Nanoscale Wetting and Fouling Resistance of Functionalized Surfaces

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Nanoscale Wetting and Fouling Resistance of Functionalized Surfaces: A Computational Approach George Yiapanis,† Shane Maclaughlin,‡ Evan J. Evans,‡ and Irene Yarovsky*,† †

School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, GPO BOX 2476, Melbourne, Victoria 3001, Australia ‡ BlueScope Steel Research, Port Kembla, NSW, Australia

ABSTRACT: A computational modeling methodology has been developed and employed to characterize the nanoscale wettability and antifouling properties of functionalized hard and deformable surfaces, with a specific focus on poly(ethylene glycol) grafted substrates and their resistance to graphitic carbons. Empirical evidence suggests that the antifouling behavior of polyethylene PEG is associated with two main mechanisms: steric repulsions and hydration via formation of a structured water layer. However, there is also little attention paid to the contribution of steric repulsion vs surface hydration. We examine these two mechanisms through a combination of in silico contact angle and force measurements at the nanoscale level. We investigate the properties of the grafted functional chains and the underlying substrate, responsible for resisting surface deposition of graphitic contaminants in aqueous solution. Our results reveal that the fouling-release efficiency is enhanced when PEG chains are grafted onto hard hydrophilic substrates such as silica in contrast to deformable polymer substrates where surface modifications are effectively mitigated during interfacial contact with a hard contaminant. We conclude that the contribution of steric repulsion vs surface hydration to the antifouling ability of surfaces is strongly dependent on the nanoscale structure and deformability of the substrate. This generic method can be applied to examine individual contribution of steric repulsions and surface hydration to antifouling performance of grafted chains.

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INTRODUCTION

characterize nanoscale features has prompted an interest to relate the antifouling behavior to the nanoscale structure and chemistry of the surface. The intrinsic difficulties in the experimental characterization of nanoscale features and their effect on materials properties provide us with motivation to use computational modeling techniques to complement the experiments. Herein we present a theoretical simulation methodology to examine the fouling-release properties of polymer surfaces grafted with poly(ethylene glycol) (PEG) one of the most widely used surface protectors whose antifouling mechanisms have yet to be precisely characterized.11 Our technique enables one to decompose the surface foulingrelease properties into two terms: the first of which is associated with the surface’s affinity to the foulant (solid adhesion) and

Fouling is a general term used to describe the accumulation of unwanted material on solid surfaces in the presence of solvent (water). A detriment to the surface’s functionality and a major economic problem, fouling is estimated to cost manufacturing industries hundreds of billions of dollars a year.1 Fouling contaminants can present themselves in many forms and are not limited to biological molecules. In fact, fouling is considered a hierarchical event in time which begins by the clean surface being conditioned by organic molecules, debris, and contaminants from the environment.1 Antifouling chemical surface modifications have been used to maintain sterility of medical devices,2 to improve selectivity in nanobiomaterials,3 and in paint coatings to deter deposition of precipitated matter.4,5 Examples of fouling-release chemistries include stimuliresponsive molecules,6,7 zwitterionic molecules,8 carbohydrate derivatives,9 and self-assembled monolayers.10 The complexity of antifouling chemistries combined with a growing capacity to © XXXX American Chemical Society

Received: January 10, 2014 Revised: July 28, 2014

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The poly(ethylene glycol) grafting of the silica substrates was modeled via two PEG derivatives: poly(ethylene glycol) and poly(ethylene glycol) silane (2-[methoxy(polyethyleneoxy)propyl]trimethoxysilane). The reason we considered an additional PEG derivative is that the silyl ester group (Si− O−C) associated with direct grafting is considered hydrolytically unstable.19 All schemes considered involve a condensation reaction between hydroxyl groups (OH) at the substrate’s surface and terminal OH groups of the PEG derivative, in line with experiment.20−22 In the case of polyester, the surface was first functionalized with hydroxyl groups at surface density of 5.4 groups/nm2 to generate a hydroxylated polyester (polyOH).19 This model was subsequently tethered with poly(ethylene glycol) pentamers via the condensation reaction, fusing the oxygen of randomly identified alcohol groups along the surface, with the terminal oxygen of the PEG pentamers, at a surface grafting density of 0.3 PEG/nm2. We note the low molecular weight PEG oligomers considered here enable high grafting densities to be achieved which are of particular interest for self-cleaning surface design. The resultant PEGylated surface models were energy minimized, relieving any strain due to the formation of new bonds and re-equilibrated using molecular dynamics in the constant volume and temperature ensemble (4 ns). The PEGylated polyester and silica substrates are denoted poly-0.3PEG5 and silica-0.5PEG5, respectively, while silica-0.3PEG5-silane denotes the PEG-silane functionalized silica (Table 1). A Method To Calculate the Interfacial Adhesion in a Three-Phase System. In the context of surface fouling, the work of adhesion (Wslc) is the energy required to separate a contaminant from a solid surface in a liquid medium and can be directly linked to the components’ surface energies via the Young−Dupré equation23

the second with the surface’s affinity to water (wettability). We demonstrate that this technique can be used to correlate macroscopically observable wetting and interfacial adhesion with the nanoscale features of the surface. Provided the models of all individual components are reliable as validated experimentally, it can be used as a predictive tool for design of novel antifouling surface treatments.

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MODELS AND METHODS All-Atom Nanoscale Surface and Contaminant Models. To examine organic fouling-release properties, we constructed surface models of a highly ordered carbon allotrope (as a contaminant) and of amorphous silica and polyester surfaces (as functional substrates), with the atomistic structure of all the models comparing well with experiment.12,13 A carbon allotrope in the form of graphite (six-layered graphene sheets) is chosen to represent the primary surface chemistry of commonly encountered contaminants such as soot particulates.14−17 The silica and polyester surfaces represent typical industrial amorphous coatings with silica used to benchmark a generally hard “nondeformable” substrate, while polyester is used as a prototype of a relatively soft deformable polymer based material. To investigate fouling-release abilities for these surfaces when functionalized with PEG, we attached polyethylene pentamers with surface density of 0.3 PEG/nm2 to the silica and polyester models. The summary of all substrates examined is presented in Table 1. Table 1. Computed Contact Angle on Polyester, Silica, and Graphite Surfaces Together with the Calculated Interaction Energy between Surface and Contaminant in Watera surfaces examined 1. silica (fully hydrated silica) 2. silica-0.3PEG5 (PEGylated silica) 3. silica-silane-0.3PEG5 (silane-PEG grafted silica) 4. polyester (ungrafted polyester) 5. poly-OH (hydroxylated polyester) 6. poly-0.3PEG5 (PEGylated polyester) 7. 6-layered graphite (benchmark hydrophobic surface) 8. graphene (benchmark hydrophobic surface)

Young’s contact angle (deg)

Wslcb (mJ m−2)

22 ± 3 19 ± 6 17 ± 6

37 ± 14 −14 ± 17 −2 ± 19

117 ± 5 87 ± 5 73 ± 7 103 ± 6 (90− 98)43−45 127 ± 4 (127)43

95 ± 23 96 ± 24 81 ± 31 n/a

Wslc = Wsc − γl/v(cos θ l/s + cos θ l/c)

(1)

where Wsc is the free energy change in separating the contaminant from the solid surface in vacuum, γl/v is the interfacial free energy of the liquid/vapor phase, and θl/s and θl/c are the contact angles formed by the liquid droplet on the solid and contaminant surfaces, respectively. Note that Wslc can be positive (attraction between the solid surface and contaminant) or negative (repulsion between the solid surface and contaminant). A realistic simulation of adhesion between surface and contaminant for design of antifouling surfaces in either industrial or biomedical applications needs to include a threephase system, where a substrate surface is interacting with a contaminant in a liquid medium (e.g., aqueous solution). Therefore, one needs to take into account both the “dry” adhesion (Wsc) between the substrate and the contaminant as described by the first term of eq 1 as well as the wettability of the coating and contaminant γl/v(cos θl/s + cos θl/c), as defined by the second term of eq 1. On the basis of these considerations, we devised a two-step modeling approach to characterize the adhesion in the three phase systems: (step 1) evaluation of wettability of the substrate and contaminant surfaces by the liquid medium (water) via all-atom simulation of the water droplet on each of the surfaces and calculation of the static contact angle and (step 2) calculation of the dry adhesion between the substrate and contaminant in vacuum. Step 1 of the simulation procedure is described below while step 2 is described only briefly, with the full details published previously.19 The combination of the two

n/a

a

Experimental values where available are shown in parentheses. Wslc can be positive (attraction between the solid surface and contaminant) or negative (repulsion between the solid surface and contaminant). b The main source of error is associated with exctracting the interaction potential from mean force calculations as previously documented.19

The polyester substrate is composed of polyester chains containing on average 15 units of 2-butyl-2-ethyl-1,3-propanediol, 2 units of trimethylolpropane, and 16 units of isophthalic acid. The chains have been cross-linked based on a simulated curing algorithm12 with tributoxymethylmelamine resulting in a cross-linked polyester. It displays a density of 1.3 g/cm3, comparing well to experiment,18 an average film thickness of 15 Å, and a surface area of 1399 Å2 with equilateral dimension of 37.4 Å in the x and y directions. The vitreous silica substrate which is based on the work of Garofalini and co-workers13 has a silanol density of 5.4 groups/nm2, which represents a fully hydrated silica, an average film thickness of 14 Å, and a surface area of 743 Å2 with equilateral dimension of 27.3 Å. B

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dimension of 76.69 Å. To prohibit cross-interaction with the neighboring water droplets when periodic boundary conditions are applied, the polyester and silica-based substrates were replicated from their original size in the xy plane to achieve a maximum cell length of 187 and 218 Å, respectively. The complete substrate/droplet models contained between 5880 and 80 158 atoms in the unit cell. The 3D periodic models were then subjected to molecular dynamics in the NVT ensemble, allowing for a spontaneous wetting of the substrate by the spreading water droplet. Constraints applied to the substrates during the loading were also applied during the wetting simulations. The simulations were undertaken to allow the contact angle to reach a steady state (shown by example in Figure 1a) with the droplet spreading time to equilibrium

steps enables the adhesion in a three phase system to be evaluated as described in the Results and Discussion section for various surface types of surface substrates and contaminants. Step 1. Evaluation of nanoscale and macroscale surface wettability: in-silico contact angle measurements. Wettability by a liquid is traditionally investigated using contact angle measurements of the liquid droplets on the substrate surface. In the past decade molecular dynamic based techniques have been increasingly used to simulate the spreading of water droplets on solid surfaces.24−31 When compared to experimental procedures, these simulation approaches have the advantage of real-time molecular-scale observation of the wetting process. Most of the simulations, however, emulated wetting of the rigid, amorphous26,29 or crystalline surfaces24,25,30 rather than of flexible substrates.27,28,31 This is partly due to the complexities in evaluating the contact angle of water for deformable substrates, especially when using the Gibbs dividing surface approach.27,31 Because of a possible swelling and partial dissolution of segments at the outermost region of the flexible substrates27 it is necessary to simulate the interactions of such substrates with contaminants in aqueous environment. It is also important to ensure that during the allatom simulation the relaxation of the substrate and equilibration of the water droplet have been reached. For the nanoscale droplets, the contact angle will be influenced by the nature of the three-phase solid−liquid− vapor contact line which contributes an additional free energy per unit length, or line tension τ, to the excess free energy of the droplet, as described by a modified Young’s equation:32 τ cos θ = cos θ∞ − rbγl/v (2) where θ∞ is the contact angle in the limit of very large (macroscopic) droplets (Young’s contact angle), rb is the radius of the nanoscale droplet footprint in contact with the surface, and γl/v is the interfacial energy of the liquid/vapor phase (72.8 mJ m−2).33 According to eq 2, the quantities τ and θ∞ can be determined from plots of cos θ as a function of the droplet size (1/rb). Our approach involves undertaking a series of contact angle simulations by systematically varying the size of the droplet and plotting cos θ as a function of 1/rb, from which the macroscopic contact angle can be obtained through linear extrapolation and the line tension can be calculated from the gradient of the lines. Step 2. Calculation of the dry adhesion between the substrate and contaminant. The first term of eq 1, Wsc, is associated with the surface’s affinity to the contaminant in vacuum. Termed the “ideal work of adhesion”, it can be theoretically calculated by “in silico” force measurements as we have previously described in detail.18,19 A typical simulation involves a model organic contaminant particle or a surface being driven toward a substrate surface model in vacuum, during which the force between the contaminant and substrate is calculated as a function of separation distance in the direction perpendicular to the interface, generating a force−separation curve (Figure 2). The area under the force−separation curve (as shown) corresponds to the ideal work of adhesion (Wsc) while from the minimum of the force well, the adhesive force or “pull-on” force is determined. Simulation Procedures and Parameters. Nanoscale Wetting Simulations. Water droplets were generated from a pre-equilibrated water box and placed on top of each substrate including the graphite surface that displayed an equilateral

Figure 1. (a) Simulated spreading of a water droplet (2076 water molecules) on a solid surface. The composite system displayed here is contained inside a unit cell with x, y, and z dimensions of 112, 112, and 90 Å, respectively. This large cell size prohibits interference of neighboring water droplets when periodic boundary conditions are applied, even when complete wetting is observed. (b) The 3D mapping of a water droplet, from which the contact angle is determined, is also shown.

ranging from 200 ps to 6 ns. The 3D maps of the droplet from which the contact angle was extracted (Figure 1b) were generated by subdividing the x and y plane parallel to the surface, into square grids of 3 Å in length and within each grid, monitoring the maximum (Zmax) and minimum (Zmin) water level during the final 2 ns of simulation. By averaging the C

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minimum positions of the center of mass of the water molecules across all exposed grids (those containing water), we determined the base plane of the droplet from which the droplet dimensions were obtained. The contact angle, θ, of the water droplet on the substrate was determined from the contact area between the surface and the droplet (A) and height (h) of the droplet (Figure 1b). The height was defined as the maximum distance between the base plane and top of the droplet (in the perpendicular direction) measured within a 4 Å distance from the center axis of the droplet. The contact area made by the droplet on the surface (droplet footprint) was calculated as the surface area along the base plane of the droplet. To examine droplet size dependence on the contact angle, we undertook a series of wetting simulations by varying the radius of the water droplet from 20 to 30 Å. All simulations were carried out using DISCOVER code from Materials Studio Inc. Intermolecular interactions were evaluated using the COMPASS force field.34,34 Optimized for the simulation of condensed phase polymers and organic/ inorganic interfaces, the COMPASS force field has been demonstrated to predict cohesive properties of an extensive number of polymers including poly(ethylene glycol) oligomers35 as well as silica−organic interface properties.36,37 For energy minimization, the nonbonded interactions, including the Coulomb term for electrostatics and 6−9 Lennard−Jones potential for vdW, were calculated using the Ewald procedure with an accuracy of 0.01 kcal/mol and an update width of 1.0 Å. The conjugate gradient algorithm was used for energy minimization, with an energy convergence criterion of 0.01 kcal/(mol Å). To retain computational tractability during MD, nonbonded interactions were calculated using the atom-based summation method, with a cutoff radius of 15.5 Å, a spline width of 5 Å, and a buffer width of 2 Å. A long-range vdW tail correction was applied for nonbonded interactions larger than the cutoff radius. A 1.0 fs time step was used for MD with the Andersen thermostat38 employed to control the temperature at 298 K with a collision ratio of 1.0. Prior to the surface modification with PEG, the NPT (constant pressure and temperature) ensemble with the Berendsen barostat was used to control the pressure (1 atm) and equilibrate the densities of the substrates.19 For the loading and wetting simulations, the NVT (constant volume and temperature) ensemble was employed and trajectories were generated by saving the data in 10 ps time intervals, with total simulation times varying between the systems to ensure equilibrium, as described in the loading and wetting process modeling procedures below. Dry Adhesion Simulation. In silico force measurements were undertaken by first forming an interface between the substrate and graphite in a unit cell placing the carbonaceous surface at a vertical distance at least 26 Å from the substrate, where its interaction with the substrate is negligible (Figure 2). The graphite layer is then displaced by an initial step size of 3 Å in the vertical direction, toward the substrate. After each displacement, molecular dynamics was undertaken, during which the PEG chains and the underlying polymer were equilibrated ensuring that the energy of the system and radius of gyration of the tethered chains attained steady values, which typically happened within 1 ns as documented previously.19 Relaxation times for low molecular weight PEG are in the order of ∼10−100 ps in dilute solutions.39 During molecular dynamics, the force and distance between graphite and substrate were measured in the direction perpendicular to the

Figure 2. Schematic representation of in silico loading, whereby deformable surfaces are probed using a graphite surface in vacuum. A typical force plot obtained during loading allows determination of the ideal work of adhesion and the adhesive force between graphite and the probed substrate.

interface, while the graphite model was kept in a fixed geometry maintaining graphite’s structural integrity through the entire loading procedure. The lowermost portion of the polymer substrate (3 atom %) was also constrained, providing a rigid support during loading. Hence, 97 atom % of the polymer substrate was free to move during molecular dynamics. When a graphite−substrate separation of at least 12 Å was attained, the step size was reduced to 0.25 Å, and the loading procedure continued. Loading was terminated when the graphite surface advanced well past the point of interfacial equilibrium, defined as the position where the net force between graphite and substrate surface is zero. This loading procedure was used to generate a force versus separation curve (Figure 2), where the area underneath the curve, obtained by numerical integration, corresponds to the ideal work of adhesion (Wsc). Because the graphite layer and the lowermost portion of the polymer representing the “bulk” substrate were constrained during simulations, it cannot be expected that the procedure generates fully reversible force profiles, and therefore the values presented here are not yielding the exact free energy but rather the estimates. However, we note that the effect of the “bulk” (below 13 Å from the surface) on the interfacial adhesion can be reasonably assumed negligible. A more detailed description of the in silico loading process has been presented elsewhere.18

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RESULTS AND DISCUSSION The most direct and critical property related to fouling release is interfacial adhesion. Here we present the results of application of our modeling approach to characterize the interfacial adhesion in aqueous solution between model carbonbased contaminants and PEG-functionalized hard (silica) and soft (polymer) surfaces, using a two-step procedure: (1) in silico water contact angle measurements of the surface and contaminant using nanosized droplets and (2) in silico surface−contaminant dry adhesion force measurements. The antifouling ability of the substrate is then deduced from the combination of properties obtained in steps 1 and 2. All models are summarized in Table 1. Nanoscale Surface Wetting and in Silico Contact Angle Measurements. Figure 1a shows a typical simulated spreading of a spherical nanosized water droplet of 25 Å radius including the evolution of equilibrium three-dimensional snapshots of the water droplet, from which the nanoscale contact angle was determined (Figure 1b). We undertook a systematic series of such nanoscale contact angle simulations by D

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varying the size (radius) of the spherical droplet (rb) and plotting the nanoscale contact angle (cos θ) as a function of 1/ rb (Figure 3a), from which the macroscopic contact angle and

Our predicted macroscopic contact angles for graphene and graphite, 127° and 103°, respectively, are in an close agreement with the experimentally measured contact angles of 127° for graphene43 and 90°−98° for graphite.43−45 The difference in contact angle between multilayer carbon and corresponding single-layer structures can be attributed to augmented vdW interaction between water and the extra carbon layers25 and is in agreement with recent molecular dynamic studies.46,47 It should be pointed out here that despite Young’s modified equation been widely used,24−26,48−50 a shortcoming of this model is that the macroscopic contact angle extracted from linear extrapolation of the simulated contact angle values can be dependent on the range in droplet sizes used in the simulations. For example, using a droplet size within the range between 21 and 30 Å, we have predicted the macroscopic contact angle of graphene to be 127° with a negative line tension value of −1.0 × 10−10 J/m. Shih et al.46 used a broader size range between ∼15 and 39 Å, yielding a macroscopic contact angle of 104° and a negative line tension value of −0.3 × 10−10 J/m. Scocchi et al.50 examined an even wider size range between 15 and 61 Å which leads to positive line tension value for droplet sizes larger than 30 Å and negative line tension values for smaller droplet sizes. Additionally, it has been shown that the predicted macroscopic contact angle is highly dependent on the interatomic potential parameters employed to describe the Lennard-Jones interactions between water and graphitic carbon.24 Therefore, each set of results has been obtained in a specific framework and, to a certain extent, is dependent on the choice of the parameters used in particular simulations. It may be rather fortuitous that our contact angle of water on graphene is predicted very close to experiment; however, as the model is applied consistently within a chosen simulation framework, it is shown to be effective in predicting the relative differences in the wetting behavior of different surface types. Specifically, we obtained qualitative experimentally consistent differences in behavior of polyester and silica substrates, while also detecting differences in the wetting behavior of a surface as a result of surface grafts (e.g., polyester vs PEGylated polyester). However, further work is necessary to systematically investigate the influence of different simulation parameters to help in understanding the wetting phenomena at the nanoscale. Our macroscopic water contact angle for polyester is estimated to be 117°, reflecting the hydrophobic character of the surface. When the polyester surface is functionalized with hydroxyl or PEG, the contact angle is reduced by 30° and 44°, respectively. Experimental results show a similar trend11,51−53 with the covalent attachment of OH and low molecular weight PEG to polyester (PET) decreasing the contact angle by 15° and 20°, respectively.54 The increased hydration is attributed to the interaction of polar surface functional groups with water molecules. This is demonstrated in Figure 4a displaying the radial distribution functions (RDFs) of interatomic pairs comprising the carbon, hydrogen, oxygen, and nitrogen atoms of the polyester surface and the oxygen atoms of water molecules. Considering that the peak at 1.8 Å and shoulder at 2.8 Å are associated with formation of interfacial hydrogen bonds, the RDFs suggest that the improvement in wettability of PEGylated and hydroxylated polyesters is largely a result of the specific interactions between the surface and water. Moreover, Figure 5a shows a correlation between the simulated contact angle and H-bond number per unit area for all polyester based substrates examined. Here, a hydrogen bond was defined using geometric criteria55 with a cutoff distance of 2.5 Å and a

Figure 3. (a) Plot of cos θ versus droplet size (1/rB) for droplets of water on solid surfaces. The lines are linear fits to the data from which the line tension τ and Young’s contact angle θ∞ were extracted. We use linear interpolation to extract the Young’s contact angle from the y-intercept of the lines. Furthermore, from the slope of the lines we calculated the line tension of the various surfaces. (b) Young’s contact angles (blue bars) and line tensions (red squares) of the examined surfaces. The variation of contact angle with surface composition is evident, with PEG grafted surfaces displaying an increase in wettability compared to their ungrafted counterparts.

the line tension values were obtained (see Table 1 and Figure 3b). Our calculated line tension values lie within the experimentally estimated range of 1 × 10−10 and 1 × 10−12 J m−2.40 It can be seen from Figure 3b that as the surface wettability improves with the inclusion of hydrophilic surface modifiers such as the hydroxyl groups or PEG, the magnitude of the line tension diminishes, in qualitative agreement with experiment.40,41 For polyester based substrates and surfaces of carbon allotropes where the contact angle is generally >90° we predict negative line tension values, meaning that wettability is enhanced as the nanodroplet size is reduced. For superhydrophilic surfaces where the line tension approaches a positive transition, we note an improved stability with a reduced droplet size. These results demonstrate that the line tension is strongly dependent on the nanoscale wettability of the solid phase.42 E

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Figure 4. Radial distribution functions between interatomic pairs comprising water and surface atoms of (a) silica and (b) polyester. The peak at 1.8 Å and shoulder at 2.8 Å are indicative of hydrogen bonding between surface and water. The RDF’s have been normalized to account for cell size and substrate thickness differences between systems.

Figure 5. (a) Contact angle as a function of H-bond concentration per unit area of the surface water interface. (b) Decomposition of the interactions within a three-phase system comprising of a solid surface, solvent (water), and contaminant surface (graphite): Wsc is the work of adhesion between the solid surface and contaminant in vacuum (red bars), Wsl represents the work of adhesion between the solid surface and water (green bars), and Wslc is the work of adhesion between the solid surface and contaminant in solution (blue bars).

donor−hydrogen−acceptor cutoff angle of 120°. The figure demonstrates a heuristic power-law relationship between contact angle and the concentration of the interfacial Hbonds, suggesting that it may be possible to predict the surface wettability from the calculated surface water H-bond density. For example, simulations of an “infinite” interface composed of the PEGylated polyester surface and water yield a surface water H-bond density of 5.6 × 10−3 Å−3, which according to the power law relationship of Figure 5a corresponds to a contact angle value of 65.7°. This value compares well with the macroscopic contact angle of water on PEGylated polyester (73 ± 7°, open circle data point) which was obtained from linear extrapolation of our simulated droplet measurements. Furthermore, the power-law relationship suggests that surfaces with contact angles larger than ∼60° can be made more hydrophilic by incorporating additional H-bond capable functionality (e.g., PEG) until a surface saturation plateau is reached, after which any further increase in concentration of interfacial H-bonds will not lead to any significant improvement in wettability. In the case of silica, studies have suggested that the surface wettability will depend greatly on the silanol surface density. Fully hydroxylated states of silica, which typically display a silanol coverage in the range of 4.2−5.7 OH groups/nm2, yield contact angles of 0−5°.56,57 In comparison, we have predicted that water droplets on amorphous silica with silanol coverage of 5.6 OH group/nm2 forms a Young’s contact angle of 22 ± 3°, which is slightly higher than expected. This variation may be

attributed to the heterogeneity of the surface. Grafting of this model silica surface with PEG segments does not result in any significant difference in wetting behavior between hydroxylated and PEGylated silicas.57 In comparison,20 it was shown that low molecular weight PEG silanes grafted onto the surface of silicon reduce the advancing contact angle from 50° to 38° but lead to no discernible difference in the receding contact angle. The peaks at 1.8 Å in the RDFs of silica based surfaces (Figure 4b) show no significant variation, aside from a slight broadening in the case of PEG-silane modified silica. This lack of variation suggests that grafting a fully hydroxylated silica with PEG does not necessarily increase the surface interaction with water via hydrogen bonding as is generally assumed.21,57,58 Furthermore, for silica the variation in contact angle with H-bond number per unit area does not follow the power-law relationship observed in the case of polyester (Figure 5a). In fact, the wettability of PEGylated silica seems to be unaffected by the concentration of the interfacial H-bonds, suggesting that the hydroxylation saturation plateau may have already been attained. Large deviations among the RDFs of silica based surfaces is observed only at interatomic separations over 3 Å, which suggests that tethering of hydroxylated silica with PEG primarily increases the nonspecific interactions, such as vdW and electrostatic, at the water/surface interface. This may explain the slight reduction in contact angle of PEG modified silicas compared to the fully hydroxylated silica. F

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In Silico Surface−Contaminant Dry Adhesion Force Measurements. The adhesive force values between graphite (a model contaminant) and various PEGylated substrates in dry environment (vacuum) are shown Figure 6a. The results

carpeting the surface (lying parallel to the interface) which will lead to the loss of subnanoscale surface roughness and enhanced interfacial adhesion, as illustrated in Figure 6b,c. Conversely, for hard substrates with higher stiffness coefficient, as in the case of silica,19 steric forces between the PEG chains and the adhering contaminant surface are augmented due to the subnanoscale roughness able to be maintained at a greater degree during adhesion of the approaching surface (in Figure 6d,e). This explains the PEG’s ability to exclude organic based contaminants when the chains are grafted onto hard solid surfaces in a vacuum.19 Antifouling Ability of the Substrate. Figure 5b presents a decomposition of the interactions within a three-phase system comprising a polymer substrate surface interacting with the carbon-based contaminant in aqueous solution. The parameter Wslc provides a quantitative measure of the fouling-release ability of the substrate. It can be seen that the silica surface displays enhanced antifouling capabilities compared to the hydrophobic polyester, with a 61% reduction in Wslc, largely due to silica’s strong interaction with water. The trend is consistent with experimental observations that surfaces treated with colloidal silica are more hydrophilic and less prone to contamination.59 Polyester surfaces grafted with silanol groups have also demonstrated a 34% reduction in biofilm formation believed to be to be due to an increase in hydrophilicity of the Si−OH groups.54 In addition, a recent study has indicated suppressed adsorption of carbon allotropes such as graphene onto silica surfaces in water60 attributed to the formation of an interfacial water layer between the silica and graphene that prevents direct interfacial contact. Grafting the PEG pentamers onto the polyester substrates improves the fouling-release properties of the coatings. However, this improvement is rather modest (15%) due to a relatively strong adhesion between the contaminant and PEGylated polyester (Wsc) associated with a significant surface deformation upon contact with the contaminant. In contrast, grafting the PEG pentamers onto the silica substrates improves the fouling-release properties considerably. In fact, work of adhesion, Wslc, values become negative, which indicates that the contact between the hydrophobic contaminant and the PEGylated silica in water is energetically unfavorable. The reason for this is twofold. First, the PEGylated silica is strongly hydrophilic, yielding contact angle values smaller than 19 ± 6°. Therefore, in an aqueous environment a water layer will be maintained on the surface which would effectively screen its interactions with hydrophobic contaminants, as is also true for nonfunctionalized silica. However, the second reason for the effectiveness of silica PEGylation compared to that of the polymer is not so obvious. It is related to the difference in mechanical deformability between soft (organic) and hard (inorganic) substrates. The polyester is highly deformable, and any modifications undertaken to its surface will be effectively lost over time due to the natural flexibility of the substrate causing the “hydrophobic recovery”, as has been previously shown.61 In contrast, the silica surface is much more rigid and hence its modifications (e.g., with PEG) are more robust, which assists in maintaining the contaminant at significant separation distances from the substrate under dry conditions.19 This also enables a high level of hydration of the surface so the contaminant can be more easily washed off in water. Experimental confirmation of the reported behavior has been difficult to identify, largely due to the differences in molecular weight of PEG grafts, substrate composition, and contami-

Figure 6. Examination of PEGylated surfaces interacting with graphite in vacuum. (a) The bar chart illustrates the effect of the underlying substrate on adhesion. (b−e) Topographic profiles of PEG grafted polyester (b, c) and silica (d, e) surfaces, where the colors indicate the degree of extension from the substrate. The initial and postadhesion states are illustrated in the left and right panels, respectively. PEG− polyester systems (b, c) are able to conform to the approaching surface, resulting in significant loss in subnanoscale roughness and carpeting of the surface by the chains. When PEG is grafted on to the less deformable silica substrate (d, e), the subnanoscale roughness is maintained to a greater degree.

indicate that at a grafting density of 0.3 PEG/nm2 the ability of PEG to reduce adhesion in vacuum is strongly dependent on the deformability of the underlying substrate. When the substrate is flexible (as in the case of the polyester surface) and irregular, both the grafted chains and the substrate are able to conform to the approaching surface, thereby maximizing their interfacial contact and rendering the interface more fluidlike than solid-like. In other words, steric repulsion between the grafted chains and contaminant is reduced.19 As a result, the grafted PEG segments on the flexible polymer can adjust by G

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National Computational Infrastructure (NCI), the Western Australian computational facility (iVEC), the Victorian Partnership for Advanced Computing (VPAC), and the Victorian Life Sciences Computational Initiative (VLSCI). We are also thankful to Dr. Andrew Christofferson for helpful comments.

nation species considered. However, it has been previously shown that the attachment of low molecular weight PEG onto polyester increases the polymer’s wettability but does not result in any significant improvement in antifouling performance (no reduction in biofilm formation was observed).11 This is in accordance with our performance assessment of PEG pentamers grafted onto polyester substrates. Alternatively, experimental evidence has suggested that when low molecular weight PEG molecules are grafted onto silica, a slight improvement in wettability induces a significant improvement in antifouling performance (∼93% reduction in protein adsorption).20 These studies reveal that the substrate can have a significant impact on the antifouling performance of low molecular weight PEG and our molecular dynamics approach provides a plausible explanation for this phenomenon.

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CONCLUSIONS The presented simulations provide molecular level explanation of the well-documented antifouling properties of PEGylated surfaces using a molecular dynamics based methodology that decomposes interfacial adhesion in a three-phase environment into the “dry” and “wet” components using the Young−Dupré equation. A computational modeling methodology has been developed and employed to characterize the nanoscale wettability and antifouling properties of functionalized hard and deformable surfaces, with a specific focus on poly(ethylene glycol) grafted substrates and their resistance to graphitic carbons. Using our method, we examined two empirically suggested mechanisms of the antifouling behavior of PEGylated surfaces: steric repulsions and hydration via formation of a structured water layer. We employed a combination of in silico contact angle and force measurements at the nanoscale level and related them to the macroscopically observed properties, including the antifouling behavior of the grafted surfaces. We investigated the properties of the grafted functional chains and the underlying substrate, responsible for resisting surface deposition of graphitic contaminants in aqueous solution. Our results reveal that the fouling-release efficiency is enhanced when PEG chains are grafted onto hard hydrophilic substrates such as silica in contrast to deformable polymer substrates where surface modifications are effectively mitigated during interfacial contact with a hard contaminant. We conclude that the contribution of steric repulsion vs surface hydration to the antifouling ability of surfaces is strongly dependent on the nanoscale structure and deformability of the substrate. This generic method can be applied to examine individual contribution of steric repulsion and surface hydration to antifouling performance of grafted surfaces provided the molecular models of all individual components are reliable as validated experimentally.

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REFERENCES

AUTHOR INFORMATION

Corresponding Author

*E-mail [email protected] (I.Y.). Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We acknowledge the award of an Australian Research Council Linkage Grant in partnership with BlueScope Steel to carry out this work. We acknowledge the generous allocation of high performance computational resources from the Australian H

dx.doi.org/10.1021/la500114k | Langmuir XXXX, XXX, XXX−XXX

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