 2482−2493. (22) Lee, J. H.; Guggenheim, S. Single-Crystal X-ray Refinement of Pyrophyllite-1Tc. Am. Mineral. 1981, 66 (3−4), 350−357. (23) Bleam, W. F.; Welhouse, G. J.; Janowiak, M. A. The Surface Coulomb Energy and Proton Coulomb Potentials of Pyrophyllite (010), (110), (100), and (130) Edges. Clays Clay Miner. 1993, 41 (3), 305− 316. (24) White, G. N.; Zelazny, L. W. Analysis and Implications of the Edge Structure of Dioctahedral Phyllosilicates. Clays Clay Miner. 1988, 36 (2), 141−146.

possible terminations are required to evaluate the relative stabilities and hence equilibrium morphologies. Furthermore, we suggest from the calculated stability that the morphology of the crystal is dominated by kinetic effects. The knowledge of the structure of water on the surfaces is of great importance as it will define the adsorption behavior of metals, as well as inorganic and organic cations. We have shown that the edge surfaces of pyrophyllite are hydrophilic, unlike the hydrophobic basal {001} surface. This is caused not only by the hydroxylation of the dangling bonds but also by the presence of uncoordinated surface Al atoms, which strongly interact with the water molecules. The residence time of water on the edge surfaces was calculated to be very similar to that of an iron oxyhydroxide. On some surfaces, we predict that this interaction is so strong that water is trapped in bulk-like sites above the surface Al atoms. This will have consequences in catalytic and remediative processes as well as on the behavior of phyllosilicate nanoparticles in geological processes where a notorious hydrophobic material can gain hydrophilic behavior on the nanosize scale; indeed, in this case, the nanoparticles will have both hydrophobic and hydrophilic surfaces. Again, there are no strong generic correlations between the dynamical properties and the Miller index, but rather with local geometries. This indicates the importance of exploring a wide variety of edge surfaces as the total behavior of the nanoparticles in an aqueous environment will be defined by the local structural features.

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ASSOCIATED CONTENT

S Supporting Information *

List of lattice parameters from previous and current work structures of bulk pyrophyllite; detailed surface energies, selected bonding parameters, structures, and stacking diagrams of edge surfaces; and diffusion coefficients of water at the edge surfaces. This material is available free of charge via the Internet at http:// pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected] (S.C.P.). Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors would like to thank the FCT (“Fundaçaõ para a Ciência e a Tecnologia”) under the KADRWaste Grant - PTDC/ CTE-GEX/82678/2006 and the Department of Geology of the University of Lisbon for financial support. D.M.S.M. also acknowledges the FCT for the award of a postdoctoral fellowship (SFRH/BPD/71118/2010). We also acknowledge the EPSRC for funding (EP/I03601X/1). Computations were run on HECToR and ARCHER through the Materials Chemistry Consortium funded by EPSRC (EP/F067496 and EP/L000202) and on Aquila the HPC at the University of Bath.

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REFERENCES

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Toward Modeling Clay Mineral Nanoparticles: The Edge Surfaces of

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