J Mater Sci COMPOSITES Composites

Graphene dispersion in a surfactant-free, polar solvent Vahid Shabafrooz1 1

, Sudheer Bandla1

, and Jay C. Hanan1,*

School of Mechanical and Aerospace Engineering, Helmerich Advanced Technology Research Center, Oklahoma State University, Tulsa, OK 74106, USA

Received: 17 April 2017

ABSTRACT

Accepted: 4 August 2017

Here, we demonstrate dispersion of graphene nanoplatelets (GNPs) in ethylene glycol (EG), a polar dispersion medium, by a liquid-phase exfoliation method involving sonication and centrifugation. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Raman spectroscopy are used for qualitative and quantitative characterization of the graphene dispersions. TEM micrographs confirm the exfoliation and production of monolayer GNPs after sonication. Statistical analysis of TEM micrographs shows that increasing the sonication time increases the degree of exfoliation of GNPs. Raman spectroscopy studies also show that high-power probe sonication exfoliates multilayer GNPs to few-layer GNPs. The proposed method is promising to provide monolayer and few-layer graphene dispersed in a polar medium, practical to multiple engineering applications including polymer nanocomposites.

Ó

Springer Science+Business

Media, LLC 2017

Introduction Development of polymer nanocomposites in the 1990s [1] opened up an interesting research area in the field of materials science. The use of two-dimensional (2D) nanomaterials in the preparation of polymer nanocomposites has attracted great interest due to their unique properties and potential applications in automotive, aerospace, and electronic industries [2, 3]. Graphene, a planar, electrically conductive, elastic, and crystalline allotrope of carbon, is a 2D nanomaterial that can be described as a one-atom-thick layer, arranged in a hexagonal pattern [4, 5]. Graphene-based materials can consist of single-layer graphene sheets, few-layer graphene sheets, and multi-layer graphene sheets [6]. The Address correspondence to E-mail: [email protected]

DOI 10.1007/s10853-017-1456-0

elastic properties and breaking strength of graphene sheets have been measured and reported as 100 times stronger than steel, with a Young’s modulus of 1 TPa and tensile strength of 130 GPa [7–9]. In addition, graphene outperforms carbon nanotubes (CNTs), which have a measured Young’s modulus and tensile strength in the range of 0.2–0.9 TPa and 11–63 GPa, respectively [10–13]. The unique mechanical [14], electrical [15], and thermal properties [16] of graphene make it a promising and practical substance for several applications such as the development of lightweight and strong composite materials. Recent progress has shown that exfoliation and dispersion of graphene in a liquid medium through liquid exfoliation method is a promising approach for achieving graphene monolayers, which could have a significant impact on the creation of new materials such as

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nanocomposites [17–19]. Creating a uniform dispersion of graphene relies on using a dispersion medium whose surface energy is comparable to that of graphene. Surface energy can be correlated with enthalpy of mixing using the Hildebrand solubility parameter, d, from the following equation: 2 DHmix 2   dgraphene  dmedium / and di T Vmix qflake ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð1Þ ¼ Eisurfeace energy ; where DHmix is enthalpy of mixing per unit volume (mJ.m-3), Vmix is the volume of mixture, Tflake is the thickness of graphene flake, dgraphene is surface energy of graphene (mJ.m-2), dmedium is surface energy of liquid the medium (mJ.m-2), / is volume fraction of graphene. According to Hildebrand theory [20], materials with similar surface energies are likely to interact well and be miscible. Exfoliation and dispersion of graphene happen when the energetic penalty between graphene and the medium is minimized. As shown in Eq. (1), a good exfoliation of graphene in a dispersion medium suggests the enthalpy of mixing to be close to zero. This suggests that liquid media with surface energies matching that of graphene would lead to more exfoliated dispersions than others. There are several groups of liquid media that can be used to disperse graphene [21, 22]. Several studies have been published on using sonication and centrifugation methods to disperse graphene in a wide range of dispersion media including non-polar solvents (i.e., n-methyl-2-pyrrolidone—NMP) [23–26]. While these solvents allow a simple exfoliation and dispersion, many of these solvents have issues of high cost and toxicity [22]. The second group consists of polar dispersion media such as dimethylformamide (DMF), isopropyl alcohol (IPA), and water [27, 28]. Due to the hydrophobicity of graphene, aqueous dispersions are usually combined with stabilizing agents, (i.e., surfactants) [29, 30]. DMF possesses a lower polarity compared to water and IPA, making it a good candidate to produce a more stable graphene dispersion without the use of surfactants [28]. The stabilizing power of the polar dispersion media can be evaluated based on their polarity values [31, 32] mentioned as follows: H2O (1.0) [ EG (0.79) [ DMF (0.38)

A successful exfoliation and dispersion of graphene can also happen in water or organic solutions with the help of polymers. In these solutions, the solvent can be dried off resulting in a polymer–graphene nanocomposite. Polyvinyl alcohol (PVA) [33] and polyvinylpyrrolidone (PVP) [34] have been used in a solution to fabricate the graphene nanocomposites. Non-polar solvents cannot serve as a precursor for in situ polymerization to make composites [35] or to make conductive thin films through spray coating and vacuum filtration [36, 37]. For these applications, a polar solvent with a surfactant is generally required. However, surfactants can interfere with certain chemically dependent applications such as the formation of polymer nanocomposites [27]. The interaction between the surfactant molecules and the polymer matrix depends on the chemistry of both components [38]. The presence of some surfactant molecules in a polymeric matrix detrimentally alters the properties of the polymer or the nanoreinforcement interface. Removing surfactant molecules before the incorporation into the polymer adds complexity to the process and increases the production cost of nanocomposites [39, 40]. Ethylene glycol (EG) is a polar compound due to the presence of a hydroxide group in its molecular structure (CH2OHCH2OH) [41–43]. Zhang et al. [44] have established that EG can be used as a stabilizing agent to aid exfoliation of graphene in the production of graphene-based nanocomposites. Here, we demonstrate graphene dispersions in EG without the use of a surfactant to prepare the dispersions. The current research explores graphene dispersion in EG, aiming to gain insight into how the process parameters affect the size and number of the layers of the dispersed GNPs in the dispersion medium.

Experimental Materials GNPs (xGnP-M-5 grade with surface area = 150 m2.g-1) from XG Sciences were added to EG (Reagent Plus grade, Aldrich) at an initial concentration of 0.25 mg.mL-1 (200-mL glass beaker) to prepare GNP dispersions.

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Preparation of GNP dispersions in EG Sonication was performed in 30 min increments between 30 and 90 min with a probe sonicator (Qsonica-Q700) with a 19 mm probe at 50% amplitude. The beaker was placed in a temperature-controlled (maintained between 50 and 60 °C) water bath for the duration of sonication. The power delivered to the convertor with the probe in air, not immersed in the sample, was first recorded. Without changing the amplitude, the probe was immersed into the sample and the amount of delivered power was recorded. The difference between power readings was divided by the area of the probe to calculate the power that is being delivered to the sample (power density) using the following equation: P¼

P2  P1 ; pr2

ð2Þ

where P is the power density (W.cm-2), P1 is the power when probe is in air (W), P2 is the power when probe is immersed (W), r is the radius of probe tip (cm). With the P1 value to be 5 W and P2 value of 60 W, the power density was estimated to be 19 W.cm-2. The total input energy released during sonication for each time point can be calculated by: E ¼ P  t;

A ¼ aCL;

ð5Þ

where A is absorbance of liquid, a is absorption coefficient (mL.mg-1.m-1), L is cell path length (m), C is concentration of material (mg.mL-1). To measure the absorption coefficient of the graphene dispersion, a series of dispersion samples were prepared using varied processing parameters, including sonication times and centrifugal forces. With a subsequent decanting, 10 mL of the supernatant of each sample was taken as a feed solution and was filtered through a porous (0.2 lm) alumina filter membrane using a vacuum-assisted system. By weighing the filter membrane’s weight before and after filtration at a temperature above dispersant’s flash point, the concentration of actual dispersed GNPs in each sample was calculated. Measured concentration was then correlated with the ratio of the absorbance values per path length (A/L, where the path length L = 0.01 m) of the corresponding sample using the Beer–Lambert law, to calculate the absorption coefficient.

Characterization techniques

ð3Þ -2

where P is power density (W.cm ), E is the energy density released to the sample during sonication (J.cm-2), t is time (s). After sonication, dispersion samples were transferred into centrifuge tubes. Centrifuge tubes were filled with 30 mL of the sonicated dispersion pipetted from the middle of the beaker to avoid sediment. Then, centrifugation was performed for 45 min (Hermle Labnet, Z206A). During the centrifugal acceleration, sedimentation of GNPs happens in response to the forces acting on them. The force exerted on GNPs in centrifuge, relative centrifugal force (RCF) or G-Force, is a function of the rotational speed and is calculated using the following equation: G - Force ¼ 1:12RðRPM=1000Þ2 ;

concentration of GNPs in the supernatant was measured based on optical absorbance values using the Beer–Lambert law [23, 28, 45]:

ð4Þ

where R is the radius of rotational radius in centrifuge (cm), RPM is rotational speeds measured in revolutions per minute. After centrifugation at 260 and 2350 RCF, the top 10 mL of the supernatant with dispersed GNPs was collected for dispersion analysis (Fig. 1). The

Scanning electron microscopy (SEM) was carried out using a Hitachi S-4800 to capture micrographs of GNPs on a copper tape at 3 kV. To characterize the GNP dispersions, transmission electron microscopy (TEM) was performed using a JEOL JEM-2100 system at 200 kV and k = 0.0025 nm to capture bright-field (BF) micrographs of grids prepared from all dispersion samples. A few drops from each dispersion sample were placed on holey carbon grids, and then, each grid was dried at 100 °C using a vacuum dryer (Yamato Scientific America-ADP21). The boiling point of EG at atmospheric pressure is 197 °C. Through vacuum drying, the solvent was evaporated at a lower temperature and a large number of GNPs remained attached to the grid, to verify their presence and size distribution. Selected area electron diffraction (SAED) was carried out to study the crystal structure of graphene [46]. Raman spectroscopy was carried out using a Renishaw RM 1000 Raman microscope with 514 nm excitation laser at room temperature. Raman spectra collected were averaged from five different measurements across the sample. A 209 objective lens with a

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Figure 1 Schematic representation achieving graphene dispersion in a solvent with the aid of sonication and centrifugation.

working distance of 12 mm and a 50% density filter were used for measurements. The laser power of 1 mW was used to avoid heating effects. Samples were prepared by pipetting a small quantity of the dispersion samples to glass slides, which were then heated to 200 °C for 30 min.

Analysis of Raman spectra Raman spectroscopy is a widely used characterization technique in the study of graphene. A typical Raman spectrum of graphene consists of three major bands including the D band, G band, and 2D band near 1355, 1570, and 2700 cm-1, respectively [47, 48]. Raman spectroscopy is by far the most straightforward method to study the level of defects and identify the number of layers in graphene materials [49–52]. The D band is a weak feature of the Raman spectrum. It is due to a one-phonon scattering from defects [53]. The G band is due to the degeneration of the optical phonon mode in graphene. According to Yoon et al., the intensity (I) of the G band is sensitive and increases with the number of layers up to 7 layers. The shape of this band does not vary much [49]. Childres et al. [54] showed that the ratio of the intensity of the D band to the G band, ID/IG in Raman spectra, can be used to characterize the level of defects in graphene-related materials. According to Khan et al. [55] and Paton et al. [56], if defects present in graphene are edge defects or pre-existing defects in the material, then the ID/IG of the as-received powder and the samples after the sonication and centrifugation is approximately related to the lateral size of graphene by:

ID =IG  ID =IGPowder þ

k ; hlateral sizei

ð6Þ

where k is estimated to be 0.17. The 2D band, on the other hand, is a strong band that is due to a two-phonon scattering. This band is also referred to as G0 [53]. The lineshape of the 2D band reflects the electronic band structure and the number of layers of graphene.

Results Morphology of the GNPs SEM micrographs of the GNPs are shown in Fig. 2. As shown below, they are agglomerated particles in platelet shape, each consisting of several graphene layers stacked together.

Effects of process parameters on the size of the dispersed GNPs Monolayer graphene can be identified from the bright-field TEM micrographs because of their welldefined edges, while the multi-layer is typical of the larger objects regularly observed in the original presonication samples [57, 58]. Figure 3 shows micrographs of a TEM grid and SAED analysis of platelets prepared by drop casting a few droplets of the samples that were sonicated for 30 min and centrifuged at 2350 RCF. To analyze the nanoplatelets exfoliation, binary filters were applied to aid in distinguishing few-layer GNPs. As shown in Fig. 3b, the platelet, indicated by arrows, shows the presence of three

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layers. The SAED analysis indicates the presence of a monolayer as a basal plane with two smaller layers attached from edges (Fig. 3c). According to O’Neill et al. [28], Khan et al. [23], and Hernandez et al. [24], increasing the sonication time better separates the starting material into individual layers followed by a decrease in the size of the dispersed graphene. To investigate this, the length and width of GNPs were collected from the TEM micrographs of all samples. As shown in Fig. 3, GNPs are irregular in shape. To measure their length and width, the longest axis was considered to be the length and the shortest axis as the width. A total of 85 TEM micrographs were analyzed in a similar fashion (Fig. 4).

Raman spectroscopy results

Figure 2 SEM micrographs of as-received powder a 91500 and b 96000.

Figure 3 a TEM micrograph of a grid coated with few-layer GNPs dispersed in EG. b A binary version of the same TEM micrograph with heightened contrast to show the position of the

Figure 5 presents the evolution of the D, G, and 2D Raman bands collected from the as-received and the dispersed GNPs in EG, after sonication and centrifugation. Not represented on this graph are the dispersion samples that were sonicated for 30 min and centrifuged, as they could not be used for measurements by Raman spectroscopy. It was observed that the concentration of GNPs after centrifugation increases with sonication time. Samples that are sonicated for longer times were darker in color after

edges and identify the few-layer sheet with a binary filter. c SAED is taken from the highlighted region in a.

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Figure 4 Average length and width measurement of GNPs in dispersions centrifuged at a 260 RCF and b 2350 RCF. Error bars show standard deviation.

experiment for as-received and dispersed GNPs are reported in Table 1, as well. No significant difference was observed in this ratio after increasing the sonication time from 60 to 90 min at 260 RCF. However, increasing the centrifugation speed from 260 to 2350 RCF caused an increase in the level of defects.

Discussion Size distribution of the dispersed GNPs

Figure 5 Raman spectra illustrating the D, G, and 2D bands for the as-received and the dispersed GNPs.

centrifugation resulting in a higher quantity of the remainder GNPs in the supernatant. The ratio ID/IG for different dispersion samples and the as-received powder is reported in Table 1 using the method mentioned in ‘‘Analysis of Raman spectra’’ section. Knowing the average lateral size of the GNPs and the ratio ID/IG of the as-received powder, we calculated the ID/IG of the centrifuged graphene by Eq. 6. The values obtained from this Table 1 Ratio ID/IG represents the level of defects in dispersed GNPs

Statistical analysis of TEM micrographs showed that the platelets’ dimensions decreased with increasing the sonication time (Fig. 4). We see that by increasing the sonication time from 30 to 90 min, the average length of the GNPs for samples centrifuged at 260 RCF decreases from 1.5 to 1.2 lm, whereas the average width decreases from 0.9 to 0.8 lm, resulting in 20 and 11% decrease, respectively. Interestingly, for samples centrifuged at 2350 RCF the average length and width of the platelets remained almost same at 0.9 and 0.6 lm, respectively. This is likely because of the separation of smaller nanoplatelets into the supernatant at higher G-Force, irrespective of the sonication time. It was observed, from the TEM

Sample label

Sonication time (mins)

G-Force (RCF)

ID/IG experimental

ID/IG theory

As-received GNPs Dispersed GNPs

n/a 60 90 60 90

n/a 1500 1500 4500 4500

0.24 0.27 0.27 0.47 0.37

– 0.37 0.38 0.40 0.41

The theoretical values of the ID/IG are obtained using the equation proposed by Paton et al. [56]

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Figure 6 a–f Histograms showing the area of GNPs measured for supernatants with varied sonication time a and d 30, b and e 60, and c and f 90 min and varied G-Force at a–c 260 RCF and d–f at 2350 RCF.

micrographs, that increasing the sonication time will break up the GNPs’ layers and separate them from each other. This could be attributed to exfoliation of the GNPs due to the released energy during sonication [24]. Additionally, degree of exfoliation can be generated by plotting a histogram of the area of the GNPs. Figure 6 shows distributions of the area of the GNPs from TEM micrographs of the different dispersion samples. They show that increasing the

sonication time from 30 to 90 min at constant G-Force decreases the GNPs’ average area from 1.56 to 1.10 lm2 (Fig. 6a–c). Using the absorption coefficient of graphene dispersions, 2374 mL.mg-1.m-1 (see Table S1 and S2 in supporting information), the concentrations of the dispersions were evaluated with respect to the sonication time and G-Force. Table 2 summarizes the ratio of the concentration of the dispersed to the

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Table 2 Final concentration of the dispersed GNPs as a function of sonication time and G-Force Sonication time (mins)

30 60 90 120 180

Centrifugation at 260 RCF

Centrifugation at 2350 RCF

Final concentration (mg.mL-1)

Percent of initial concentration

Final concentration (mg.mL-1)

Percent of initial concentration

0.047 0.087 0.070 0.080 0.075

19 35 28 32 30

0.008 0.019 0.019 0.015 0.015

3 8 8 6 6

Table 3 Energy density and energy required to exfoliate a bilayer GNP Sonication time (mins)

30 60 90

Energy density (kJ/cm2)

Energy density (eV/cm2)

Centrifugation at 260 RCF Average area (cm2)

Exfoliation energy (eV)

Average area (cm2)

Exfoliation energy (eV)

34 68 103

2.1E?23 4.2E?23 6.4E?23

1.56E-8 1.10E-8 1.10E-8

3.28E?15 4.62E?15 7.04E?15

6.5E-9 6.4E-9 6.4E-9

1.37E?15 2.69E?15 4.10E?15

initial concentration of GNPs (0.25 mg.mL-1) at 30, 60, 90, 120, and 180 min time points. As shown, a dispersion of GNPs with a relatively higher concentration can be achieved at higher sonication times beyond 30 min at both 260 and 2350 RCF. Even though the concentration of the dispersed GNPs significantly drops from 260 to 2350 RCF, increasing the G-Force better separates the larger area GNPs from smaller ones. As a result, the dispersed GNPs have a smaller area (Fig. 6d–f). The apparent plateau in concentration after 30 min of sonication may reflect a saturation level of GNPs in EG. It is important to note how other measurements indicate that an increase in sonication time decreases the average platelet size. It is possible that these platelets are fracturing from the sonication energy. This also indicates that sonicating for longer exposure time causes a higher number of these smaller platelets per unit volume than the shorter exposure. To estimate the energy required to exfoliate the GNPs during sonication process, the energy density was calculated for different process conditions (i.e., sonication time and G-Force). Considering the energy with which graphene sheets are bound as *891011 eV/cm2 [59] and using the average area of the dispersed GNPs, reported in Fig. 6, the energy required to exfoliate a bilayer GNP was calculated for the corresponding sonication time and G-Force and

Centrifugation at 2350 RCF

reported in Table 3. As shown, the exfoliation energy at each process condition is higher than what it is required to break a bilayer graphene sheet, confirming that exfoliation of GNPs during sonication process.

Qualitative analysis of GNP exfoliation TEM micrographs of the as-received and the dispersed GNPs were taken and analyzed using binary filters1 (Fig. 7). Using binary filters, the number of layers in GNPs can be revealed as explained in ‘‘Effects of process parameters on the dispersed GNPs’’ section. For the as-received GNPs, applying the binary filter made the micrograph white, indicating the presence of many graphene layers that block the transmitted electrons. Once dispersed, the platelets appear dark with the binary filter, similar to the holes in the TEM grid. This indicates the presence of one to few layers, allowing visibility of single edges with the binary filter. As shown in Fig. 7, an isolated monolayer graphene was observed in all the dispersion samples. Increasing the G-Force better separates the larger GNPs that are not exfoliated through sonication, from the as-received powder, with a potential of increasing the frequency of monolayer graphene. However, the exfoliation of the GNPs from the 1

ImageJ: http://imagej.nih.gov.

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Figure 7 TEM micrographs of the as-received and the dispersed GNPs. Insets represent the region shown with binary filter to distinguish the edges of the monolayer graphene. Numeric labels indicate ‘‘sonication time–G-Force’’.

starting powder can be achieved through sonication and is independent of the centrifugation. TEM confirmed the presence of monolayer GNPs after sonication for 60 and 90 min of sonication and

centrifugation at 260 and 2350 RCF. To further investigate the effect of process parameters on the number of layers of the dispersed GNPs, Raman spectra were collected from the corresponding

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Figure 8 Illustration of the peak analysis of 2D Raman bands (R2–0.98). Averaged intensity collected from five different positions of the as-received and dispersed GNPs. Numeric labels indicate ‘‘sonication time–G-Force’’.

samples, and the 2D bands of the spectra were analyzed by integrating the Gaussian peaks in the spectra. Figure 8 shows the evolution of the 2D Raman bands and their corresponding sub-components collected from the as-received and the dispersed GNPs. As shown in Fig. 8, no significant differences were observed in the integrated peaks between the dispersed GNPs, making it difficult to understand the effects of sonication time and G-Force on the number of layers. However, they are different compared to the integrated peak of the as-received GNPs. To

further understand the difference between the 2D bands, the full width at half maximum (FWHM) of the 2D bands was obtained through the analysis. Table 4 summarizes the FWHM data collected from the first and the second sub-components of each sample. While the FWHM of the second sub-component does not vary significantly, the FWHM of the first sub-component shows significant variance from 62 observed with the as-received GNPs, supporting the above observation that the lineshape of the 2D band

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Table 4 FWHM data obtained by fitting the 2D Raman bands collected from the as-received and the dispersed GNPs

Sample

Sonication time (mins)

G-Force (RCF)

FWHM at 2696 cm-1

FWHM at 2731 cm-1

As-received GNPs Dispersed GNPs

n/a 60 90 60 90

n/a 260 260 2350 2350

62 80 73 74 82

47 48 46 48 48

is dependent on the number of layers of GNPs. Prior work demonstrates that the number of layers of fewlayer GNPs can be revealed by analysis of the Raman spectra. Latil et al. [60] and Saito et al. [61] reported on electronic structure and determination of number of layers by using the ratio of the 2D and G bands, I2D/IG. Ferrari et al. investigated the evolution of monolayer and few-layer graphene by Raman spectroscopy and compared their results with graphite [53]. They reported that the 2D band possesses a unique lineshape for single-layer graphene. As the number of graphene layers increases, the electronic band structure changes and approaches that of graphite [53]. They add that the shape and the formation of sub-components under the 2D band can be used as a graphene ‘‘fingerprint’’ to estimate the number of graphene layers. We see that the Raman spectra of dispersed GNPs are very similar to the spectra published by Yoon et al. [49] and Ferrari et al. [53] for graphene with 5–10 layers. Graphene is one of the most promising nanofillers for the development of nanocomposites owing to its exceptional mechanical, thermal, and electrical properties. Exfoliation of graphene in a liquid medium through sonication and centrifugation can be useful for obtaining monolayer graphene that are considered the most desirable structure of graphene for many research areas. To create a high-quality graphene dispersion, graphene must be dispersed at a concentration useful to the appropriate application and remain stably dispersed for the duration of use. Graphene dispersions, in an appropriate medium, can be incorporated into polymer matrices to produce polymer nanocomposites. Performance enhancements of these nanocomposites can be highly influenced by the level of exfoliation of the GNPs before incorporation into the polymer matrix. Further investigation is required to understand the effects of process parameters (i.e., varied sonication

time and G-Force) on the size of the dispersed GNPs. Future study will focus on the stability of these dispersions as a function of concentration of the GNPs and solvent’s interaction with graphene.

Conclusion Liquid-phase exfoliation was effective to disperse GNPs. We demonstrated that EG, a polar solvent, is a potential candidate for achieving a surfactant-free dispersion of GNPs. The TEM micrographs showed that sonication breaks up the GNP layers and separates them from each other. Also, increasing the G-Force separates the larger, or unexfoliated, GNPs from the exfoliated GNPs. The data suggest that longer sonication times are associated with a reduction in the average GNP size, indicating that the GNPs are fractured by longer exposure to sonication. Our results here show GNP dispersions as promising candidates for incorporation into polymer matrices in the development of graphene-based nanocomposites.

Acknowledgements We acknowledge XG Sciences for providing xGnP-M5 grade nanoplatelets. We also acknowledge Dr. Kaan Kalkan, Professor at the Mechanical and Aerospace Engineering department at OSU, and Ms. Lisa Whitworth, an associate at OSU Microscopy Lab, for their continuous support and help with Raman spectroscopy and TEM imaging. This work is part of an industry-sponsored research program at Oklahoma State University.

Compliance with ethical standards Conflicts of interest The authors declare that they have no competing interests.

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Electronic supplementary material: The online version of this article (doi:10.1007/s10853-017-1456-0) contains supplementary material, which is available to authorized users.

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