The Structure and Dynamics of Hydrated and Hydroxylated

Jan 12, 2011 - Langmuir 2011, 27(5), 1821–1829. Published on ... ‡Present address: School of Chemistry, Trinity College Dublin, Dublin 2, Ireland...
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The Structure and Dynamics of Hydrated and Hydroxylated Magnesium Oxide Nanoparticles Dino Spagnoli,† Jeremy P. Allen,‡ and Stephen C. Parker* Department of Chemistry, University of Bath, Bath, BA2 7AY, U.K. †Present address: Nanochemistry Research Institute, Department of Chemistry, Curtin University of Technology, P.O. Box U1987, Perth, WA 6845, Australia. ‡ Present address: School of Chemistry, Trinity College Dublin, Dublin 2, Ireland. Received October 18, 2010. Revised Manuscript Received December 10, 2010 An understanding of the structure of water on metal oxide nanoparticles is important due to its involvement in a number of surface processes, such as in the modification of transport near surfaces and the resulting impact on crystal growth and dissolution. However, as direct experimental measurements probing the metal oxide-water interface of nanoparticles are not easily performed, we use atomistic simulations using experimentally derived potential parameters to determine the structure and dynamics of the interface between magnesium oxide nanoparticles and water. We use a simple strategy to generate mineral nanoparticles, which can be applied to any shape, size, or composition. Molecular dynamics simulations were then used to examine the structure of water around the nanoparticles, and highly ordered layers of water were found at the interface. The structure of water is strongly influenced by the crystal structure and morphology of the mineral and the extent of hydroxylation of the surface. Comparison of the structure and dynamics of water around the nanoparticles with their two-dimensional flat surface counterparts revealed that the size, shape, and surface composition also affects properties such as water residence times and coordination number.

Introduction Reactions at the metal oxide-water interface are some of the most important in nature, and this interface has been known to play a governing role in processes such as dissolution, precipitation, and adsorption.1 The impact of these reactions can affect the composition and quality of natural waters, the formation of soils, the removal of CO2 from the atmosphere and the global cycling of chemical elements.2 In particular, nanoparticles have been found to have a variety of important geological and technological properties, including an increased capacity for scavenging toxins from the environment3 and as a key abrasive material for chemical-mechanical planarization of advanced integrated circuits.4 Although magnesium oxide is relatively simple in structure (face-centered cubic with six coordinate oxygen atoms and cations), it is an attractive system to model because of its importance both as a support for metal catalysts, a high temperature superconductor,5 and a catalyst in its own right.6 More recent research has alluded to the fact that simple binary oxide minerals, such as CaO and MgO, have the ability to adsorb CO2 *Corresponding author. Address: Department of Chemistry, University of Bath, Bath, BA2 7AY, U.K. Phone: þ44 (0) 1225 386505. Fax: þ44 (0) 1225 386231. E-mail: [email protected]. (1) Brown, G. E.; Henrich, V. E.; Casey, W. H.; Clark, D. L.; Eggleston, C.; Felmy, A.; Goodman, D. W.; Gratzel, M.; Maciel, G.; McCarthy, M. I.; Nealson, K. H.; Sverjensky, D. A.; Toney, M. F.; Zachara, J. M. Chem. Rev. 1999, 99, 77– 174. (2) Brown, G. E. Science 2001, 294, 67–69. (3) Waychunas, G. A.; Kim, C. S.; Banfield, J. F. J. Nanopart. Res. 2005, 7, 409– 433. (4) Feng, X. D.; Sayle, D. C.; Wang, Z. L.; Paras, M. S.; Santora, B.; Sutorik, A. C.; Sayle, T. X. T.; Yang, Y.; Ding, Y.; Wang, X. D.; Her, Y. S. Science 2006, 312, 1504–1508. (5) Langel, W.; Parrinello, M. Phys. Rev. Lett. 1994, 73, 504–507. (6) Kumar, D.; Reddy, V. B.; Mishra, B. G.; Rana, R. K.; Nadagouda, M. N.; Varma, R. S. Tetrahedron 2007, 63, 3093–3097. (7) Jensen, M. B.; Pettersson, L. G. M.; Swang, O.; Olsbye, U. J. Phys. Chem. B 2005, 109, 16774–16781. (8) Allen, J. P.; Parker, S. C.; Price, D. W. J. Phys. Chem. C 2009, 113, 8320– 8328.

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on their surfaces, which has important implications for carbon sequestration.7,8 However, despite the importance of the mineralwater interface, which has been the subject of a number of studies (for example, that of Geysermans et al.9 and Broughton and Gilmer10 and the references therein), little work has been conducted on its role in the magnesium oxide system, particularly for nanoparticles. Experimental observations by Hacquart and Jupille11 of cubic {100} MgO nanoparticles immersed in water revealed a change of nanoparticle shape over time. This initially proceeds via corner faceting of the cubic crystals, due to hydroxylation of the {110} surface. However, when left for further time, domination of the {111} surface results, giving rise to octahedral morphologies. Supporting computational studies12 of this process using constrained Wulff constructions, where the {111} surface energy was discounted to mimic the slow growth of the surface, suggests that the initial stabilization of the {110} termination is a kinetic effect, leading to a metastable state prior to the formation of the most stable configuration, that of the {111}-bound hydroxylated nanoparticle. However, although the surface stabilization has been characterized, little analysis of the water structure surrounding these nanoparticles or water dynamics has been discussed. The mineral-water interface has been probed using both experiment13-16 and computer simulation,17,18 with results indicating that water organizes into very ordered layers. For example, Cheng et al.’s15 use of X-ray reflectivity measurements (9) Geysermans, P.; Gorse, D.; Pontikis, V. J. Chem. Phys. 2000, 113, 6382– 6389. (10) Broughton, J. Q.; Gilmer, G. H. J. Chem. Phys. 1986, 84, 5759–5768. (11) Hacquart, R.; Jupille, J. Chem. Phys. Lett. 2007, 439, 91–94. (12) Geysermans, P.; Finocchi, F.; Goniakowski, J.; Hacquart, R.; Jupille, J. Phys. Chem. Chem. Phys. 2009, 11, 2228–2233. (13) Fenter, P.; Cheng, L.; Park, C.; Zhang, Z.; Sturchio, N. C. Geochim. Cosmochim. Acta 2003, 67, 4267–4275. (14) Geissbuhler, P.; Fenter, P.; DiMasi, E.; Srajer, G.; Sorensen, L. B.; Sturchio, N. C. Surf. Sci. 2004, 573, 191–203. (15) Cheng, L.; Fenter, P.; Nagy, K. L.; Schlegel, M. L.; Sturchio, N. C. Phys. Rev. Lett. 2001, 87, 156103.

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to study the distribution of interfacial water above the mica surface clearly showed a strong layer of adsorbed water followed by a hydration layer of water. Beyond this, the density distribution was seen to oscillate up to a distance of 12 A˚ above the surface, suggesting the formation of weak layers. More recently, Ghose et al.16 reported similar crystal truncation rod (CTR) results of the hydrated goethite surface, showing the presence of adsorbed water layers above the surface. Computer simulations provide a useful tool in probing the interface between mineral surfaces and water due to their inherent ability to consider these processes at the atomic level. The adsorption of ions to and from surfaces have been quantified in terms of free energy profiles, where the layers that form above the surface affect the adsorption profile.19 Additionally, the dissolution of ions from mineral surfaces can be investigated using computer simulations, and there is a difference in the free energy profile depending on whether the dissolved ions come from a step or a flat surface.20 More recently, computer simulation has been used to describe the interface between nanoparticle surfaces and water.21,22 The lack of experimental work on the nanoparticle-water interface, in particular the distribution of interfacial water, is not surprising due to the difficulties involved in directly probing and analyzing this region. Not only will the increased degrees of freedom of the nanoparticle make accurate measurement more difficult than for a surface, but also any variation in density around the nanoparticle surface will act as a hindrance. Therefore, computer simulation offers a method of simulating this interface, allowing predictions of both the density distribution and processes that could occur at this interface. The aim of this study is to model magnesium oxide nanoparticles, of a variety of shapes, using classical molecular dynamics (MD) to predict the effect morphology has on the stability of the nanoparticle. To illustrate the interaction of water with MgO nanoparticles, we consider the effect of surface hydroxylation on nanoparticle stability and simulate the interaction of nanoparticles with liquid water. One of the ultimate goals of our work is to model the role of water on the interaction and aggregation of particles in solution; thus identifying the structure and strength of the interaction with water will be central. This will be realized by considering both the structure of the surrounding water as well as considering the strength of its interaction through the water mobility at the nanoparticle surfaces. Furthermore, comparisons will also be made between the nanoparticles and two-dimensional flat surfaces to investigate the differences between the two types of interfaces, as understanding the differences between continuous surfaces and nanoparticles could be of direct use for customizing these systems for a specific role.

aim to represent the van der Waals attractive forces and repulsions between electron charge clouds. The reliability of our results is dependent on the short-range interatomic potentials, and thus we use parameters that have been tested against both experiment and theory. The interaction between magnesium and oxygen was first introduced by Lewis and Catlow24 and has been successfully used to simulate bulk magnesium oxide,25 grain boundaries,26 surfaces,27 and the mineral-water interface.17 The water potential used is that of de Leeuw and Parker17 with the revised hydrogen bonding parameters introduced by Kerisit and Parker.18 The potential parameters of Baram and Parker28 were used to describe the hydroxide ion interactions. The anions in this study are all described using the shell model of Dick and Overhauser29 to account for any polarization on these ions, and the shells were given a small mass of 0.2 au following the approach introduced by Mitchell and Fincham.30 A table of the potential parameters used in this study has been included in the Supporting Information. The initial construction and energy minimization of surfaces and nanoparticles were all performed using the METADISE code.31 We use the two-region approach, which was originally developed by Tasker,32 where the atoms in the first region are those near the surface and are able to relax mechanically. The atoms in the second region are further away and held fixed at their bulk equilibrium positions. The MD simulations of all surfaces and nanoparticles in both vacuum and water were performed using the DL_POLY code.33 The trajectories were generated in the NVT (constant number of particles, volume, and temperature) ensemble by means of the Verlet leapfrog algorithm34,35 with a time step of 0.2 fs, and the temperature was kept constant at 300 K by a Nose-Hoover thermostat.36 However, to ensure that the water or surfaces experienced no residual strain from the choice of boundary conditions, we introduced a vacuum gap for the twodimensional surface simulations, while for the nanoparticles immersed in water we used the NPT (constant number of particles, pressure, and temperature) ensemble with no applied pressure, and the shape of the simulation cell was conserved. DL_POLY uses the constrained isothermal-isobaric approach developed by Melchionna et al.37 The electrostatic interactions were calculated using the Ewald summation38 for all simulations where periodic boundary conditions were applied. However, for simulations where no periodic boundary conditions are enforced, such as a nanoparticle in vacuum, direct summation of the Coulomb pair interaction is adequate,39 and the real space cutoff was chosen so as to incorporate all pairs of atoms within the simulation cell.

Methodology The computer simulations are based on the Born model of solids23 and assumes a formal charge on the atoms. The interactions between the atoms can be separated into long-range Coulombic and short-range interactions. The short-range interactions are modeled by parametrized analytical functions, which

(24) Lewis, G. V.; Catlow, C. R. A. J. Phys. C: Solid State Phys. 1985, 18, 1149– 1161. (25) Cooke, D. J.; Parker, S. C.; Osguthorpe, D. J. Phys. Rev. B 2003, 67, 134306. (26) Harris, D. J.; Watson, G. W.; Parker, S. C. Phys. Rev. B 1997, 56, 11477– 11484. (27) Watson, G. W.; Oliver, P. M.; Parker, S. C. Surf. Sci. 2001, 474, L185–L190. (28) Baram, P. S.; Parker, S. C. Philos. Mag. B: Phys. Condens. Matter Stat. Mech. Electron. Opt. Magn. Prop. 1996, 73, 49–58. (29) Dick, A. W.; Overhauser, B. G. Phys. Rev. 1958, 112, 90–103. (30) Mitchell, P. J.; Fincham, D. J. Phys.: Condens. Matter 1993, 5, 1031–1038. (31) Watson, G. W.; Kelsey, E. T.; deLeeuw, N. H.; Harris, D. J.; Parker, S. C. J. Chem. Soc., Faraday Trans. 1996, 92, 433–438. (32) Tasker, P. W. Philos. Mag. A 1979, 39, 119. (33) Smith, W.; Forester, T. R. J. Mol. Graph. 1996, 14, 136–141. (34) Verlet, L. Phys. Rev. 1967, 159, 98–103. (35) Hockney, R. W. The Potential Calculation and Some Applications; Academic Press: New York/London, 1970; Vol. 9. (36) Hoover, W. G. Phys. Rev. A 1985, 31, 1695–1697. (37) Melchionna, S.; Ciccotti, G.; Holian, B. L. Mol. Phys. 1993, 78, 533–544. (38) Ewald, P. P. Ann. Phys. 1921, 64, 253. (39) Smith, W.; Todorov, I. T. Mol. Simul. 2006, 32, 935–943.

(16) Ghose, S. K.; Waychunas, G. A.; Trainor, T. P.; Eng, P. J. Geochim. Cosmochim. Acta 2010, 74, 1943–1953. (17) de Leeuw, N. H.; Parker, S. C. Phys. Rev. B 1998, 58, 13901–13908. (18) Kerisit, S.; Parker, S. C. J. Am. Chem. Soc. 2004, 126, 10152–10161. (19) Kerisit, S.; Parker, S. C. Chem. Commun. 2004, 52–53. (20) Spagnoli, D.; Kerisit, S.; Parker, S. C. J. Cryst. Growth 2006, 294, 103–110. (21) Cooke, D. J.; Elliott, J. A. J. Chem. Phys. 2007, 127, 9. (22) Spagnoli, D.; Gilbert, B.; Waychunas, G. A.; Banfield, J. F. Geochim. Cosmochim. Acta 2009, 73, 4023–4033. (23) Born, M.; Huang, K. Dynamical Theory of Crystal Lattices., 1st ed.; Oxford University Press: Oxford, 1954.

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Figure 1. The resultant morphologies of the (a) {100}, (b) {110}, and (c) {111} MgO nanoparticles.

Results and Discussion Construction of Nanoparticles. We have developed a simple and robust approach for generating mineral nanoparticles, which involves the use of Wulff constructions40 based on the surface energies of different two-dimensional surfaces. For a crystal consisting of a given number of atoms, the equilibrium shape is that which minimizes the surface energy. In a two-dimensional Wulff construction, for a polar coordinate system, a vector is drawn parallel to the normal of the surface, with its length proportional to the energy of the surface. At the end point of the vector, a tangent is drawn. If repeated for all surfaces, the tangents limit the equilibrium shape. If a particular surface has a high energy, it will not be present in the final construction. The construction in three dimensions is in principle the same except that a spherical coordinate system is used, and, at the end of the vectors, tangent planes are drawn. We chose to construct three different morphologies of MgO nanoparticles using the surface energies of the {100}, {110}, and {111} Miller indices. The two-dimensional static lattice calculations of the {100}, {110}, and {111} flat surfaces (unfaceted) produced surface energies of 1.29, 2.18, and 4.16 J m-2, respectively. As expected from previous studies, the relative stability of the surfaces from the least to most stable follows the order {111} > {110} > {100}, with comparable surface energies to literature values.41,42 MgO was modeled using an Fm3m space group. Each nanoparticle was generated as consisting of only one surface, resulting in the crystal morphologies shown in Figure 1. The {100} morphology is cubic, the {110} is rhombic dodecahedral, and the {111} is octahedral (Figure 1), as would be expected from this space group. These morphologies were then used to “carve out” the structure from bulk MgO. However, a problem arises if the origin of the nanoparticle is at the crystallographic origin, where an ion is at the center of the crystal lattice. The nanoparticle with flat outer surfaces would have an overall net charge and may result in the particle’s configurational energy not being able to converge during the energy minimization. The approach we use to overcome this was to perform a scan of different configurations, varying the position of the particle origin compared to the crystallographic origin. In practice, this results in the nanoparticle origin being at a high symmetry site between two oppositely charged ions. This method allows the construction of stoichiometric, charge neutral nanoparticles of any shape and size and has been applied successfully in simulations of calcite21,43,44 and hematite nanoparticles.22 The cubic, rhombic dodecahedral and octahedral MgO nanoparticles, with a diameter of 40 A˚, were then simulated in two ways. First, the nanoparticles were simulated for 1 ns in the NVT ensemble at a temperature of 300 K with a Hoover relaxation (40) Wulff, G. Kristallogr. Kristallgeom. 1901, 39, 449. (41) Mackrodt, W. C. Phys. Chem. Miner. 1988, 15, 228–237. (42) Refson, K.; Wogelius, R. A.; Eraser, D. G.; Payne, M. C.; Lee, M. H.; Milman, V. Phys. Rev. B 1995, 52, 10823–10826. (43) Kerisit, S.; Cooke, D. J.; Spagnoli, D.; Parker, S. C. J. Mater. Chem. 2005, 15, 1454–1462. (44) Martin, P.; Spagnoli, D.; Marmier, A.; Parker, S. C.; Sayle, D. C.; Watson, G. Mol. Simul. 2006, 32, 1079–1093.

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Figure 2. Initial structures for the 40 A˚ MgO nanoparticles bound by the (a) {100}, (b) {110}, and (c) {111} surfaces. The final structures following simulation at 300 K are shown in images (d), (e), and (f), respectively. The final structures following a method of simulated annealing are shown in images (g), (h), and (i), respectively. Oxygen atoms are shown in red, and magnesium atoms are shown in green.

constant of 0.5 ps. However, to allow the nanoparticles to undergo surface rearrangement to form stable surfaces, simulated annealing was also used. This process involves taking the initial surface structure and simulating it in the NVT ensemble for 500 ps at a temperature of 2500 K, allowing the surface to melt. The temperature was then reduced in steps of 500 K until the temperature of the system reached 300 K. Each temperature was run for 500 ps, with the 300 K simulation having a simulated real time of 1 ns. No matter which method was used for the relaxation of nanoparticles in vacuum, all atoms in the simulation were allowed to relax. It can be seen that the different simulation processes give rise to differing structures (Figure 2). For the nanoparticles simulated at 300 K, the {100} nanoparticle is seen to be stable with no surface rearrangement. The {111} nanoparticle also shows a high degree of stability, with the shape and surface structure remaining similar to the initial configuration, suggesting that this configuration is metastable. However, rearrangement of corner sites is seen, with atoms clustering at these sites to remove the undercoordinated magnesium. The {111} nanoparticle, however, undergoes a more dramatic rearrangement. The high-energy {111} surface can be seen to rearrange, giving a multifaceted structure, with the appearance of the more stable {100} surface structure containing flat sheets of magnesium and oxygen atoms, which differ from the initial structures that have either magnesium- or oxygen-terminated surfaces. This trend is expected due to the stability of the perfect {100} surface and the polar {111} surface, which shows the most surface reconstruction. For the simulated annealing simulations, a greater extent of rearrangement is seen. The stability of the {100} is seen by retention of the cubic shape, with a minor amount of surface faceting. However, the {110} and {111} both show the formation of multifaceted nanoparticles, comprising {100} surfaces. This also indicates the stability that the {100} surface gives to these nanoparticulate systems, in the absence of other species, giving rise to nanoparticles with a more ordered cubic structure. These simulations highlight the strengths of the simulated annealing process, allowing for the formation of more DOI: 10.1021/la104190d

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Table 1. Average Configurational Energies Per Formula Unit for the 40 A˚ MgO Nanoparticles Following Simulation at 300 K for 1 ns or via a Simulated Annealing Approach nanoparticle morphology

300 K for 1 ns (eV)

annealed from 2500 K (eV)

{100} {110} {111}

-40.87 -40.58 -40.57

-40.85 -40.74 -40.69

complex rearrangements, which are either not seen in the simulations at 300 K, due to insufficient kinetic energies, or would take a considerably longer simulation time to form. The relative stabilities can also be illustrated by considering the average configurational energies of the nanoparticles per formula unit (Table 1). As can be seen, the {100} is the lowest in energy for both simulation methods. The stability of the {100} nanoparticle is again demonstrated by both processes giving rise to similar energies, which are lower in energy than the {110}- and {111}-dominated nanoparticles. This is also in agreement with experimental work11 concerning MgO nanoparticles formed from smokes, which show a cubic morphology. In addition, the surface faceting of the {110} and {111} nanoparticles is shown to give rise to significant stabilization, with the differences in energies related to the different number of atoms and different surface areas. Surface hydroxylation is an important process to consider, as surface interactions with water can give rise to a certain degree of hydroxylation. To begin to address this, we have studied the full surface hydroxylation of these nanoparticles. This was achieved via the replacement of surface oxygen atoms with hydroxide groups and the placement of one hydroxide group above all surface magnesium atoms. One of the interesting features is that the hydroxide ions were mobile and lead to surface reconstruction at even modest temperatures. This is illustrated in Figure 3, where nanoparticles with diameter of 40 A˚ were simulated at 300 K for 1 ns and show significant differences between initial and final configurations. The hydroxylated {111} nanoparticle shows the least reconstruction, which is opposite to the trend of the unhydroxylated nanoparticles, therefore suggesting that hydroxylation causes sufficient stabilization of the {111} surfaces to make it the most stable. This stabilization, giving rise to octahedral-shaped nanoparticles, is also in agreement with experimental results, showing that, when left in water, hydroxylated {111} surfaces dominate, giving a stabilized octahedral morphology.11 The {111} nanoparticle remains roughly in an octahedral shape, with a small amount of faceting seen on the higher energy corner sites. Although the exact surface termination of the corner faceting cannot be determined conclusively here, it is qualitatively similar to that seen by Geysermans et al.12 In their study they report that, at 300 K and a pressure of 1 atm, corner faceting of the hydroxylated {100} surface is seen for fully hydroxylated {111} MgO nanoparticles. The {110} nanoparticle appears to become slightly more rounded while still retaining a similar shape to the initial configuration. The cubic {100} nanoparticle appears to have the most disorder, with distinct amorphous regions occurring on the near-surface region. As with the dry nanoparticles, we can also consider the effect of hydroxylation by considering the nanoparticle energies. These can again be determined from the average configurational energy; however, a correction factor, which accounts for the differences in charges on oxygen, needs to be applied to account for the reaction of water with MgO to form Mg(OH)2 when comparing lattice energies. This correction factor has a value of -7.15 eV and is both calculated and applied in an identical 1824 DOI: 10.1021/la104190d

Figure 3. Initial structures for the fully hydroxylated 40 A˚ MgO nanoparticles bound by the (a) {100}, (b) {110}, and (c) {111} surfaces. The final structures following simulation at 300 K are shown in images (d), (e), and (f), respectively. Oxygen atoms are shown in red, magnesium are shown in green, and hydrogen are shown in white.

manner to that outlined for surfaces by Allen et al.8 The calculated configurational energies, eV per MgO unit, for the hydroxylated {100}, {110}, and {111} nanoparticles are -41.17, -41.17, and -41.21, respectively. Thus all nanoparticles are stabilized in comparison to their dry counterparts. However, the most stable configuration is also seen to change to the {111}-bound nanoparticle. The results of this study are also in broad agreement with previous experimental and computational studies.11,12 The results show that, in the absence of water, the {100}-bound nanoparticle is the most stable. Upon surface hydroxylation, the stability changes, giving rise to the {111} surface dominating. However, whereas the {100} surface shows this instability through the beginnings of a surface rearrangement, the hydroxylated {110} nanoparticle retains its shape, with only a minor amount of faceting observed. This suggests that the hydroxylated {110} nanoparticle may have some degree of metastability. This is similar to that reported by Geysemans et al., who observed the initial formation of {110} faceting of a {100}-bound MgO nanoparticle immersed in water prior to the {111} surface dominating the morphology.12 This method can be adapted to generate nanoparticles of various shape, size, and composition, based on the material under study. We have shown that, using techniques such as energy minimization and simulating annealing, the nanoparticles will undergo surface reconstruction to get to the lowest energy structure. These final configurations can then be applied to other simulations, including immersing the nanoparticles in water to investigate the structure of water around nanoparticles, which is described below. Predicting the Structure of Water around Hydrated and Hydroxylated MgO Nanoparticles. The structure of water around different nanoparticles is considered using two different systems. First, we consider the interactions of a pure MgO nanoparticle with water using a {100}-bound structure. Second, we model a hydroxylated nanoparticle, in this case dominated by the {111} surface. In this study, we have decided to only examine the structure of water around nanoparticles dominated by one surface. In reality, there may be the possibility of multifaceted nanoparticles present; however, in order to get a comparison between the structure of water on a hydrated and hydroxylated surface, we included the two most stable morphologies where no hydroxylated species are present and where the surface is fully Langmuir 2011, 27(5), 1821–1829

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Figure 4. Left hand side: three-dimensional projection of the structure of water around a 1.7 nm {100} MgO nanoparticle. The color blue indicates a magnesium ion. Pink indicates a lower number of water molecules, whereas dark red indicates a high number of water molecules. Right hand side: The water density relative to bulk (red line) taken in the direction perpendicular to the {100} surface with the magnesium (blue line) concentration inserted for reference.

hydroxylated, namely, the {100} and {111} nanoparticles, respectively. The {100} nanoparticle, constructed with a diameter of 1.7 nm, was submerged in a box containing 2613 water molecules. The cubic simulation cell had a side length of 39.5 A˚ on average, containing 10 764 species, and was performed at a temperature of 300 K and ambient pressure in an NPT ensemble for 600 ps. To avoid rotation and translation of the nanoparticle during the simulation, two atoms at the center of the nanoparticle were held fixed, while the remaining atoms were allowed to relax fully. The density of water surrounding the nanoparticles can be considered using two approaches. The first method allows for the threedimensional water structure around the nanoparticle to be considered by splitting the simulation cell into 0.3 A˚ bins and evaluating the number of times a species of interest passes through a given bin. Plotting this allows us to obtain a three-dimensional projection of the water structure from the MD simulation. The second approach is to express the water density as a function of the z-coordinate, where the z-direction is taken to be perpendicular to one of the faces of the nanoparticle, thus allowing a characterization of the water layers as a function of distance from the nanoparticle face. The three-dimensional projection shows a highly structured ordering of water around the 1.7 nm MgO nanoparticle (Figure 4), where the color blue indicates the magnesium ion density, and pink through dark red indicates the water density. As the red color darkens, it indicates a greater density of water in that part of the simulation cell. To aid the visualization of the water structure, the picture shows half the nanoparticle, cut through the x-axis. The slight smearing of blue around the magnesium ions is due to the vibrations around their lattice sites. To give some perspective of the distances of the water layers above the flat surface of the nanoparticle, the water density relative to bulk can be expressed as a function of distance perpendicular to the surface. This allows the position of any layered structures or regions of high density to be seen and their relative distances to the surface to be considered. The highly ordered structure of water around the nanoparticle has at least three distinct layers of water, where the distances of the three layers are 2.0, 4.4, and 7.1 A˚ above the surface (Figure 4). The water structure above the faces mimics the flat surfaces of the nanoparticle. However, around the corners and edges of the nanoparticle, there is significant disruption of the water density distribution. The difference in water density could be highly significant for the rates of growth and dissolution. The fluctuations in water density are likely to slow the transport of ions and molecules to the flat surfaces, but the fluctuations are absent from edges and corners. Langmuir 2011, 27(5), 1821–1829

The {111} hydroxylated nanoparticle, of diameter 3.7 nm, was simulated in a box filled with water molecules. The cubic simulation cell had dimensions of approximately 61.8 A˚, containing 39416 species, with 9179 water molecules. The particle was simulated for 1 ns in the NPT ensemble, with a relaxation constant of 1.0 ps for both the thermostat and barostat, at a temperature of 300 K; ensuring convergence in the configurational energy was achieved. As with the {100} nanoparticle, two atoms at the center were held fixed to avoid rotation and translation of the nanoparticle. A similar three-dimensional projection of the structure of water can be generated (Figure 5), although for clarity only a slice through the x-direction is shown. The water structure around the {111} hydroxylated nanoparticle indicates a less structured water layering. The spots of water density that appear to be inside the hydroxide layer are due to the nanoparticle being unaligned in the slice and belong to the region above the hydroxide. There is one layer of water formed strongly at the interface; however, the second and third layer are much less prominent than around the hydrated {100} MgO nanoparticle. This water layering primarily appears above the faces of the nanoparticle, and the water density around the corner sites appears to show considerably less ordering. There are distinct regions of coordinated water immediately above the corner sites, but no long-range ordering is evident. There is also less intensity in the layers of water compared with the ordering formed on the hydrated {100} nanoparticle. The peaks of water density at 1.9, 4.4, and 7.3 A˚ above the surface are much broader (Figure 5) than in the plot above the hydrated {100} MgO nanoparticle surface (Figure 4). However, the distances of the peaks are very similar, indicating that the bulk structure, rather than the surface composition, affects the layering of the water above the surface. The difference in ordering of water around different surface sites, i.e., side or corner sites, is simply caused by the coordination number of the sites. Sites with a higher surface coordination will restrict the number of sites a water molecule can reside in. For a regular surface structure, such as this one, this is even more restricted, giving rise to the coordinated sheets. For sites with lower coordination, the greater degrees of freedom allow the water density to ether smear around the site or, in particular for the corner sites, to form in discrete crystallographic sites. Comparison of the Structure of Water around Nanoparticles with Flat Surfaces. The structure of water around the nanoparticles can be compared with simulations of two-dimensional flat surfaces of the {100} and hydroxylated {111} planes where no edge effects will be present. The depth of both the {100} DOI: 10.1021/la104190d

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Figure 5. Left hand side: three-dimensional projection of the structure of water around a 3.7 nm fully hydroxylated {111} MgO nanoparticle. The color blue indicates a magnesium ion, and the color black indicates the oxygens of the hydroxide ions. Pink indicates a lower number of water molecules, whereas dark red indicates a high number of water molecules. Right hand side: two-dimensional density profile perpendicular to the {111} surface showing the distances of the magnesium (blue curve), hydroxide oxygen (black curve), and hydroxide hydrogen (green curve) ions and water molecules (red curve).

Figure 6. Left hand side: three-dimensional projection of the structure of water above a hydrated {100} MgO surface. The color blue indicates magnesium ions on the surface, and dark red indicates a high number of water molecules. Right hand side: two-dimensional density profile along the z-axis showing the distances of the magnesium (blue curve) and water molecules (red curve).

and hydroxylated {111} slabs were approximately 20 A˚, bound on both sides with an identical surface structure. A water layer, approximately 20 A˚ in width, was then added to one side of the slab. A vacuum gap was also added between the water and the other surface, approximately 20 A˚ in width, to prevent potential problems that can result from possible residual pressure occurring as a result of using the NVT ensemble. The simulations were run with the NVE ensemble at 0 K for 10 ps followed by 1 ns at 300 K using the NVT ensemble, ensuring that convergence in the configurational energy was seen, with a relaxation constant of 1.0 ps. As in the previous simulations of the nanoparticles, we can consider the density of water between the magnesium ions and the oxygen of the water molecules by two methods. The {100} surface (Figure 6) show that the flat surface structure leads to the formation of a two-dimensional array of water molecules bound in crystallographic sites, giving the appearance of flat sheets of high water density, similar to that reported for the structurally similar CaO {100} surface.45 There appears to be more layers forming at larger distances from the surfaces, with the formation of 5-6 layers, than seen for the {100} nanoparticle. The two-dimensional density profile along the z-direction shows that the first three peak distances are at 2.2, 4.7, and 7.2 A˚ from the surface. The distances are around 0.2 A˚ further out than the three layers around the hydrated {100} MgO nanoparticle, which could be because of the introduction of corners and edges on the nanoparticle.

The equivalent three-dimensional water projection of the water structure above the hydroxylated {111} MgO slab (Figure 7) indicates considerably more water layering compared to the hydroxylated {111} MgO nanoparticle. The water density plotted in two dimensions along the z-axis has a very ordered structure with high-density peaks at 2.3, 5.0, and 7.7 A˚ above the surface. Again the distances of the first two layers are approximately 0.3 A˚ further out from the surface on the hydroxylated {111} slab compared to the nanoparticle. This result indicates that the water structure on hydroxylated nanoparticles differ from their macroscopic surface counterparts because of the small amount of flat surface available and the introduction of edge and corner sites. The reduction in bond distances is a manifestation of the higher reactivity of the nanoparticle surfaces compared with their twodimensional flat counterparts. The density plots (Figures 4 and 5) are taken in the direction perpendicular to the nanoparticle surface and would be more sensitive to the corner and edges, as the sides are relatively small in area. This affects the bond distances, making the positions of the water molecules a shorter distance from the nanoparticle surfaces compared with the two-dimensional flat surfaces. Dynamics of Water at the Mineral-Water Interface. The mobility of the water molecules surrounding the nanoparticles was investigated by calculating the residence time, which is defined as the average time a water molecule spends within the first hydration shell around the atom in question.46 Residence times have been used in previous experimental studies to describe

(45) Allen, J. P.; Gren, W.; Molinari, M.; Arrouvel, C.; Maglia, F.; Parker, S. C. Mol. Simul. 2009, 35, 584–608.

(46) Impey, R. W.; Madden, P. A.; McDonald, I. R. J. Phys. Chem. 1983, 87, 5071–5083.

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Figure 7. Left hand side: three-dimensional projection of the structure of water above a fully hydroxylated {111} MgO surface, with only a portion of the water layer and none of the MgO shown. The color blue indicates a hydroxide hydrogen above the {111} magnesium oxide surface, and dark red indicates a high number of water molecules. Right hand side: two-dimensional density profile along the z-axis showing the distances of the magnesium (green curve), hydroxide oxygen (black curve), and hydroxide hydrogen (blue curve) ions and water molecules (red curve).

the dynamics of ions in water,47 and also in computer simulation studies to describe the dynamics of water on flat surfaces.43,45,48 Comparison of residence times for the nanoparticle is more complex than on flat surfaces, as the residence time will vary depending on the surface site and its coordination. Therefore, we plot the variation of the water residence times for all surface hydrogen atoms (in the case of hydroxylated nanoparticles) and all surface magnesium atoms (in the case of hydrated nanoparticles) onto an isosurface across these atoms and colored them according to the residence time (Figure 8a,b). This approach was also applied to the calculated average number of water molecules coordinated to the surface atoms (Figure 8c,d). The data for these representations was determined over the final 400 ps of the total simulation for both systems. The first observation that is apparent for the {111} hydroxylated nanoparticle (Figure 8a) is the movement of hydroxide groups on the faces. Opposing faces on the nanoparticle show gaps in the hydroxide arrangement, leading to exposure of the surface magnesium atoms. Although not clear from the image, these hydroxides appear to move toward corner sites, increasing the hydroxide concentration at the less stable sites, with flattening or rounding occurring on some corners. The hydroxylated {111} nanoparticle also reveals a large degree of symmetry with similar movement seen on the bottom face of the nanoparticle, indicating that this stabilization of corner sites is not a random process. The large degree of symmetry in opposing faces of the nanoparticle is evidence that the surfaces of the nanoparticle interact with each other. The activity of one surface affects the behavior on the opposite face, which could account for the difference in behavior between nanoparticles and flat surfaces. This is evident by the large degree of symmetry found in the contour maps of both residence times and coordination number. Analysis of the residence times for the hydroxylated {111} nanoparticle shows that there is no clear variation pattern of the residence times for corner, edge, and face sites. This is because the surface of the nanoparticle is completely hydroxylated and therefore the interaction of water with hydrogen atoms is universal and does not depend on whether the surface atoms are at the corner, edge, or side of the (47) Ohtaki, H.; Radnai, T. Chem. Rev. 1993, 93, 1157–1204. (48) Spagnoli, D.; Cooke, D. J.; Kerisit, S.; Parker, S. C. J. Mater. Chem. 2006, 16, 1997–2006.

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Figure 8. Contour plots showing the water residence times on (a) a fully hydroxylated {111} and (b) a hydrated {100} MgO nanoparticle, where the color varies from red to turquoise respectively, indicating low to high residence times. The average number of coordinated water molecules on the (c) fully hydroxylated {111} and (d) hydrated {100} MgO nanoparticles vary in color from blue to red, respectively, showing low to high water coordination. Axis indicators show the relative view of the image rather than being absolute.

nanoparticle. In a previous study, where there is a difference of residence times depending on surface site, using very similar methods, the nanoparticles were not completely hydroxylated, and hydroxylation of the surface atoms only occurred on very low coordinated sites.22 Therefore the residence time was dependent on surface position and surface hydroxylation. The hydrated {100} nanoparticle is pictured with an approximately continuous residence time across the particle surface (Figure 8b). The variation of the average number of coordinated water molecules, however, does vary depending on the position of the surface atom. DOI: 10.1021/la104190d

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Figure 9. Contour plots showing the variation in (a) the water residence times, where the color varies from red to turquoise indicating low to high residence times, respectively, and (b) the average number of coordinated water molecules, varying in color from blue to red showing low to high water coordination, respectively, for a 3.1 nm {100} MgO nanoparticle. Axis indicators show the relative view of the image rather than being absolute.

Hydroxylated {111} nanoparticle corner sites show the highest number of coordinated water, followed by the edge and face sites (Figure 8c). The regions of exposed magnesium on the {111} nanoparticle also give rise to increased numbers of coordinated water, due to both the roughening of the surface as well as increased hydrogen bonding. The variation of coordination number is also seen, to a lesser extent, for the {100} nanoparticle (Figure 8d), although if the nanoparticle were of a larger size, a similar variation to the hydroxylated nanoparticle might be expected. The size of the nanoparticle is very small and is a third smaller than the hydroxylated {111} nanoparticle. It is not surprising to see little difference in residence time and coordination number between different surface sites and water because there are only two atomic units between an atom on the side of a nanoparticle and an atom in the corner. Therefore, to investigate whether the size of the nanoparticle can affect the residence time and coordination number of water molecules on the surface, a larger {100} MgO nanoparticle was immersed in a box of water. The larger nanoparticle had a diameter of 3.1 nm and 34612 species, including 7117 water molecules. The simulation was run for 1 ns in the NPT ensemble, with relaxation parameters of 1.0 ps for both the thermostat and barostat, and data for the residence times and average water coordination number was collected over the final 800 ps of the simulation. The variation in both residence time and average coordination number can be plotted in a similar manner (Figure 9). The results given in Figure 9a,b show a greater variation in water residence time and average coordination number of water for the larger nanoparticles, which is due to the increased distance between sites of different coordination. The corner sites have the lowest residence times due to the high mobility of water at these positions, whereas the highest residence time is seen on the faces of the nanoparticle. The strong coordination of the water molecules to the nanoparticle faces is further highlighted by the fact that once coordination to the center of the faces was achieved, the water molecules remained in position for the remainder of the simulation (the turquoise regions of Figure 9a). The variation in coordination number is also as expected, with surface sites showing a reduced number of water molecules coordinated to the magnesium atoms. The average number of coordinated water molecules in then seen to increase on the edge sites, due to the increased number of coordination sites, which is seen to increase further for corner sites. To further examine the dynamics of water around the 3.1 nm {100} MgO nanoparticle, we calculated the residence time of the 1828 DOI: 10.1021/la104190d

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Figure 10. Contour plots showing the variation in the water residence times in the second hydration layer, where the color varies from red to turquoise indicating low to high residence times, respectively, for a 3.1 nm {100} MgO nanoparticle, where the residence time scale varies from (a) 0 to 50 ps and (b) 0 to 400 ps. Axis indicators show the relative view of the image rather than being absolute.

water molecules of the second layer of water with the first layer of adsorbed water (Figure 10). Due to the highly dynamic nature of the second layer of water, there is only variation in the residence time when the scale of the residence time was set from 0 to 50 ps (Figure 10a) rather than 0 to 400 ps (Figure 10b). The water in the second layer of water is less structured than the first layer (Figure 4); therefore, it is not surprising that the residence times would be lower when compared with the residence times of water in the first layer. Following the same trend as in other simulations, the water molecules spend less time at locations near the corner and edge when compared with water that is situated near the flat {100} surfaces. Although qualitatively similar to the smaller nanoparticles, this study highlights the importance of considering nanoparticle size in these simulations, and we have been able to examine the difference in dynamics of water in the first two layers around the nanoparticle surface.

Conclusions We have developed a simple and robust approach to modeling mineral nanoparticles of any shape and size. Using MgO nanoparticles as a test case, we have simulated a variety of nanoparticles, and the results show that in the absence of water, the {100}bound nanoparticle is the most stable. However, upon surface hydroxylation, the stability changes, giving rise to the {111} surface dominating. Furthermore, the {110}-bound nanoparticle, both pure and fully hydroxylated appears to have some extent of metastability, through the retention of shape during the MD simulations. Although outside the scope of the present work, this implies there will be an activation barrier to rearrangement of the surfaces. We have also performed large MD simulations of two morphologies of MgO nanoparticles in a box of water. The water structure above the {100} faces mimics the flat surfaces of the nanoparticle and produces highly structured, ordered layers of water that extend to approximately 8 A˚ above the surface. The water structure around the hydroxylated {111} nanoparticle indicates less structured water layering, with broader peaks compared with the {100} nanoparticle. In both cases, however, the corners and edges of the nanoparticles showed significant disruption of the distribution of the water density compared with the flat surface. The difference in water density could be highly significant for the rates of growth and dissolution. The dynamics of water around nanoparticle surfaces is strongly affected by size, shape, and composition. The strongly bound water molecules on the flat {100} surface have relatively high residence times compared Langmuir 2011, 27(5), 1821–1829

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with the hydroxlyated {111} surface and corner or edge sites. These strongly bound water molecules cause the formation of very highly structured water layers above the surface. This study indicates that, if the structured and dynamics of water is affected by the shape, size, and surface composition of the nanoparticle, the growth, dissolution, and adsorption of ions to the surface will also be affected. The next step will be to investigate increased complexity by considering nanoparticles with multiple crystal faces and different levels of surface hydroxylation with the eventual goal of using a reactive force field that will yield additional information of proton transfer at this interface.

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Acknowledgment. The majority of this work was performed with the computer resources provided by the MOTT2 facility (EPSRC Grant GR/S84415/01) run by the CCLRC e-Science Centre. We thank Dr. David J. Cooke, Dr. Arnaud Marmier, and Dr. Sebastien Kerisit for their useful discussions. Supporting Information Available: Tables listing the core/ shell charges and core-shell interactions of the studied ions, as well as the Buckingham, Lennard-Jones, Morse, and three-body potential parameters. This material is available free of charge via the Internet at http://pubs.acs.org.

DOI: 10.1021/la104190d

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