Article pubs.acs.org/JPCC
Potential Application and Molecular Mechanisms of Soy Protein on the Enhancement of Graphite Nanoplatelet Dispersion Yead Jewel,†,‡ Tian Liu,†,‡ Allen Eyler,† Wei-Hong Zhong,*,† and Jin Liu*,† †
School of Mechanical and Materials Engineering, Washington State University, Pullman, Washington 99164 United States S Supporting Information *
ABSTRACT: A stable dispersion of graphitic nanofillers in aqueous solution is an important prerequisite for the applications and development in graphitic nanofiller-based nanocomposites. Traditional treatments of the graphitic surfaces with different chemicals are time and energy consuming, and more seriously raise great environmental concerns. In this study, through combination of simulations and experiments we demonstrate that the soy protein, one of the most widely available proteins from the renewable resource, when properly denatured by trifluoroethanol (TFE) and heat, can be utilized to effectively treat the graphite nanoplatelet (GNP) surface and improve the dispersion. Through molecular simulations we find that the tertiary structures of soy protein are mostly destroyed under the action of TFE at high temperature. As a consequence, most aromatic residues which are originally hidden inside the hydrophobic cores become accessible and form π−π stacking interaction with the GNP surface, strengthening the adsorption of soy proteins onto the GNP surface. The adsorption of soy protein modifies the GNP surface energy, reduces the interaction force among GNPs and leads to better dispersion. Our simulation results agree with the experimental measurements on GNP dispersion. The work herein demonstrates the importance and the potential of the concept that a protein, when its structures are properly manipulated, can be exploited in nanotechnologies to improve performance and explore new functionalities.
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different proteins, such as ferritin,18 bovine serum albumin,19 and histone20 have been demonstrated as effective dispersants for CNTs. Compared with DNA and small peptides, proteins are more advantageous because they contain much richer reactive groups and structures, which may be manipulated for multiple interactions with graphitic materials and other components. Soy protein, a natural protein derived from the soy plant, is one of the most widely available proteins from the renewable resource. Soy proteins are composed of 18 kinds of amino acids, and over 60% of the residues are polar and reactive.21 Moreover, soy protein has multiple levels of structure including primary, secondary, tertiary and quaternary structures. Owing to its availability, low cost and richness in composition and structure, the soy protein has been actively investigated as a component for next-generation polymer materials recently.22−24 However, the soy proteins must be denatured before they can be used effectively in those applications. In the present work, we demonstrate that soy protein, when properly denatured, can be utilized to effectively improve the dispersion of GNPs in aqueous solution. We studied the effects of trifluoroethanol (TFE) and heat on the structural changes of soy protein. Through statistical analysis of GNP agglomerate size and morphology study of optical and electrical images, we found that the dispersion of GNPs can be significantly
INTRODUCTION Graphitic nanofillers, including carbon nanotubes (CNTs), carbon nanofibers (CNFs), and graphite nanoplatelets (GNPs), have been extensively studied for a wide range of applications in nanocomposites, nanoelectronics, biomedical devices, etc.,1−5 due to their exceptional mechanical, electrical, thermal, and optical properties.6 However, the aggregation and/or entanglement of graphitic nanofillers because of the strong attractions between graphitic surfaces have seriously limited their performance. Tremendous efforts have been devoted to various treatments, such as dry/wet oxidation,7 plasma treatment8 and electrodepostion,9 of the graphitic surfaces with a variety of different chemicals, including diaminoalkanes, poly(sodium 4styrenesulfonate), aryl diazonium salts, etc.10−12 These treatments highly rely on complicated procedures with many chemicals, and the rinsing processes after treatment are extremely time and energy consuming. More seriously, the toxicity of the chemical dispersant raises great environmental issues and therefore significantly hinders their industrial applications. Biomolecules, such as single-stranded DNA, peptides and proteins, are great alternatives to those chemicals for treating graphitic nanofillers to overcome the environmental concerns. For example, Zheng et al.13 have reported that the bundled single-walled CNTs could be effectively dispersed in water through sonification by adding single-stranded DNA. Short peptides with different functional groups have also been designed for dispersion of single-walled CNTs in water by Dieckmann and co-workers.14−17 More recently, several © 2015 American Chemical Society
Received: September 18, 2015 Revised: November 4, 2015 Published: November 5, 2015 26760
DOI: 10.1021/acs.jpcc.5b09126 J. Phys. Chem. C 2015, 119, 26760−26767
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Figure 1. Measurement of GNP dispersion in SPI solution under different conditions. (a) Distribution of the radius of the GNP agglomerations. The lines represent Gaussian fitting of the experimental data. (b) FESEM images of the corresponding GNP agglomerations. Insets are TEM images of individual GNP particles.
ments, one drop of each solution was placed onto a glass slide and allowed to evaporate at room temperature. Then the field emission scanning electron microscope (FESEM) and transmission electron microscope (TEM) images were taken for the dried suspension. After that, an in-house MATLAB program was implemented to binarize the images and measure the nanofiller aggregates.29,30 The radii of GNP aggregates were recorded as half of the largest point-to-point distance on the agglomerate. To ensure the accuracy of this study, the data for GNP aggregation size distribution were collected from 15 randomly selected images for each suspension. To supplement the quantitative data, both representative FESEM and TEM images were selected for visualization. Simulation Methods. All MD simulations were performed using GROMACS package 4.3.1.31 The GROMOS 53A6 force field was applied to the protein;32 the SPC model was chosen for water molecules; the CHARMM force field33 was adopted for graphene and the parameters of the TFE force field were taken from Chitra and Smith.34 Three chlorine ions were added to neutralize the system. The missing amino acids of the protein have been identified and superimposed using Swiss program35 (http://swissmodel.expasy.org/). Periodic boundary conditions were applied in all three directions. The long-range electrostatic interactions are treated using particle-mesh Ewald (PME) method and the van der Waals interactions were calculated using LJ potential with a cutoff of 10 Å. After minimization of the system, a short NVT simulation was performed, after which the system was equilibrated at constant temperature (300 K) and constant pressure (1 bar) for 10 ns using Berendsen thermostat and Parrinello−Rahman barostat36 respectively. Production simulations were then carried out for 200 ns using NPT ensemble simulations with time step of 2 fs. The simulation box was around ∼72 × 72 × 72 Å3 throughout the simulations.
improved by soy protein in TFE solution at high temperature. To elucidate the molecular mechanism of this phenomenon, we performed all-atom molecular dynamics (MD) simulations and found that the improvement of GNP dispersion was directly related to the disruption of the soy protein tertiary structures and the subsequent exposure of aromatic residues. Previous research on graphitic materials-protein systems has concentrated on the effects of graphitic materials, such as CNT,25,26 graphene27,28 on the protein structural changes, and the resulting potential toxicity effects. In this work, we show that by controlling the structures, an existing protein can be exploited to modify the properties of graphitic materials.
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EXPERIMENTAL AND THEORETICAL METHODS Materials. Pre-exfoliated graphitic nanoplatelets (GNPs) were obtained from XG Science Inc. The average diameter and thickness of the GNPs are ∼5 μm and ∼5 nm, respectively. Soy protein isolate (SPI) was donated by ADM Co. and contained 4.8% moisture and 90.9% dry basis protein. Trifluoroethanol (TFE; ≥ 99% technical grade) was purchased from SigmaAldrich. Methods. To evaluate the dispersion of GNP in aqueous environment, the original GNPs were first ultrasonicated and then mixed into different SPI solutions at different conditions (weight ratio of GNP and SPI = 1): (1) The SPI solution was kept at room temperature by bath-type sonication for 1 h. (2) The SPI solution was kept at 363 K by oil bath for 1 h. (3) TFE was added to SPI solution and then the mixture was bathsonicated at room temperature for 1 h. (4) TFE was added to SPI solution and then the mixture was kept at 363 K in an oil bath for 1 h. (5) TFE was added to SPI solution, and the mixture was kept at 363 K using oil bath for 1 h before being cooled to room temperature. In the TFE mixture, the TFE concentration was 30% (v/v). The GNP aggregates obtained at different solutions are stable up to 2 days at room temperature from precipitation studies. Characterizations. The GNP dispersion in different environments was quantitatively analyzed through the measurement of the radius of GNP aggregates. During the measure-
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RESULTS AND DISCUSSION Measurement of GNP Dispersion. We have performed experiments to measure the dispersion of GNPs in soy protein isolate (SPI) solutions. SPI is one of the most common 26761
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The Journal of Physical Chemistry C products from soybean and contains >90% of protein. Four different solutions were prepared: water at 300 K, water at 363 K, TFE solution at 300 K and TFE solution at 363 K (see the Methods for details). The statistical analysis on GNP dispersion in different aqueous solutions is shown in Figure 1a. As shown, in water a broad range of the GNP agglomerate size, from nano to micron levels, is observed. The radius of the aggregated GNP particles are concentrated around 2 μm at both room temperature and 363 K, increasing the temperature only slightly reduces the peak of the GNP particles. Adding TFE at room temperature slightly affects the size distribution of GNP particles; however, at high temperature, the peak particle size is significantly reduced to 600 nm. The inset of Figure 1a shows the distribution for particles with size larger than 3 μm. As illustrated, there are nearly no GNP particles larger than 5 μm in TFE solution at high temperature, but there exist very large particles (even larger than 10 μm) in the other three cases. The large shift in GNP agglomerate size suggests that the significant improvement of GNP dispersion can be achieved by the SPI in TFE solution at high temperature. The comparison of GNP dispersion under different conditions can also be welldocumented by the morphology studies, as presented in Figure 1b by field emission scanning electron microscopy (FESEM) and transmission electron microscope (TEM) images. Clearly, GNP particles are gathered together to form agglomerations in the samples treated by water. In contrast, much less aggregations of GNP particles are observed in the two TFE modified samples, especially for the sample of high temperature. Comparing the inset TEM images (Figure 1b inset), both the samples treated by water exhibit very low transparency, which is due to the stacking of many GNPs. However, the significantly improved transparency of GNP particle can be observed in the samples with TFE treatment, reflecting that the GNP dispersion has been improved effectively. Computational Analysis and Molecular Mechanisms on Soy Protein Denaturation and GNP Dispersion. Experiments above indicate that the dispersion of GNPs in aqueous solution can be significantly improved by soy protein when properly treated by TFE and heat. The denaturation of the soy protein and the protein-GNP interactions are complex. The whole process involves many different interactions among TFE, water, soy protein and GNP under different conditions. Here, we have performed a series of all-atom MD simulations to investigate the underlying molecular mechanisms. Soy protein is a mixture of proteins containing 2S, 7S, 11S, and 15S. Among them, the 7S (or β-conglycinin) and 11S (or glycinin) comprise more than 80% of the total soy protein content.37,38 The 7S is composed of three types of subunits, α, α′ and β which interact to produce different heterotrimers and homotrimers.39 The 11S consists of five kinds of subunits forming a hexamer by face-to-face stacking of two trimers.40 The subunits of 7S and 11S are compositionally and structurally similar. Figure 2a shows the crystal structure of a 7S homotrimer formed by three α′ subunits (PDB ID 1UIK). As shown, each subunit can be divided into two very similar modules related by a pseudodyad axis. Each module contains a core barrel domain formed by β-sheets at the center of the edges and an extended loop domain containing several αhelices at the hinges. In the native state, the majority of the hydrophobic residues (including many aromatic residues, such as phenylalanine and tyrosine residues) are encapsulated inside the barrel domain and the extended loop domain forming hydrophobic cores. Because of the computational limitation
Figure 2. Illustration of the simulation system. (a) Crystal structure of the 7S homotrimer formed by three α′ subunits. The cyan portion representing the whole 7S is simulated in our simulations. (b) Simulation box of TFE solution containing SPI (cyan), TFE molecules (yellow), and water molecules (red dots).
and structural similarity, we only simulate one of the modules (residues 148 to 350 as indicated by cyan color in Figure 2a) in our simulations. The module protein is initially placed at the center of a simulation box of ∼72 × 72 × 72 Å3 as shown in Figure 2b. Each simulation runs for 200 ns and three independent realizations are performed in each case (see the Methods for details). Since all three realizations produce similar results, only one of the realizations from each case will be presented in the following sections. First, we focus on the denaturation of the soy protein. To make a direct comparison with the experiments, four cases were created: water (at 300 and 363 K) and TFE solution (at 300 and 363 K). It has been reported that the accumulation of TFE on protein occurs in the concentration range of 0% to 80% (v/ v), with a maximal clustering at ∼30%. TFE-induced protein conformational changes can be achieved at concentrations higher than 30%.41 Therefore, consistent with our experiments, the concentration of the TFE solution in our simulations is set to ∼30% (v/v) by adjusting the number of water and TFE molecules. Figure 3a shows the evolution of the radius gyration (Rg) during 200 ns simulations (400 ns simulations for TFE solutions). It is clear that, in water at 300 K, the protein structure is quite stable and the Rg value is minimal indicating a compact configuration. At 363 K, the protein becomes less stable but the overall structure is still compact. However, in TFE solution especially at high temperature, we find a much greater value of Rg with larger fluctuations, indicating a significant change to the protein structure. To further understand the structural changes, we calculate the number of native hydrophobic contacts (NHC) formed within the protein at different conditions as shown in Figure 3b. In our calculations the side chains of two hydrophobic residues are considered as in contact if the distance between one of the carbon atoms on hydrophobic groups (one of the carbon atoms on methyl groups (Ala, Val, Ile, and Leu) and the number 1 carbon atom on aromatic rings (Phe and Tyr)) is within 5.0 Å. As shown, the number of native hydrophobic contacts is around 40 in water both at 300 and 363 K, but drops significantly in TFE solution to ∼30 at 300 K and ∼20 at 363 K, indicating the disruption of the hydrophobic cores. The reduction of the hydrophobic interactions is directly related to the breaking of the tertiary structures as illustrated by the snapshots in Figure 3d. As a consequence, the hydrophobic residues originally hidden inside the barrel and extended loop domains become accessible to the solvent. Figure 3c shows the solvent-accessible 26762
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Figure 3. Denaturation of the soy protein. The time evolution of the (a) radius of gyration (Rg), (b) number of native hydrophobic contacts (NHC), and (c) solvent-accessible surface areas (SASA) in water and TFE solutions at different temperatures. (d) Ribbon representations of the protein structure at different conditions.
surface areas (SASA) for the hydrophobic residues using the double cubic lattice method.42 Consistent with the Figure 3a,b, the SASA of hydrophobic residues is significantly increased in TFE solution, and becomes maximal at high temperature. A movie illustrating the soy protein structural changes in TFE− water solution at 363 K is provided in the Supporting Information. The effects of TFE as a cosolvent on the structures of peptides and proteins have been extensively studied in experiments43−45 and atomistic simulations.46−48 It was found that TFE molecules tend to accumulate at the surface as well as in the interior of the proteins. The TFE accumulation generates multiple effects, such as weakening of hydrophobic interactions, strengthening electrostatic interactions and stabilizing backbone hydrogen bonds. Therefore, the TFE has a mixed effect of destroying the tertiary structures and stabilizing the secondary structures on a protein. Increasing the temperature, on the other hand causes the disruption of hydrogen bonds and nonpolar hydrophobic interactions in a protein due to the elevated kinetic energy. In our simulations we do observe the accumulation of TFE molecules surrounding the protein. Figure 4a shows the number of TFE molecules within 3.4 Å of protein backbone α-carbons as a function of simulation time. As clearly illustrated in Figure 4a and the snapshots in Figure 4b,c, increasing the temperature enhances the fluctuations of molecular movement and facilitates the accumulation of TFE into the protein interior. This is consistent with the experimental finding that the TFE had a much stronger effect on the denaturation of bovine β-lactoglobulin45 at high temperature. Overall, results in Figure 3 and Figure 4 demonstrate that the TFE is effective, particularly at high temperature, in destroying the tertiary structures of the soy protein. The disruption of the tertiary structures expands the hydrophobic cores in the original native protein, and allows the hydrophobic residues which are originally hidden inside the
Figure 4. Interaction between TFE and soy protein. (a) The time evolution of the number of TFE (NTFE) accumulating inside the soy protein at 300 K (orange) and 363 K (red). (b, c) Snapshots under two different conditions. The TFE molecules within 3.4 Å of protein backbone α-carbons (first solvation shell) are shown using CPK representations.
hydrophobic cores to be more accessible to other components for interaction. Next we concentrate on the interaction between soy protein and GNP. The exposure of the hydrophobic residues provides attraction forces to the graphitic surfaces. Among the hydrophobic residues, the aromatic residues adsorb strongly onto a graphitic surface because of the π−π stacking interactions.49 It has been reported that the π−π stacking interactions played central roles in the interaction between proteins and graphitic nanomaterials. For example, the atomic force microscopy (AFM) measurements and optical absorption spectroscopy indicated that increasing the number of aromatic residues in a peptide increases the dispersion of single-walled CNTs.16,17 Li et al.50 showed that the poly tryptophan exhibited greater adhesion on nanotubes compared with poly lysine. Salzmann et al.51 measured the dispersion effect of 26763
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Figure 5. Interaction between soy protein and graphite surface. Time evolution of (a) the number of aromatic residues in contact with graphite surface (NAC) in water at 300 K (cyan) and in TFE solution at 363 K (red), (b) the van der Waals energy (Evdw) between aromatic side chains and graphite atoms, and (c) the x-coordinate of the center of mass of the protein relative to graphite center. (d) Snapshots under two different conditions. The aromatic residues contacting with graphite are shown in purple.
Figure 6. Effect of the temperature when GNPs are mixed. (a) The time evolution of the number of hydrophobic contacts. (b) Distribution of the radius of GNP agglomerations. Red color shows the case when protein is denatured in TFE−water at 363 K and GNPs are mixed at the same temperature. Green color shows the case when protein is denatured in TFE−water at 363 K, after which the temperature is reduced to 300 K at which the GNPs are mixed.
protein at the center of the domain and then allow it to interact with the graphite surface. Because of the importance of the π−π stacking interactions, we focus on the interaction between the aromatic groups (phenylalanine, tyrosine and histidine) and the graphite surface. Figure 5a shows the number of aromatic groups interacting (contacting) with the graphite surface. Here we define the aromatic group as actively interacting with the graphite surface when the aromatic group (the number 1 carbon on phenylalanine and tyrosine, the number 4 carbon on histidine) is within 4 Å distance from the graphite surface. As shown, the number of aromatic groups interacting with the graphite surface is significantly increased from ∼3 to ∼6 when
polypeptides comprising both pure and mixed sequences of lysine/tryptophan and lysine/tyrosine, and reported the best performance for the tryptophan-containing molecules. More recently, MD simulations were also performed to elucidate the influences of aromatic residues on interactions between proteins and graphitic surfaces.25,26 To directly explore the interactions between the soy protein and the GNP, a graphite consisting of three layers of 7.2 × 7.2 nm2 graphene sheet is inserted in the simulation domain. The carbon atoms in graphite are fixed during our simulations.52 We study two cases with different protein forms: water at 300 K (compact form) and TFE at 363 K (fully denatured form). We initially place the 26764
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reduction of contact probability between SPI and GNP when mixing at low temperature.
the protein is fully denatured by TFE at high temperature (also illustrated by the snapshots in Figure 5d). This increase of aromatic groups directly leads to stronger adsorption forces as indicated by the van der Waals interaction energies as shown in Figure 5b. In addition, If we track the trajectory of the center of mass of the protein (Figure 5c), the protein movement is much more constrained in TFE at 363 K than in water at 300 K, indicating a much stronger adsorption. In aqueous solutions, graphitic materials tend to aggregate due to the strong direct van der Waals interactions at the surfaces. It is clear from our MD simulations that for soy protein in water at room temperature, most of the hydrophobic residues (including the aromatic groups) are hidden inside the hydrophobic cores. By adding the TFE as cosolvent and elevating the temperature, under the action of heat and TFE the soy protein tertiary structures will be mostly destroyed. As a consequence, the hydrophobic residues, including the aromatic residues, become accessible to other components in the solution. When the graphitic materials are added into the solution, the adsorption becomes stronger because of the π−π stacking interactions between the graphitic surface and aromatic groups. The strong adsorption of the soy proteins onto the graphitic surfaces will effectively modify the graphitic surface and reduce the interfacial interactions. As a result the aggregation of the graphitic materials will be reduced and the dispersion will be improved. Effect of Temperature when GNPs are Mixed. As demonstrated by our experimental and simulation results, TFE is able to denature the soy protein such that the denatured protein can act as effective dispersant on the GNP particles at the high temperature. However, in most situations the nanocomposites cannot be processed at a high temperature. For example, mixing the dispersed nanofillers with a polymer matrix at room temperature is the common method in reality and keeping nanofillers in a well-dispersed status at room temperature during the fabrication process of nanocomposite is the key. To investigate effect of temperature when we mix the nanofillers, we first perform simulations of the soy protein in TFE solution at 363 K for 200 ns, and then we reduce the temperature to 300 K and run for another 200 ns. Figure 6a shows the number of native hydrophobic contacts for these two cases. At 363 K, as shown above, the tertiary structures of the soy protein are disrupted. As we reduce the temperature down to 300 K, from Figure 6a the protein structure does not change much, remaining fully denatured. The NHC value is very close to the high temperature case but much smaller than the case in which the protein is denatured at 300 K. This is because the TFE molecules that penetrate into the protein at high temperature stay inside the protein strucutre, even when the temperature is reduced. This is consistent with our experimental measurements of GNP agglomeration distribution at two different GNP mixing temperatures in Figure 6b. Two cases are studied: (1) the SPI is denatured in TFE at 363 K and then GNP particles are added; (2) the SPI is denatured in TFE at 363 K and then cools down to room temperature, after which the GNP particles are added for mixing. As shown, in case 2 (green color), although the GNP particles are mixed at room temperature, the agglomeration size is very close to case 1 and much smaller than when SPI is denatured at room temperature. Also, there are no large agglomerations as indicated in the inset. This observation does confirm that increasing the temperature significantly assists the denaturation function of TFE. The slight shift of the GNP agglomeration to larger size is due to the
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CONCLUSIONS By adding the TFE as a cosolvent and increasing the mixing temperature of the SPI solution, the agglomeration size of the GNP is significantly reduced. According to the distribution of GNP agglomeration sizes, compared to the samples treated in pure water, the peak particle size of the SPI-modified GNPs in TFE solution at high temperature is reduced from 2 μm to 600 nm. In addition, the FESEM and TEM images clearly show that the individual GNP particles are much thinner in TFE solution at high temperature than the particles in water. The results suggest that the GNP dispersion can be significantly improved by using properly denatured SPI as a dispersant in TFE solution at high temperature. Through MD simulations, we investigate the underlying molecular mechanisms controlling this process. Simulations showed that the TFE was able to effectively denature the soy protein, especially at high temperature (363 K). From the time evolution of radius of gyration, number of native hydrophobic contacts and solventaccessible surface areas of hydrophobic residues we found that most of the tertiary structures of the protein were destroyed. As a consequence, the hydrophobic residues which were originally hidden inside the protein become accessible to other components. Particularly, the increased number of aromatic residues yields stronger attractive force to the graphitic surfaces. The adsorption of the soy proteins on the GNP surface modified the surface energy, reduced the interaction force among GNPs, leading to much smaller agglomeration sizes and therefore significantly improved the GNP dispersion. We further confirm our explanation by study of the effect of GNP mixing temperature. The MD results agree well with the corresponding experimental data. Soy protein as a dispersant for the treatment of the graphitic nanofillers, compared with chemicals and other biomolecules, has the inherent advantages of wide availability, low cost and environmental sustainability. Moreover, as shown in Figure 2, the soy protein is highly complex with multiple levels of structures and contains a wide variety of polar and nonpolar functional residues. The diversity in content and complexity in structure make the soy protein a powerful and flexible platform one can tune to improve the material properties and explore new material functionalities in composite fabrication. The work herein demonstrates an excellent example of how the aromatic residues contained in soy protein can be effectively exploited for improved dispersion of graphitic nanofillers. Moreover, in the fabrication of polymer composites containing graphitic nanofillers, in addition to effective nanofiller dispersion, sufficient filler−polymer interfacial interaction is also critically important. Many polar groups contained in soy protein actively interact with polymer molecules; therefore we expect that the nanofiller−polymer interaction will also be strengthened after treatment with denatured soy protein. In addition, the denatured soy protein may also be exploited to improve the electrical properties of nanocomposites with nanofillers. There are a variety of means to denature the soy protein other than TFE and heat; for instance, recently we have shown that with the addition of different type of salts, we were able to control the morphologies and mechanical properties of the SPI/ poly(ethylene oxide) (PEO) biocomposites.24 Although the mechanisms we proposed in this paper are based on the simulation and experiments of GNP dispersion, they should 26765
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(10) Herrera-Alonso, M.; Abdala, A. A.; McAllister, M. J.; Aksay, I. A.; Prud’homme, R. K. Intercalation and Stitching of Graphite Oxide with Diaminoalkanes. Langmuir 2007, 23, 10644−10649. (11) Si, Y.; Samulski, E. T. Synthesis of Water Soluble Graphene. Nano Lett. 2008, 8, 1679−1682. (12) Lomeda, J. R.; Doyle, C. D.; Kosynkin, D. V.; Hwang, W.-F.; Tour, J. M. Diazonium Functionalization of Surfactant-Wrapped Chemically Converted Graphene Sheets. J. Am. Chem. Soc. 2008, 130, 16201−16206. (13) Zheng, M.; Jagota, A.; Semke, E. D.; Diner, B. A.; McLean, R. S.; Lustig, S. R.; Richardson, R. E.; Tassi, N. G. DNA-Assisted Dispersion and Separation of Carbon Nanotubes. Nat. Mater. 2003, 2, 338−342. (14) Dieckmann, G. R.; Dalton, A. B.; Johnson, P. A.; Razal, J.; Chen, J.; Giordano, G. M.; Munoz, E.; Musselman, I. H.; Baughman, R. H.; Draper, R. K. Controlled Assembly of Carbon Nanotubes by Designed Amphiphilic Peptide Helices. J. Am. Chem. Soc. 2003, 125, 1770−1777. (15) Zorbas, V.; Ortiz-Acevedo, A.; Dalton, A. B.; Yoshida, M. M.; Dieckmann, G. R.; Draper, R. K.; Baughman, R. H.; Jose-Yacaman, M.; Musselman, I. H. Preparation and Characterization of Individual Peptide-Wrapped Single-Walled Carbon Nanotubes. J. Am. Chem. Soc. 2004, 126, 7222−7227. (16) Zorbas, V.; Smith, A. L.; Xie, H.; Ortiz-Acevedo, A.; Dalton, A. B.; Dieckmann, G. R.; Draper, R. K.; Baughman, R. H.; Musselman, I. H. Importance of Aromatic Content for Peptide/Single-Walled Carbon Nanotube Interactions. J. Am. Chem. Soc. 2005, 127, 12323−12328. (17) Poenitzsch, V. Z.; Winters, D. C.; Xie, H.; Dieckmann, G. R.; Dalton, A. B.; Musselman, I. H. Effect of Electron-Donating and Electron-Withdrawing Groups on Peptide/Single-Walled Carbon Nanotube Interactions. J. Am. Chem. Soc. 2007, 129, 14724−14732. (18) Bhattacharyya, S.; Sinturel, C.; Salvetat, J. P.; Saboungi, M. L. Protein-Functionalized Carbon Nanotube-Polymer Composites. Appl. Phys. Lett. 2005, 86, 113104. (19) Karajanagi, S. S.; Yang, H. C.; Asuri, P.; Sellitto, E.; Dordick, J. S.; Kane, R. S. Protein-Assisted Solubilization of Single-Walled Carbon Nanotubes. Langmuir 2006, 22, 1392−1395. (20) Nepal, D.; Geckeler, K. E. Proteins and Carbon Nanotubes: Close Encounter in Water. Small 2007, 3, 1259−1265. (21) Kim, J. T.; Netravali, A. N. Physical Properties of Biodegradable Films of Soy Protein Concentrate/Gelling Agent Blends. Macromol. Mater. Eng. 2012, 297, 176−183. (22) Ji, J.-y.; Lively, B.; Zhong, W.-H. Soy Protein-Assisted Dispersion of Carbon Nanotubes in a Polymer Matrix. Mater. Express 2012, 2, 76−82. (23) Ji, J.; Li, B.; Zhong, W.-H. Effects of Soy Protein on the Crystallization and Dielectric Properties of Peg/Peg Copolymers. Macromol. Chem. Phys. 2012, 213, 757−765. (24) Eyler, A.; Wang, Y.; Liu, T.; Li, B.; Zhong, W.-H. Ion-Induced Effective Control of Morphologies of Soy Protein Biocomposites. J. Mater. Sci. 2015, 50, 2691−2699. (25) Tomasio, S. M.; Walsh, T. R. Modeling the Binding Affinity of Peptides for Graphitic Surfaces. Influences of Aromatic Content and Interfacial Shape. J. Phys. Chem. C 2009, 113, 8778−8785. (26) Zuo, G.; Huang, Q.; Wei, G.; Zhou, R.; Fang, H. Plugging into Proteins: Poisoning Protein Function by a Hydrophobic Nanoparticle. ACS Nano 2010, 4, 7508−7514. (27) Zuo, G.; Zhou, X.; Huang, Q.; Fang, H.; Zhou, R. Adsorption of Villin Headpiece onto Graphene, Carbon Nanotube, and C60: Effect of Contacting Surface Curvatures on Binding Affinity. J. Phys. Chem. C 2011, 115, 23323−23328. (28) Luan, B.; Huynh, T.; Zhao, L.; Zhou, R. Potential Toxicity of Graphene to Cell Functions Via Disrupting Protein-Protein Interactions. ACS Nano 2015, 9, 663−669. (29) Lively, B.; Zhong, W.-H. An Efficient Quantified Stereological Macrodispersion Analysis Approach for Determining Microscale Influences on Nanocomposite Material Properties. Macromol. Mater. Eng. 2013, 298, 221−234. (30) Liu, T.; Li, B.; Lively, B.; Eyler, A.; Zhong, W.-H. Enhanced Wear Resistance of High-Density Polyethylene Composites Re-
also be applicable to other graphitic nanofillers, such as CNT and CNF. Finally, due to the computational limit on the system size and duration time of molecular simulations, our statements on GNP dispersion are based on the extrapolations of results from single protein/GNP simulations. The effects of protein− protein, GNP−GNP interactions on the GNP dispersion should be explored in future.
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ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jpcc.5b09126. Soy protein structural changes in TFE-water at 363 K (AVI)
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AUTHOR INFORMATION
Corresponding Authors
*(W.-H.Z.) Telephone: (509) 335 7658. Fax: (509) 335 4662. E-mail:
[email protected]. *(J.L.) Telephone: (509) 335 4968. Fax: (509) 335 4662. Email
[email protected]. Author Contributions ‡
These authors contributed equally.
Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS We thank Dr. Yu Wang for helpful discussions. This work was supported by US National Science Foundation under grant No. CBET-1250107 (JL) and USDA NIFA 2015-67021-22911 (KZ). We also acknowledge the important support from the Franceschi Microscopy and Imaging Center at Washington State University. Computational resources were provided in part by the Extreme Science and Engineering Discovery Environment (XSEDE) under grant No. MSS150014.
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REFERENCES
(1) Bachtold, A.; Hadley, P.; Nakanishi, T.; Dekker, C. Logic Circuits with Carbon Nanotube Transistors. Science 2001, 294, 1317−1320. (2) Shi Kam, N. W.; Jessop, T. C.; Wender, P. A.; Dai, H. J. Nanotube Molecular Transporters: Internalization of Carbon Nanotube-Protein Conjugates into Mammalian Cells. J. Am. Chem. Soc. 2004, 126, 6850−6851. (3) Dalton, A. B.; Collins, S.; Razal, J.; Munoz, E.; Ebron, V. H.; Kim, B. G.; Coleman, J. N.; Ferraris, J. P.; Baughman, R. H. Continuous Carbon Nanotube Composite Fibers: Properties, Potential Applications, and Problems. J. Mater. Chem. 2004, 14, 1−3. (4) Rege, K.; Raravikar, N. R.; Kim, D. Y.; Schadler, L. S.; Ajayan, P. M.; Dordick, J. S. Enzyme-Polymer-Single Walled Carbon Nanotube Composites as Biocatalytic Films. Nano Lett. 2003, 3, 829−832. (5) Pantarotto, D.; Briand, J. P.; Prato, M.; Bianco, A. Translocation of Bioactive Peptides across Cell Membranes by Carbon Nanotubes. Chem. Commun. 2004, 16−17. (6) Ajayan, P. M. Nanotubes from Carbon. Chem. Rev. 1999, 99, 1787−1799. (7) Yoon, C.-M.; Long, D.; Jang, S.-M.; Qiao, W.; Ling, L.; Miyawaki, J.; Rhee, C.-K.; Mochida, I.; Yoon, S.-H. Electrochemical Surface Oxidation of Carbon Nanofibers. Carbon 2011, 49, 96−105. (8) Liston, E. M. Plasma Treatment for Improved Bonding - a Review. J. Adhes. 1989, 30, 199−218. (9) Chiu, H. T.; Lin, J. S. Electrochemical Deposition of Polypyrrole on Carbon-Fibers for Improved Adhesion to the Epoxy-Resin Matrix. J. Mater. Sci. 1992, 27, 319−327. 26766
DOI: 10.1021/acs.jpcc.5b09126 J. Phys. Chem. C 2015, 119, 26760−26767
Article
The Journal of Physical Chemistry C inforced by Organosilane-Graphitic Nanoplatelets. Wear 2014, 309, 43−51. (31) Hess, B.; Kutzner, C.; van der Spoel, D.; Lindahl, E. Gromacs 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. J. Chem. Theory Comput. 2008, 4, 435−447. (32) Oostenbrink, C.; Villa, A.; Mark, A. E.; Van Gunsteren, W. F. A Biomolecular Force Field Based on the Free Enthalpy of Hydration and Solvation: The Gromos Force-Field Parameter Sets 53a5 and 53a6. J. Comput. Chem. 2004, 25, 1656−1676. (33) MacKerell, A. D.; Bashford, D.; Bellott, M.; Dunbrack, R. L.; Evanseck, J. D.; Field, M. J.; Fischer, S.; Gao, J.; Guo, H.; Ha, S.; et al. All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins. J. Phys. Chem. B 1998, 102, 3586−3616. (34) Chitra, R.; Smith, P. E. A Comparison of the Properties of 2,2,2Trifluoroethanol and 2,2,2-Trifluoroethanol/Water Mixtures Using Different Force Fields. J. Chem. Phys. 2001, 115, 5521−5530. (35) Guex, N.; Peitsch, M. C. Swiss-Model and the Swiss-Pdbviewer: An Environment for Comparative Protein Modeling. Electrophoresis 1997, 18, 2714−2723. (36) Parrinello, M.; Rahman, A. Polymorphic Transitions in SingleCrystals - a New Molecular-Dynamics Method. J. Appl. Phys. 1981, 52, 7182−7190. (37) Saio, K.; Kamiya, M.; Watanabe, T. Food Processing Characteristics of Soy Bean-D 11s and 7s Proteins I Effect of Difference of Protein Components among Soy Bean-D Varieties on Formation of Tofu-Gel. Agric. Biol. Chem. 1969, 33, 1301−1308. (38) Nishinari, K.; Fang, Y.; Guo, S.; Phillips, G. O. Soy Proteins: A Review on Composition, Aggregation and Emulsification. Food Hydrocolloids 2014, 39, 301−318. (39) Maruyama, Y.; Maruyama, N.; Mikami, B.; Utsumi, S. Structure of the Core Region of the Soybean Beta-Conglycinin Alpha ’ Subunit. Acta Crystallogr., Sect. D: Biol. Crystallogr. 2004, 60, 289−297. (40) Adachi, M.; Kanamori, J.; Masuda, T.; Yagasaki, K.; Kitamura, K.; Mikami, B.; Utsumi, S. Crystal Structure of Soybean 11s Globulin: Glycinin A3b4 Homohexamer. Proc. Natl. Acad. Sci. U. S. A. 2003, 100, 7395−7400. (41) Hong, D. P.; Hoshino, M.; Kuboi, R.; Goto, Y. Clustering of Fluorine-Substituted Alcohols as a Factor Responsible for Their Marked Effects on Proteins and Peptides. J. Am. Chem. Soc. 1999, 121, 8427−8433. (42) Eisenhaber, F.; Lijnzaad, P.; Argos, P.; Sander, C.; Scharf, M. The Double Cubic Lattice Method: Efficient Approaches to Numerical Integration of Surface Area and Volume and to Dot Surface Contouring of Molecular Assemblies. J. Comput. Chem. 1995, 16, 273−284. (43) Luo, P. Z.; Baldwin, R. L. Mechanism of Helix Induction by Trifluoroethanol: A Framework for Extrapolating the Helix-Forming Properties of Peptides from Trifluoroethanol/Water Mixtures Back to Water. Biochemistry 1997, 36, 8413−8421. (44) Gast, K.; Zirwer, D.; Muller-Frohne, M.; Damaschun, G. Trifluoroethanol-Induced Conformational Transitions of Proteins: Insights Gained from the Differences between Alpha-Lactalbumin and Ribonuclease A. Protein Sci. 1999, 8, 625−634. (45) Kumar, S.; Modig, K.; Halle, B. Trifluoroethanol-Induced Beta > Alpha Transition in Beta-Lactoglobulin: Hydration and Cosolvent Binding Studied by H-2, O-17, and F-19 Magnetic Relaxation Dispersion. Biochemistry 2003, 42, 13708−13716. (46) Roccatano, D.; Colombo, G.; Fioroni, M.; Mark, A. E. Mechanism by Which 2,2,2-Trifluoroethanol/Water Mixtures Stabilize Secondary-Structure Formation in Peptides: A Molecular Dynamics Study. Proc. Natl. Acad. Sci. U. S. A. 2002, 99, 12179−12184. (47) Shao, Q.; Fan, Y.; Yang, L.; Qin Gao, Y. From Protein Denaturant to Protectant: Comparative Molecular Dynamics Study of Alcohol/Protein Interactions. J. Chem. Phys. 2012, 136, 115101. (48) Shao, Q. The Addition of 2,2,2-Trifluoroethanol Prevents the Aggregation of Guanidinium around Protein and Impairs Its Denaturation Ability: A Molecular Dynamics Simulation Study. Proteins: Struct., Funct., Genet. 2014, 82, 944−953.
(49) McGaughey, G. B.; Gagne, M.; Rappe, A. K. Pi-Stacking Interactions - Alive and Well in Proteins. J. Biol. Chem. 1998, 273, 15458−15463. (50) Li, X. J.; Chen, W.; Zhan, Q. W.; Dai, L. M.; Sowards, L.; Pender, M.; Naik, R. R. Direct Measurements of Interactions between Polypeptides and Carbon Nanotubes. J. Phys. Chem. B 2006, 110, 12621−12625. (51) Salzmann, C. G.; Ward, M. A. H.; Jacobs, R. M. J.; Tobias, G.; Green, M. L. H. Interaction of Tyrosine-, Tryptophan-, and LysineContaining Polypeptides with Single-Wall Carbon Nanotubes and Its Relevance for the Rational Design of Dispersing Agents. J. Phys. Chem. C 2007, 111, 18520−18524. (52) Raffaini, G.; Ganazzoli, F. Protein Adsorption on a Hydrophobic Surface: A Molecular Dynamics Study of Lysozyme on Graphite. Langmuir 2010, 26, 5679−5689.
26767
DOI: 10.1021/acs.jpcc.5b09126 J. Phys. Chem. C 2015, 119, 26760−26767
