pubs.acs.org/Langmuir © 2009 American Chemical Society
Measurement of Multicomponent Solubility Parameters for Graphene Facilitates Solvent Discovery Yenny Hernandez, Mustafa Lotya, David Rickard, Shane D. Bergin, and Jonathan N. Coleman* School of Physics and Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN), Trinity College Dublin, Dublin 2, Ireland
Downloaded via UNIV OF GOTHENBURG on August 1, 2018 at 05:09:03 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.
Received August 26, 2009. Revised Manuscript Received October 8, 2009 We have measured the dispersibility of graphene in 40 solvents, with 28 of them previously unreported. We have shown that good solvents for graphene are characterized by a Hildebrand solubility parameter of δT ∼ 23 MPa1/2 and Hansen solubility parameters of δD ∼ 18 MPa1/2, δP ∼ 9.3 MPa1/2, and δH ∼ 7.7 MPa1/2. The dispersibility is smaller for solvents with Hansen parameters further from these values. We have used transmission electron microscopy (TEM) analysis to show that the graphene is well exfoliated in all cases. Even in relatively poor solvents, >63% of observed flakes have 70% of the starting material remains dispersed after 250 h. However, toluene is less stable with 70% remaining dispersed after 10 h. In all cases, the sedimentation curves can be fit to single exponentials as expected for dispersions with one sedimenting component.30 From the fits, we can estimate that each dispersion has a stable (nonsedimenting) component consisting of 75%, 72%, and 60% of the graphene mass for the NMP, CPO, and toluene samples, respectively. Previously, we showed that effective solvents for graphene had surface tensions close to 40 mJ/m2.23 We developed a simple model showing that, for two-dimensional nanomaterials, the surface energy acts like a solubility parameter; good solvents are those for which solvent and nanomaterial have the same surface energy. Indeed, the data collected during this work fit well to this model. As shown in Figure 2A, the graphene dispersibility is maximized for solvents with surface tension close to 40 mJ/m2. However, while we believe that the surface energy is an important parameter for understanding the solvent-graphene interaction, we also recognize it is a blunt tool which can only describe the overall interaction. In most solvent-solute systems, we can divide the intermolecular interactions into at least three types. The simplest formulation divides them into dispersive (D), polar (P,) and hydrogen-bonding (H) components.31,32 The most successful solubility theories treat these interactions separately.32 Basic solubility theory states that, for molecular solutes, the property most closely associated with solubility is the cohesive energy density, EC,T/V, where EC,T is the total molar cohesive energy and V is the molar volume of the solvent.32 The most commonly used solubility parameter, the Hildebrand parameter, δT, is just the square root of EC,T/V. It can be shown that the cohesive energy density can be divided into D, P, and H components: EC, T EC, D EC, P EC, H ¼ þ þ ð1Þ V V V V The square root of each of these components is a Hansen solubility parameter δi (i = D, P, H). δT2 ¼ δD2 þ δP2 þ δH2
ð2Þ
(31) Barton, A. F. M. CRC Handbook of Solubility Parameters and other Cohesion Parameters; CRC Press Inc.: Boca Raton, FL, 1983. (32) Hansen, C. M. Hansen Solubility Parameters - A User’s Handbook; CRC Press: Boca Raton, FL, 2007.
DOI: 10.1021/la903188a
3209
Article
Hernandez et al.
Figure 2. Graphene dispersibility, CG, as a function of (A) solvent surface tension and (B) solvent Hildebrand parameter.
These solubility parameters are incredibly useful when it comes to solvent selection for a given solute. Successful solvents are governed by two criteria. First, the Hildebrand solubility parameters of solvent and solute must be very close to each other.33,34 This is usually sufficient to identify solvents for nonpolar solutes. However, for polar solutes, a further criterion is often required: good solvents are those where solvent and solute have similar values of all three Hansen solubility parameters.31,32 However, graphene is not a molecular solute but a nanomaterial. Such materials are distinctive in that the interaction with the solvent occurs at a well-defined surface. For this reason, the surface energy based solubility parameters we have described previously appear more appropriate.23,29 In fact, the surface energy can also be divided up into dispersive, polar, and Hbonding components.35-37 For nanomaterials, it would appear more natural to use these components to develop Hansen-like solubility parameters based on surface energy.38 However, the components are only known for a small number of solvents. In contrast, Hansen parameters have been published for 1200 solvents.39 This makes them much more useful even though surface energy may be a more intuitive parameter for nanostructured systems such as nanotubes and graphene. Thus, we analyze the dispersibility of graphene using Hildebrand and Hansen solubility parameters. We can test the first solvent criterion by plotting the measured graphene dispersibility, CG, as a function of δT as shown in Figure 2B. A well-defined peak in the data is observed with the best solvents having δT ∼ 23 MPa1/2. This is in good agreement with previous work on solvent-assisted dispersions of carbon nanotubes.38,40 However, some of the solvents with δT very close to 23 MPa1/2 display poor dispersibility. This means that the
(33) Hildebrand, J. H.; Prausnitz, J. M.; Scott, R. L. Regular and related solutions, 1st ed.; Van Nostrand Reinhold Company: New York, 1970; p 228. (34) Rubinstein, M.; Colby, R. H. Polymer Physics, 1st ed.; Oxford University Press: Oxford, 2003; p 440. (35) Beerbower, A. J. Colloid Interface Sci. 1971, 35, 126–132. (36) Brandrup, J.; Immergut, E. H.; Grulke, E. A.; Akihiro, A.; Bloch, D. R. Polymer Handbook, 4th ed.; 1999. (37) Koenhen, D. M.; Smolders, C. A. J. Appl. Polym. Sci. 1975, 19, 1163–1179. (38) Bergin, S. D.; Sun, Z. Y.; Rickard, D.; Streich, P. V.; Hamilton, J. P.; Coleman, J. N. ACS Nano 2009, 3(8), 2340–2350. (39) Hansen, C. M.; Abbott, S. HsPiP software, www.hansen-solubility.com. (40) Detriche, S.; Zorzini, G.; Colomer, J. F.; Fonseca, A.; Nagy, J. B. J. Nanosci. Nanotechnol. 2007, 8(11), 6082–6092.
3210 DOI: 10.1021/la903188a
Figure 3. Dispersibility for all solvents tested as a function of (A) dispersive Hansen solubility parameter, (B) polar Hansen solubility parameter, and (C) hydrogen-bonding Hansen solubility parameter.
second criterion (that solute and solvent must have similar Hansen parameters) must be fulfilled. To test this, we plot CG as a function of δD, δP, and δH, as shown in Figure 3. The Hansen solubility parameters (and molar volumes used later) of the solvents were taken from HSPiP software (www.hansen-solubility.com) and are given in Table S1 in the Supporting Information. This software has a database of values from literature32 but also provides algorithms to calculate unknown Hansen parameters.41,42 Figure 3A shows a welldefined dependence of CG on the dispersive Hansen parameter with a peak around 18 MPa1/2. Clearly, good solvents for graphene have 15 MPa1/2 < δD < 21 MPa1/2. These results for the dispersive Hansen parameter are in good agreement with recent work on single walled carbon nanotube dispersions.38 As shown in Figure 3B and C, when plotted versus polar and H-bonding Hansen parameters, CG also displays broad peaks centered at δP ∼ 12 MPa1/2 and δH ∼ 9 MPa1/2, respectively. The most surprising thing about this data is that good solvents have nonzero values of δP and δH at all. The nonpolar nature of graphene would suggest that the Hildebrand parameter or the dispersive Hansen parameter alone would determine dispersibility. For example, we note that the solubility of C60 is very well described by the Hildebrand parameter alone.43 X-ray photoelectron spectroscopy measurements have confirmed that no oxides or other polar groups are attached to the graphene either before or after dispersion in solvents such as NMP.23 Park et al. have shown that highly reduced graphene oxide can be dispersed in solvents defined by 10 < (δH+δH) < 30.44 These results agree well with our data for defect free graphene, suggesting a fundamental requirement for a well defined degree of polarity in successful (41) Stefanis, E.; Panayiotou, C. Int. J. Thermophys. 2008, 29(2), 568–585. (42) Isu, Y.; Nagashima, U.; Aoyama, T.; Hosoya, H. J. Chem. Inf. Comput. Sci. 1996, 36(2), 286–293. (43) Ruoff, R. S.; Tse, D. S.; Malhotra, R.; Lorents, D. C. J. Phys. Chem. 1993, 97, 3379–3383. (44) Park, S.; An, J.; Jung, I.; Piner, R. D.; An, S. J.; Li, X.; Velamakanni, A.; Ruoff, R. S. Nano Lett. 2009, 9, 1593–1597.
Langmuir 2010, 26(5), 3208–3213
Hernandez et al.
Article
graphene solvents. However, the exact reason why nonzero values of δP and δH are required to disperse graphene remains a mystery. While Figure 3 supports the validity of the use of Hansen parameters, much scatter is present. This is likely due to the fact that each panel in Figure 3 treats the Hansen parameters one by one. Thus, a solvent with only one matching solubility parameter will appear under the peak in one panel but with lower than expected CG. To address this, we need to test the suitability of all three Hansen parameters simultaneously. To do this, we first estimate the Hansen parameters of the graphene itself. The simplest way to do this is by solvent screening, that is, by associating the solubility parameters of the solute with those of the most successful solvents.31-33,36 Here, we estimate the graphene Hansen parameters from the weighted averages of the Hansen parameters of the solvents. We suggest using the quantitative measure of the solvent quality, here the dispersibility, as the weighting factor. The three Hansen parameters are then given by P P Æδi æ ¼ solvent
CG δi, sol CG
ð3Þ
solvent
where i = D, P, or H (or T). Here, CG is the graphene dispersibility in a given solvent and δi,sol is the ith Hansen parameter in a given solvent. The summation is taken over all solvents studied. The advantage of this approach is that solvents contribute to the final result in proportion to their quality. For the solvents studied in this work, we estimate the Hansen parameters for graphene to be ÆδDæ ≈ 18.0 MPa1/2, ÆδPæ ≈ 9.3 MPa1/2, and ÆδHæ ≈ 7.7 MPa1/2. These values agree reasonably well with the Hansen parameters for carbon nanotubes measured by us (ÆδDæ = 17.8 MPa1/2, ÆδPæ = 7.5 MPa1/2, and ÆδHæ = 7.6 MPa1/2)38 and others.40 To test the validity of this methodology, we need a parameter which represents the potential of a given solvent. The simplest parameter is the distance in Hansen parameter space from solvent to solute Hansen parameters. This distance is just the length, R, of the vector from the point in Hansen space representing the solute, A (δD,A,δP,A,δH,A), to the point representing the solvent, B (δD,B,δP,B,δH,B): R ¼
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðδD, A - δD, B Þ2 þ ðδP, A - δP, B Þ2 þ ðδH, A - δH, B Þ2 ð4Þ
However, rather than using R, we borrow an alternative metric from Flory-Huggins theory. The Flory-Huggins parameter,34 χ, is a measure of the cost of mixing solvent and solute and can be related to the Hansen solubility parameters by32 v0 2 v0 h R ¼ ðδD, A - δD, B Þ2 þ ðδP, A - δP, B Þ2 χ≈ kT kT i þ ðδH, A - δH, B Þ2 ð5Þ where v0 is the solvent molecular volume. Hansen has written this expression with a prefactor of 0.25 before the second and third terms on the right-hand side.32 Incorporation of this prefactor is extremely common, and its usage is supported empirically by significant quantities of data from many areas of solubility research.32 However, it is not supported by a rigorous theoretical foundation. Thus, we use eq 5 in its more intuitive form. However, we note that we have also carried out all the analysis described below using eq 5 but including the factor of 0.25. We find the same trends in both cases. Langmuir 2010, 26(5), 3208–3213
Figure 4. Dispersibility, CG, as a function of Flory-Huggins parameter, χ.
According to eq 5, the lower the value of χ, the lower the energetic cost of dispersing (and exfoliating) the graphene. However, this expression is approximate as it does not allow for negative values of χ. Such values are of course possible and occur when solvent-solute interactions are particularly strong. Equation 5 also confirms that dispersion and exfoliation is favored when both solute (graphene) and solvent have similar values of all three Hansen parameters (D, P, and H). In the simplest case, χ gets smaller as solvent and solute Hansen parameters converge. Thus, if the concept of Hansen parameters is appropriate to graphene dispersions and if our estimate of the graphene Hansen parameters is realistic, CG should decrease with increasing χ. We use eq 5 to calculate the Flory-Huggins parameter for each solvent-graphene dispersion. Figure 4 plots CG as a function of χ and clearly shows the expected behavior. We observe a clear improvement in graphene dispersibility as the estimated χ values tend toward zero. This graph confirms that our estimated Hansen parameters are close to the true values. We expect that identification of the exact values would reduce the scatter in Figure 4 further. In addition, it clearly shows that the dispersion of graphene is at least partly controlled by the energetic cost of exfoliation. These results mean that good solvents for graphene should have Hansen parameters close to δD = 18.0 MPa1/2, δP = 9.3 MPa1/2, and δH = 7.7 MPa1/2. In fact, during this work, we made a preliminary estimation of ÆδDæ, ÆδPæ, and ÆδHæ. We then tested a number of solvents with Hansen parameters in this region. This resulted in the discovery of our best two solvents, cyclopentanone (δD = 17.9 MPa1/2, δP = 11.9 MPa1/2, δH = 5.2 MPa1/2) and cyclohexanone (δD = 17.8 MPa1/2, δP = 8.4 MPa1/2, δH = 5.1 MPa1/2). We feel this strongly validates our results and our method. Of course, probing dispersibility through optical absorbance measurements does not confirm the presence of graphene in the dispersions. In order to prove exfoliation of graphene from the starting graphite, we conducted TEM analysis. Samples for TEM were prepared by pipetting a few milliliters of dispersion onto holey carbon grids (400 mesh). Bright field TEM images were taken with a Jeol 2100 instrument operating at 200 kV. This was done for solvents with the highest CG, a selection of intermediate CG values, and a low value of CG (the square bracketed numbers give the solvent rankings from Table S1 in the Supporting Information): cyclopentanone[1] (CPO, CG = 8.5 ( 1.1 μg/ mL), 1,3-dimethyl-2-imidazolidinone[5] (DMEU, CG = 5.2 ( 1.3 μg/mL), N-ethyl-2-pyrrolidone[13] (NEP, CG = 4.0 ( 0.7 μg/ mL), N-dodecyl-2-pyrrolidinone[28] (N12P, CG = 2.1 ( 1.1 μg/ mL), and acetone[34] (CG = 1.2 ( 0.4 μg/mL). These samples were compared to previously studied23 N-methyl-2-pyrrolidone[8] (NMP, CG = 4.7 ( 1.9 μg/mL). Figure 5 shows selected TEM images for monolayer and multilayered graphene flakes derived from these solvents. All the solvents produce some monolayer DOI: 10.1021/la903188a
3211
Article
Hernandez et al. Table 1. Mean Numbers of Graphene Layers per Flake, ÆNæ, Number Fraction of Monolayer Graphene, N1/NT, and Number Fraction of Few-Layer Graphene (1-5 layer), N1-5/NT, from TEM Analysis of Six Solvents
NMP DMEU cyclopentanone NEP N12P acetone
Figure 5. Selected monolayer and few-layer graphene flakes from various solvents: (A-C) monolayers and (D-F) multilayers.
Figure 6. Histograms for numbers of layers per flake from TEM analysis of six solvents.
graphene with no visible evidence of large scale defects. Many of the flakes observed under TEM are composed of few-layer graphene. In order to investigate the quality of the dispersions further, we conducted detailed statistical analysis of the TEM images. We counted the numbers of graphene layers per flake by examining the edges of the flakes. In TEM images of graphene multilayers, the edges of the individual flakes are almost always distinguishable. Thus, by carefully counting the flake edges, it is possible to measure the number of layers per flake.23 (We note that most researchers use atomic force microscopy to measure flake thickness. However, this is not possible here. Due to the high boiling point of most successful graphene solvents, flakes tend to aggregate during deposition onto surfaces, rendering quantitative AFM analysis impossible). A minimum of 64 flakes per sample were analyzed; the data are summarized in Figure 6. It is clear that all the solvents examined produce distributions of flake types that are weighted toward few-layer graphene (see Table 1). All the solvents produce a significant population of monolayer material. As shown in Table 1, the best solvent was NMP with a mean value of 2.5 layers per flake and the highest number fraction of monolayer graphene (29%). What is most interesting is that all the solvents, good and poor, have >63% of flakes with 1-5 layers. This suggests that even solvents with poor dispersibility, such as N12P and acetone, can produce dispersions of high flake quality. Finally, we note that the mechanism of graphene dispersion in these solvents is still not known. We expect the χ values estimated for the vast majority of solvents are simply too big to result in thermodynamic solubility of graphene sheets. The reason for this 3212 DOI: 10.1021/la903188a
ÆNæ
N1/NT (%)
N1-5/NT (%)
2.5 4.5 4.8 4.2 5.2 4.3
29 11 5 6 5 7
97 70 69 65 64 74
is that such large structures would be expected to have very low entropy of mixing.38 Thus, only systems with extremely small values of χ could be solutions (i.e., have negative free energy of mixing). Here, some of the solvents have enormous χ values, up to ∼10. To put this in context, in polymer systems, χ values >0.5 are considered large. In addition, the observed sedimentation suggests these systems are dispersions rather than solutions. However, if graphene is not soluble, it must be somehow stabilized against aggregation. The means for this stabilization remain unknown. However, the mechanism appears to be different from that of nanotubes in solvents. The best solvents for nanotubes tend to be amide solvents,29,38 suggesting a molecule dependent, specific interaction. In addition, a number of solvents with the correct Hansen parameters do not disperse nanotubes.38 Neither of these phenomena applies to graphene: Successful solvents are not limited to amides, and all solvents with Hansen parameters in the correct region tested by us have dispersed graphene to some degree. Another mystery is that fact that NMP is the eighth best solvent in terms of dispersibility. However, it is the best solvent in terms of exfoliation and stability. Future work is required to answer these outstanding questions.
Conclusion In conclusion, we have demonstrated the dispersion of graphene in 40 solvents of which 28 are new graphene solvents, attaining dispersibilities up to 8 μg/mL. We confirmed that successful solvents are characterized by surface tensions close to 40 mJ/m2 as described previously.23 However, we also showed that good solvents are characterized by a Hildebrand solubility parameter close to 23 MPa1/2. As both surface tension and Hildebrand parameter are related to the overall solvent-graphene interaction, we have also investigated the dispersive, polar, and hydrogen-bonding components of the interaction. We do this by showing that successful solvents have well-defined values of the Hansen solubility parameters. Surprisingly, nonzero values of the polar and H-bonding Hansen parameters are required for a solvent to disperse graphene. This is unexpected for such a nonpolar material. In addition, we have shown that the graphene is extensively exfoliated in solvents irrespective of the graphene dispersibility in that solvent. We feel this work is important for two reasons. First, we have demonstrated 28 new solvents for graphene. Many of these solvents have distinct advantages over previously known solvents such as NMP. For example, NMP is handicapped by its high boiling point of 205 C which makes it very difficult to remove solvent in applications such as deposition of flakes on surfaces. In contrast, chloroform[20] and isopropanol[19] have boiling points of 61 and 82 C, respectively. In addition, access to such a wide range of solvents means a broad choice of polymers are available for liquid phase composite formation. Second, knowledge of the Hansen parameters for any solvent allows the discovery of new Langmuir 2010, 26(5), 3208–3213
Hernandez et al.
solvents and facilitates the formulation of solvent blends. Thus, this work opens the field of liquid phase graphene exfoliation. We anticipate advances in areas such as film formation and composite processing will stem from these results. Acknowledgment. J.N.C. acknowledgments SFI funding under the PI award scheme, Contract Number
Langmuir 2010, 26(5), 3208–3213
Article
07/IN.1/I1772. M.L. acknowledges financial support from IRCSET. Supporting Information Available: Full list of solvents studied including Hansen parameters and dispersibility values. This material is available free of charge via the Internet at http://pubs.acs.org.
DOI: 10.1021/la903188a
3213
