Topological Control of Water Reactivity on Glass Surfaces: Evidence

Jun 26, 2019 - Glass surfaces are of considerable interest due to their disproportionately large influence on the performance of glass articles in man...
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Letter Cite This: J. Phys. Chem. Lett. 2019, 10, 3955−3960

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Topological Control of Water Reactivity on Glass Surfaces: Evidence of a Chemically Stable Intermediate Phase Collin J. Wilkinson,† Karan Doss,† Seung Ho Hahn,‡ Nathan Keilbart,† Arron R. Potter,§ Nicholas J. Smith,⊥,¶ Ismaila Dabo,*,†,¶ Adri C. T. van Duin,*,†,‡,¶,□ Seong H. Kim,*,†,¶,□ and John C. Mauro*,†,¶

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Department of Materials Science and Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States ‡ Department of Mechanical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States § Department of Materials Science and Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180, United States ⊥ Science and Technology Division, Corning Incorporated, Corning, New York 14831, United States ¶ Materials Research Institute, The Pennsylvania State University, University Park, Pennsylvania 16802, United States □ Department of Chemical Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, United States ABSTRACT: Glass surfaces are of considerable interest due to their disproportionately large influence on the performance of glass articles in many applications. However, the behavior of glass surfaces has proven difficult to model and predict due to their complex structure and interactions with the environment. Here, the effects of glass network topology on the surface reactivity of glasses have been investigated using reactive and nonreactive force field-based molecular dynamics simulations as well as density functional theory. A topological constraint-based description for surface reactivity is developed, allowing for improved understanding of the physical and chemical origins of surface reactivity. Results show evidence for the existence of a chemically stable intermediate phase on the surface of the glass where the glass network is mechanically isostatic.

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lass surfaces, especially their interactions with water,1−4 are of the utmost importance to nuclear waste glass, cover glass, and many other modern applications.5 It is generally accepted that the outermost surface of oxide glasses readily reacts with water molecules, where water molecules dissociatively chemisorb on an as-formed glass surface and populate it with surface hydroxyls. Recent models attempt to quantitatively explain the change in properties observed on hydroxylated glass surfaces,1,6−8 and most agree that the surface readily becomes hydrated because it is more stable to have a bonded hydroxyl group than to have dangling bonds. In glass/water interactions, hydrolysis and diffusion lead to two forms of adsorbed water: chemisorbed and physisorbed.4,9−11 Both affect the network differently and are controlled by different processes: physisorption by hydrogen bonding10 and chemisorption by reaction with the surface to form bonded hydroxyl groups,

consistent with those reported experimentally.8,14,17 The models described in this work may therefore enable accurate description of glass surface reactivity and characterization of glass performance, despite its complex structure and interactions with the environment. Recently, topological constraint theory (TCT) has been used to predict various bulk properties of glassy systems such as Vickers hardness and fragility.18−20 In most binary oxide glasses, a hierarchy of three types of constraints is considered: α constraints represent linear bonds that form between a bridging oxygen (BO) and a network former; β constraints are angular constraints that are centered on a network former; and γ constraints are angular constraints centered on a BO.1,21 On the basis of the TCT description of glass networks, glassy materials have been postulated to have a so-called “intermediate phase” in which the atomic structure of a material will self-organize to be isostatically constrained (n = 3), as explored extensively in various works.22−27 In this case, it has been shown that such materials exhibit anomalous

Si − O − Si + H 2O → Si − OH + OH − Si

Although binding energy studies have been performed using molecular dynamics simulations,12 a specific study of the surface reactivity, in tandem, has not been conducted. Recent advancements in reactive force field modeling allow the direct observation of both diffusion and surface reactivity,13−16 and several studies have found surface reactivity constants © 2019 American Chemical Society

Received: May 4, 2019 Accepted: June 26, 2019 Published: June 26, 2019 3955

DOI: 10.1021/acs.jpclett.9b01275 J. Phys. Chem. Lett. 2019, 10, 3955−3960

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The Journal of Physical Chemistry Letters behavior in certain properties such as high hardness and lower free energy.7,22,28 Topological constraint theory has also been used to understand the impact of water on bulk glass structure and properties. Potter et al.1 found that, by accounting for the impact of chemisorbed (dissociated) water on glass network connectivity (viz., breaking Si−O−Si bonds) and the impact of physisorbed (molecular) water on strain of the Si−O−Si bond angle, the glass transition temperature (Tg) can be predicted. This model could be further expanded to include other properties (such as elastic modulus29 or dissolution kinetics7) predicted by TCT such as the work done by Liu et al.30 on calcium−silicate−hydrate gels. An extensive study relating topological constraints20,31,32 and surface energy was performed by Yu et al.,6 specifically focusing on the transition from hydrophilic to hydrophobic behavior on silica surfaces. Their work used reactive molecular dynamics to model the change in surface energy of a silicate glass and then correlate it with the number of surface constraints present, with the work focusing primarily on the global average of the surface. To investigate the effects of glass network topology on surface reactivity, we model the hydration of a silicate glass surface using molecular dynamics simulations according to the following procedures. Initially, bulk sodium silicate glasses are simulated (150 atoms with a molar composition of 70SiO2· 30Na2O) using the Teter potential.33 For a bulk glass, this pair potential is known to accurately simulate sodium silicate glasses, and has been extensively studied to investigate various properties, including structural features, transport of sodium ions, and vibrational density of states.34 The size of the system is initially set to achieve the experimentally measured density of 2.466 g/cm3.35 A total of 35 Si atoms, 85 O atoms, and 30 Na atoms were randomly inserted within a periodic cubic box of a = 12.686 Å, and the initial configuration was energyminimized to avoid any overlaps prior to glass formation. The glass was held for 0.5 ns with a constant number of atoms, volume, and energy (a microcanonical, or NVE ensemble). Because a thermal fluctuation of ∼400 K (from 2000 to 2400 K) was observed during the microcanonical run, the glass was allowed to evolve further at 2400 K for 0.5 ns with a constant number of atoms, volume, and temperature (a canonical, or NVT ensemble). Within the same ensemble, the melted system was then cooled at a constant cooling rate of 0.5 K/ps, and after the temperature had reached 300 K, it was equilibrated for 1 ns. Finally, a constant pressure of 1 atm was applied to the system (NPT ensemble) for another 1 ns to ensure no significant fluctuations in density during the equilibration process. Three different sodium silicate glasses of the same nominal composition were constructed via the aforementioned procedure using different initial atomic positions. These repeated simulations compensate for the limitation of small sample size by allowing us to better capture the statistical behavior of glass surfaces during the glass−water reactions. The final densities of the sodium silicate are presented in Table 1. These final structures were used as starting configurations for the glass−water reactions. Molecular dynamics (MD) simulations with the Teter potential creating three glass networks were carried out using the Large-Scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) package.36 After the bulk sodium silicate glasses were obtained, a reactive potential was employed to model the glass surface and subsequent glass−water interface, as the system of interest for

Table 1. Density of the Simulated Sodium Silicate Glasses after Relaxation at 300 K run 1 run 2 run 3

simulation box dimension a (cubic) (Å)a

density (g/cm3)b

12.979 12.630 12.737

2.303 2.499 2.437

Initial dimension: a = 12.686 Å. bExperimental density (70SiO2· 30Na2O mol %): 2.466 g/cm3. a

this study required characterization of the reactive processes during the simulation of surface phenomena. In this study, all reactive MD simulations were performed with the Na/Si/O/H parametrization using the ReaxFF reactive force field framework.15 The ReaxFF parameters were trained using a firstprinciples data set that describes water interaction at the sodium silicate glass−water interface. Further details of the ReaxFF methodology and its potential forms can be found in earlier publications by van Duin et al.13,15,17,37 As shown in Figure 1, a free surface was first created by expanding the c parameters of each of the equilibrated bulk

Figure 1. Representative example of the initial nonhydrated sodium silicate used in the hydration models. Color scheme: Si atom (ivory), O atom (red), and Na atom (blue).

glasses. This process results in a vacuum region above and below the two surfaces of the glass slab along the z-direction. These exposed surfaces were relaxed at 300 K for 100 ps with the ReaxFF reactive force field. Following the relaxation of both top and bottom surfaces, the vacuum region was filled with water molecules. The number of water molecules that are inserted in the vacuum region was controlled to have a density of ∼0.99 g/cm3. In addition, all water molecules were kept at least 2 Å from the uppermost and lowermost atoms of the glass surface to prevent any initial close contact with the surface. Glass−water reaction simulations were carried out at 300 K with the configurations shown in Table 2 for 500 ps in the NVT ensemble. From these runs, trajectories at every 100 ps 3956

DOI: 10.1021/acs.jpclett.9b01275 J. Phys. Chem. Lett. 2019, 10, 3955−3960

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The Journal of Physical Chemistry Letters Table 2. System Configurations for Sodium Silicate Glass− Water Reaction run 1 run 2 run 3

simulation cell (Å3)

number of water molecules

12.979 × 12.979 × 38.94 12.630 × 12.630 × 37.89 12.737 × 12.737 × 38.21

146 134 138

were obtained for the surface reactivity analyses. Figure 2 shows the initial and final positions of glass surface reaction with water at 0 and 500 ps, respectively. The hydrated surfaces of sodium silicate glasses from each run were used to investigate the adsorption behavior of a water molecule. Because the time evolution of surface reactivity is of key interest, the binding energy of a water molecule to the hydrated surface was calculated at 0, 100, 200, 300, and 400 ps. To evaluate the local heterogeneity of reactivity imposed by varied glass surface structures, each surface was divided into a 10 × 10 grid, and the binding energy at each site was mapped across the grid. The water molecule position in the z-direction from top and bottom surface was maintained to be 2.0 Å from the outermost atom during the energy calculation. The binding energies (Eb) were calculated as below, where the energy of a surface with adsorbed water (Esw) was taken with respect to the surface energy (Es) and the energy of molecular water (Ew). E b = Esw − Es − Ew

(1)

Figure 3. (Top) Schematic of water binding energy calculations. (Bottom) An example of the electronic-structure DFT calculation using semilocal exchange-correlation functionals finding the binding energy of a water “pixel” to the surface.

A negative binding energy would indicate that water adsorption to the surface site at the corresponding grid point is thermodynamically favorable. The binding energy map may then be used to locate where water binding would be most stable based on the ReaxFF calculations. From three independent glass−water reaction boxes, a total of six hydrated surfaces were obtained (a top and bottom surface from each run), increasing the statistical reliability of this analysis in the ensemble despite the small overall system size of any one box. The binding energy mapping process is shown in Figure 3. Similar calculations were carried out at an electronic structure level using the density functional theory (DFT) framework as implemented within the Vienna Ab-initio Simulation Package (VASP) software. Total energies were computed from the static geometries obtained from the reacted ReaxFF structures using the Perdew−Burke−Ernzer-

hof (PBE) exchange-correlation functional with projector augmented wave (PAW) pseudopotentials to describe the ion cores. A kinetic energy cutoff of 500 eV was used for the expansion of the plane waves. Due to the large noncrystalline structure, the Brillouin zone was sampled only at the Γ-point with 0.1 eV of Methfessel−Paxton smearing to help electronic convergence. Due to the computational cost associated with these DFT simulations, the results were primarily used to elucidate the electronic interactions between the water molecule and the glass surface (see Figure 3). To count the topological constraints in the structure generated from the ReaxFF MD simulations, an algorithm

Figure 2. Initial (a) and final (b) states of the water/glass interface. Note that only the top surface in contact with water is shown here. 3957

DOI: 10.1021/acs.jpclett.9b01275 J. Phys. Chem. Lett. 2019, 10, 3955−3960

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The Journal of Physical Chemistry Letters was developed to systematically exclude all nonbridging oxygens, alkali, and hydrogens from the structure. The number of surface rigid constraints (in the pixel) around each networkforming atom was then calculated; an example contour plot generated using this procedure is shown in Figure 4. The

Figure 5. ReaxFF MD-derived water binding energies plotted versus the number of constraints for surface atoms at the local pixel. Results show a broad maximum in which there is a near hydrophilic− hydrophobic transition of the surface. The error bars represent the standard deviation. A second system with 1500 atoms was performed to show convergence of the ReaxFF MD results.

reaction with the water molecule allows the glass surface to reduce the number of incompatible constraints, which in turn alleviates local stress in the network. In other words, a local region undergoes an alleviation of stress when Q4 units convert to Q3 units. The larger negative binding energy values in the underconstrained regions can trace their origin to the sites available for bonding with the water molecule. Their high number of degrees of freedom allows for the facile reaction of water, i.e., the only energy cost is for the oxygen to dissociate from the network. The isostatic network within the intermediate phase is able to preserve its structural integrity because the energy barrier to deform the network is high, and there is no localized stress to create an additional driving force for reaction with water. Indeed, the isostatic network is the most energetically favorable arrangement of a noncrystalline structure. This intermediate phase is governed by the fluctuations of the topology that arise with the minimization of stresses. These stresses change the local topology (within the range of the fluctuations) so that they become isostatic.38 This leads to an energetically flat region where there is a constant binding energy at the surface. The average number of surface constraints has been shown in previous work to be largely controlled by the annealing time.6 The surface constraints may then be used to predict surface reactivity. However, it is worth noting that the local number of constraints is not constant in time.20 Furthermore, Potter et al.1 showed that molecular water in the network can radically shift the free energy of the γ constraint. Therefore, the number of surface constraints is dynamic during the influx of water. Shifting constraint energy will also alter the fragility, which in turn alters the diffusion activation enthalpy. In the future, this may provide a path for developing more durable glasses; currently, it shows that a homogeneous intermediate phase (isostatic) surface should be targeted to achieve maximum chemical durability.

Figure 4. Example contour surface showing the average coordination per atom on the glass surface for the first run at 300 ps.

surfaces were then converted to a 10 × 10 pixel grid, and the average coordination of network-forming atoms in each pixel was calculated. The depth of each pixel was taken to be 4 Å below the surface. The average coordination number of the network forming atoms (r) was used to find the number of constraints (nc) in the pixel with r nc = 2r − 3 + (2) 2 Figure 5 shows the results from the MD binding energy studies, and where a clear transition in the region around 3.0 constraints/atom is observed. This region is isostatic because the number of constraints is equal to the number of translational degrees of freedom, and the width highlighted in the figure is evidence for the existence of an intermediate phase. To confirm that the binding energies had indeed converged, a larger system of 1500 atoms was simulated. Good quantitative agreement was found between the 150- and 1500atom systems, as shown in Figure 5. In this work, the isostatic behavior occurs over a wide range in which certain silicon network sites dramatically increase the binding energy (binding energy approaching 0). The state or structure with nc outside of the intermediate range readily interacts with water. Only those in the isostatic region are likely to remain unaffected after the interaction with water; thus, they are the rate-limiting species in dissolution of silica glass. The intermediate phase result is intuitive due to the implicit stability that comes with having an isostatic phase, which is both energetically favorable and stress-minimized. The larger negative binding energies in the overconstrained regions arise from large differences between the free energies of the chemically bonded state (chemisorbed) and unbound state (water in near proximity to the surface, but not bonded) in these regions; in the overconstrained region, the chemical

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected] (I.D.). *E-mail: [email protected] (A.C.T.V.D.). 3958

DOI: 10.1021/acs.jpclett.9b01275 J. Phys. Chem. Lett. 2019, 10, 3955−3960

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The Journal of Physical Chemistry Letters *E-mail: [email protected] (S.H.K.). *E-mail: [email protected] (J.C.M.).

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ORCID

Seung Ho Hahn: 0000-0002-1722-5631 Adri C. T. van Duin: 0000-0002-3478-4945 Seong H. Kim: 0000-0002-8575-7269 John C. Mauro: 0000-0002-4319-3530 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The ReaxFF-MD simulations were conducted with support from the National Science Foundation (Grant DMR1609107).

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REFERENCES

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