Predicting the hygroscopicity of imidazolium-based ILs varying in anion by hydrogen-bonding basicity and acidity

Yuanyuan Caoa, Yu Chena, Xiaojing Wang*b and Tiancheng Mu*a
aDepartment of Chemistry, Renmin University of China, Beijing 100872, P. R. China. E-mail: tcmu@chem.ruc.edu.cn; Fax: +86-10-62516444; Tel: +86-10-62514925
bSchool of Science, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China. E-mail: xjwang@bucea.edu.cn; Fax: +86-10-61209501; Tel: +86-10-61209501

Received 19th August 2013 , Accepted 5th November 2013

First published on 6th November 2013


Abstract

Most of ILs are hygroscopic. Hygroscopicity of ILs has a significant influence on the structure and properties of ILs. There should be correlation between hygroscopicity and the other properties of ILs. In this study, eight imidazolium-based ILs varying in anion were used to investigate the relationship between their hygroscopicity parameters (experimental water sorption capacity within 3 h W3h, saturated water sorption capacity W derived from the modified two-step model, degree of difficulty to reach sorption equilibrium 1/k, and water sorption rate kW derived from the modified two step model) and their property parameters (electric field ΔE, polarity ENT, hydrogen-bonding acidity α, hydrogen-bonding basicity β, dipolarity-polarizability π*, and the difference between hydrogen-bonding basicity and acidity βα). We found that βα had a very strong positive correlation coefficient ρ ≈ 0.99 with the hygroscopicity parameters. However, the respective species, i.e., β and α are less correlated with hygroscopicity. The ENT and ΔE are much less correlated with the hygroscopicity of ILs than those of β − α, β and α, while π* has almost no correlation with hygroscopicity of ILs.


1. Introduction

Ionic liquids (ILs) are salts which are in the liquid state near room temperature.1 They have high thermal stability and negligible vapor pressure in normal operating conditions.2–5 They could be tuned by combinations of anion and cation or functionalization for specific task, thus various ILs with amazing physical and chemical properties could be obtained. Therefore, ILs are widely used for biomass dissolution,6,7 gas capture,8–11 organic solvent separation,12 microemulsion formation,13,14 material synthesis,15,16 and so on.

The presence of water has a significant influence on the structure and properties of ILs. After introducing the water molecule, the hydrogen bond between the anion and cation of ILs was weakened.17–19 Properties, such as viscosity, could be enormously reduced after mixing with water.20,21 Also, contamination of water might even make ILs (e.g., [BMIM][BF4], [BMIM][BF6]) hydrolyzed into poisonous products, such as HF.22 The presence of 1 wt% water could enhance the CO2 capacity from 1[thin space (1/6-em)]:[thin space (1/6-em)]2 to 1[thin space (1/6-em)]:[thin space (1/6-em)]1 mol. CO2/IL.23 The presence of about 1 wt% water also leads cellulose from being soluble to insoluble in ILs.24

Most of the ILs are hygroscopic, even the so called hydrophobic ILs (e.g., [BMIM][Tf2N]) could absorb some extent of water.25–28 Namely, ILs can not avoid contacting with water in normal conditions because humid air is ubiquitous. Unexpectedly, highly hygroscopic IL [BMIM][Ac] absorbed about 16% g g−1 water/IL within 3 h at temperature of 23 °C and relative humidity (RH) of 52%.27 Protic ILs are more hygroscopic. For example, in similar conditions (T = 28.9 °C, RH = 56.6%), diethyl-ammonium formate [DEA][Fo] could absorb about 25% g g−1 water/IL within 24 h.25

The above discussions indicate that the hygroscopicity of ILs is important. Therefore, it is preferable to develop a predictive method for hygroscopicity of ILs. Here, we develop a method to predict the hygroscopicity of ILs from the solvatochromic parameters (i.e., polarity ENT, hydrogen-bonding acidity α, hydrogen-bonding basicity β, and dipolarity-polarizability π*). The cue to choose solvatochromic parameters is their good correlations with biomass dissolution,6,29,30 interaction mechanism,28,31 acetylene solubility,32 CO2/CH4 and H2S/CH4 separation,33 and so on. This research indicated that hydrogen-bonding basicity β is linearly positive correlated with acetylene solubility, CO2/CH4 and H2S/CH4 selectivity and biomass dissolution. Moreover, Sixta et al.29 studied the role of solvent parameters in the regeneration of cellulose from ionic liquid solutions and found that the net basicity (βα) was the best parameter to explain the ability of mixtures to dissolve cellulose, rather than β alone. Except solvatochromic parameters, the effect of the electric field (ΔE) on hygroscopicity has also been investigated because the electric field has significant influence on the structure of ILs.34,35

It is well known that the linear solvation energy relationship (LSER) is one of the most commonly used approaches to quantitatively study solvent-dependent properties, and was introduced by Kamlet and Taft.36,37 This method correlates the solvent property and solvatochromic parameters α, β, and π* by multi-parameter equation (XYZ) = (XYZ0) + a × α + b × β + s × π*. This LSER approach has been successfully used to explain and predict the reaction rate and selectivity of reaction processes in which ionic liquids have served as solvents.38–42 According to the relationship between hygroscopicity parameters and property parameters of ionic liquids, this LSER correlation should be applied. However, herein we select the correlation between βα and the hygroscopicity parameters instead of the LSER correlation.

Eight imidazolium-based salts with the same cation [BMIM] were investigated (Table 1). On one hand, imidazolium-based ILs are widely studied and the data are available. On the other hand, the influence of the anion on the physical properties (e.g., hygroscopicity27) and potential industrial application (e.g., CO2 capture,43 biomass dissolution6) is more significant than that of cation type and alkyl chain length. Therefore, eight imidazolium-based ILs varying in anion were selected.

Table 1 Names and chemical structures of 8 imidazolium-based ILs varying in anion which were investigated
No. ILs Abbreviation Chemical structure
1 1-Butyl-3-methyl-imidazolium hexafluorophosphate [BMIM][PF6] image file: c3ra44464c-u1.tif
2 1-Butyl-3-methyl-imidazolium bis(trifluoromethylsulfonyl)imide [BMIM][Tf2N] image file: c3ra44464c-u2.tif
3 1-Butyl-3-methyl-imidazolium tetrafluoroborate [BMIM][BF4] image file: c3ra44464c-u3.tif
4 1-Butyl-3-methyl-imidazolium nitrate [BMIM][NO3] image file: c3ra44464c-u4.tif
5 1-Butyl-3-methyl-imidazolium acetate [BMIM][Ac] image file: c3ra44464c-u5.tif
6 1-Butyl-3-methyl-imidazolium trifluoromethanesulfonate [BMIM][TFO] image file: c3ra44464c-u6.tif
7 1-Butyl-3-methyl-imidazolium trifluoroacetate [BMIM][TFA] image file: c3ra44464c-u7.tif
8 1-Butyl-3-methyl-imidazolium chloride [BMIM][Cl] image file: c3ra44464c-u8.tif


2. Data processing

The names and chemical structures of the investigated ILs are listed in Table 1. The hygroscopicity parameters (Table 2) are experimental water sorption capacity within 3 h (W3h), saturated water sorption capacity derived from the modified two-step model (W), the degree of difficulty to reach sorption equilibrium (1/k), and water sorption rate (103kW).27 The six property parameters (Table 3) are polarity (ENT), hydrogen-bonding acidity (α), hydrogen-bonding basicity (β), dipolarity-polarizability (π*), the difference between hydrogen-bonding basicity and acidity (βα), and electric field (ΔE).34,44 The effect of property parameters on hygroscopicity parameters is shown in Fig. 1–4.
Table 2 Hygroscopicity of 8 selected imidazolium-based ILs varying in anion which were investigateda
No. ILs W3h % W % 1/k min−1 103kW % min−1
a All hygroscopicity data are conducted at T = 23 °C and RH = 52%, cited from our previous report.27
1 [BMIM][PF6] 0.43 0.45 75.8 5.9
2 [BMIM][Tf2N] 1.03 1.03 38.5 26.8
3 [BMIM][BF4] 2.98 3.32 80.0 41.5
4 [BMIM][NO3] 7.64 9.61 114.9 83.6
5 [BMIM][Ac] 15.63 24.24 172.4 140.6
6 [BMIM][TFO] 4.28 4.69 84.0 55.8
7 [BMIM][TFA] 9.05 9.88 85.5 115.6
8 [BMIM][Cl] 14.08 20.00 147.1 136.0


Table 3 Solvatochromic parameters and electric field of 8 selected imidazolium-based ILs varying in aniona
No. ILs PolarityENT Hydrogen-bonding acidity α Hydrogen-bonding basicity β βα Dipolarity-polarizability π* Electric field ΔE/MV cm−1
a The remaining data are cited from the ref. 34.b “–” indicates that the corresponding data is not available.c Supercooled liquid.
1 [BMIM][PF6] 0.667 0.630 0.196 −0.434 0.991 −4.8
2 [BMIM][Tf2N] 0.64 0.647 0.306 −0.341 0.903 −6.6
3 [BMIM][BF4] 0.669 0.643 0.407 −0.236 0.991 −4.2
4 [BMIM][NO3] 0.634 0.517 0.644 0.127 1.06 −2.2
5 [BMIM][Ac] 0.566 0.437 1.122 0.685 0.974 −1.7
6 [BMIM][TFO]44 0.667 (ref. 44 and 49) 0.630 (ref. 44 and 50) 0.46 (ref. 44 and 50) −0.17 1 (ref. 42 and 44) b
7 [BMIM][TFA]44,51 0.623 0.560 b b 0.56 b
8 [BMIM][Cl]c 0.614 0.470 0.87 0.4 1.1 0



image file: c3ra44464c-f1.tif
Fig. 1 Effect of the solvatochromic parameters and electric field on the water sorption capacity W3h. The value of R2 is used to represent the efficiency of a linear regression. (a) N/A means the value of R2 is negative, which is statistically meaningless. N/A indicates not available.

image file: c3ra44464c-f2.tif
Fig. 2 Effect of the solvatochromic parameters and electric field on the water sorption capacity W. The value of R2 is used to represent the efficiency of a linear regression. (a) N/A means the value of R2 is negative, which is statistically meaningless. N/A indicates not available.

image file: c3ra44464c-f3.tif
Fig. 3 Effect of the solvatochromic parameters and electric field on the equilibrium difficulty 1/k. The value of R2 is used to represent the efficiency of a linear regression. (a) N/A means the value of R2 is negative, which is statistically meaningless. N/A indicates not available.

image file: c3ra44464c-f4.tif
Fig. 4 Effect of the solvatochromic parameters and electric field on the water sorption rate 103kW. The value of R2 and R2 are used to represent the efficiency of a linear regression and an exponential regression, respectively. (a) N/A means the value of R2 is negative, which is statistically meaningless. N/A indicates not available.

Two parameters are used to analyze the correlations between hygroscopicity and property parameters. The first one is the adjusted R square R2 of the fitted linear equation. The maximum value of R2 is 1. Another parameter is the correlation coefficient ρ. The sign + and – of ρ means positive correlation and negative correlation, respectively. The closer of ρ to 1 or −1 means the better correlation. The tendency of R2 and ρ is same, so we mainly use R2 as the indicator of the linear relationship. If a change of direction is considered, ρ is also discussed. The overall presentation of R2 and ρ is presented in Fig. 5 and 6, respectively. All data processing was conducted using Origin 8.0.


image file: c3ra44464c-f5.tif
Fig. 5 Overall presentation of R2 for the fitted linear equation between the hygroscopicity parameters (W3h, W, 1/k, and 103kW) and other property parameters (ENT, α, β, βα, π*, and ΔE). W3h, W, 1/k, and 103kW represent the water sorption capacity within 3 h, steady-state water sorption capacity, the difficulty to reach water sorption equilibrium, sorption rate of ILs, respectively. ENT, α, β, βα, π*, and ΔE represent polarity, hydrogen-bonding acidity, hydrogen-bonding basicity, the difference between hydrogen-bonding basicity and acidity, dipolarity-polarizability, and electric field of ILs, respectively. Note that the correlation between hygroscopicity parameters and π* is statistically meaningless, because the value of R2 is negative as shown in the figure.

image file: c3ra44464c-f6.tif
Fig. 6 Overall presentation of the correlation coefficient ρ for the linear relationship between hygroscopicity parameters (W3h, W, 1/k, and 103kW) and other property parameters (ENT, α, β, βα, π*, and ΔE).

3. Results and discussion

3.1. Correlations between water the sorption capacity and property parameters of ILs

Water sorption capacity is directly related to hygroscopicity. It is one of the most studied parameters. Greater water sorption capacity is deemed as stronger affinity of ILs with water, hence higher hygroscopicity.45

The water amount (mass ratio as g g−1 H2O/IL) absorbed by ILs within 3 h is denoted as W3h.27 The correlations between W3h and solvatochromic parameters (i.e., polarity ENT, hydrogen-bonding acidity α, hydrogen-bonding basicity β, and dipolarity-polarizability π*) and electric field (ΔE) were conducted.

Results show that W3h has almost no linear correlation with π* (R2 is negative), a moderately linear correlation with ΔE (R2 = 0.7535), ENT (R2 = 0.8143), and α (R2 = 0.8839), a good linear correlation with β (R2 = 0.9605), while an almost perfect correlation with βα (R2 = 0.9875) (Fig. 1 and Table 4). The order of correlation coefficient ρ and R2 is same as: π* < ΔE < ENT < α < β < βα (Fig. 1 and Table 4). ENT and α have a negative correlation with W3h, while π*, β and βα have positive correlation. W3h = 6.40 + 14.38(βα), with R2 = 0.9875 and ρ = 0.9948.

Table 4 Fitted linear equation and correlation coefficient between hygroscopicity and solvatochromic parameters and between hygroscopicity and the electric field, for the 8 selected imidazolium-based ILs varying in aniona,b,c
  W3h W 1/k 103kW
a The columns and rows of the table are indicated by x and y, respectively.b ρ and R2 indicate the correlation coefficient between x and y and adjusted R square of the fitted linear equation of x and y, respectively. Contents in bold indicate the good correlation.c The table cell with and without bold text is positively and negatively correlated, respectively. The purpose for the bold text is to guide the eye.d N/A means the value of R2 is negative, which is statistically meaningless. N/A represents not available.
ENT ρ = −0.9170 ρ = −0.9380 ρ = −0.8065 ρ = −0.8765
y = 95.81 − 140.61x y = 148.30 − 220.04x y = 683.94 − 923.75x y = 832.13 − 1196.13x
R2 = 0.8143 R2 = 0.8598 R2 = 0.5921 R2 = 0.7297
α ρ = −0.9490 ρ = −0.9528 ρ = −0.9178 ρ = −0.8968
y = 38.31 − 56.26x y = 57.40 − 86.40x y = 326.71 − 406.41x y = 339.90 − 473.11x
R2 = 0.8839 R2 = 0.8924 R2 = 0.8160 R2 = 0.7716
β ρ = 0.9834 ρ = 0.9841 ρ = 0.9419 ρ = 0.9776
y = −3.95 + 18.45x y = −7.25 + 28.56x y = 26.29 + 132.33x y = −19.42 + 156.73x
R2 = 0.9605 R2 = 0.9622 R2 = 0.8645 R2 = 0.9469
βα ρ = 0.9948 ρ = 0.9935 ρ = 0.9539 ρ = 0.9869
y = 6.40 + 14.38x y = 8.77 + 22.21x y = 100.52 + 103.26x y = 68.50 + 121.90x
R2 = 0.9875 R2 = 0.9843 R2 = 0.8920 R2 = 0.9687
π* ρ = 0.02957 ρ = 0.12147 ρ = 0.32108 ρ = −0.1256
y = 5.92 + 1.02x y = 3.06 + 6.43x y = 21.20 + 83.02x y = 112.33 − 38.67x
R2 N/Ad R2 N/Ad R2 N/Ad R2 N/Ad
ΔE ρ = 0.8960 ρ = 0.8649 ρ = 0.9159 ρ = 0.8932
y = 15.04 + 2.49x y = 21.75 + 3.68x y = 166.55 + 19.01x y = 141.66 + 21.31x
R2 = 0.7535 R2 = 0.6852 R2 = 0.7986 R2 = 0.7472


The saturated water sorption capacity W is another parameter for characterizing water sorption capacity, which may be derived from the modified two-step model W = W(1 − ekt).25–27 The measurement of W is inconvenient owing to the very long time to reach equilibrium. Deriving W from the modified two-step model is relatively easy. The correlations between W and the parameters (i.e., ENT, α, β, π*, βα and ΔE) were also investigated.

The results show that the extent of the linear relationship between W and property parameters is ordered as: π* < ΔE < ENT < α < β < βα (Fig. 2 and Table 4). The correlation of β (R2 = 0.9627) and α (R2 = 0.8924) with W is less than that of βα (R2 = 0.9843). Specifically, the linear relationship between W and βα could be expressed as W = 8.77 + 22.21(βα). Instead, π* has no correlation with W; ΔE and ENT have a moderate correlation with W. It is the same as that with W3h. It suggests that designing ILs with greater water sorption capacity could be achieved by selecting IL with higher βα, and vice versa.

3.2. Correlations between the degree of difficulty to reach sorption equilibrium and the property parameters of ILs

Based on the previous studies,25–27 the degree of difficulty to reach water sorption equilibrium (1/k) at a fixed temperature and relative humidity can be derived from a modified two-step model. A higher value of 1/k means that it is harder to reach sorption equilibrium. That is to say, if the equilibrium is difficult to reach, the difficulty to reach some percentage (e.g., 90% (ref. 43)) of the equilibrium could also be used. Thus, the prediction of 1/k is of much importance.

Fig. 3 and Table 4 show that the correlations between parameters (ENT, α, β, π*, βα and ΔE) and 1/k are weaker than that for water sorption capacity. Although the most correlated parameter to 1/k is also βα, the absolute value of R2 is only 0.8919, which is much lower than that of W3h (R2 = 0.9875) and W (R2 = 0.9843). It is also corroborated by the value of correlation coefficient ρ, i.e., ρ1/k&βα (0.9539) < ρW∞&βα (0.9935) ≈ ρW3h&βα (0.9948) (Table 4).

This indicates that factors affecting 1/k might be very complicated except for solvatochromic parameters and electric field. For example, the size of ion, the charge on the ion surface and the free volume are also important.46,47 For example, [BMIM][Tf2N] (β = 0.306) shows a negative deviation in the correlation between 1/k and β, which might be caused by the relatively big size of the anion Tf2N. A bigger size of anion is not favorable for ILs for interacting with water, hence easy to reach equilibrium. Also, the positive deviation for [BMIM][PF6] (β = 0.196) in the correlation between 1/k and β might be caused by the relatively large free volume of the anion PF6. A larger free volume of ILs might be helpful to load more water, hence more difficult to reach equilibrium.

3.3. Correlations between water sorption rate and property parameters of ILs

The water sorption rate of ILs is related to how fast ILs would absorb water from humid air.25–27 It could be indicated by 103kW based on previous reports.25–27,45

Similarly, 103kW is correlated with the parameters (ENT, α, β, π*, βα and ΔE). Fig. 4 and Table 4 show that 103kW is negatively correlated to ENT, α, and π*, but positively correlated to ΔE, β, and βα. The linear fitted efficiency for βα (R2 = 0.9687) is better than that of β (R2 = 0.9469) and α (R2 = 7716). It suggests that both β and α contribute to 103kW. The value of 103kW could thus be directly predicted by βα using the equation 103kW = 68.50 + 121.90(β − -α) (Table 4). It means that designing ILs with a higher water sorption rate could also be obtained by tethering groups with a higher value of βα, and vice versa.

Both the relationship between 103kW and β, between 103kW and βα, could be well regressed by an exponential equation, such as y = y0(1 − ekx), where y represents 103kW, and x represent β or βα (Fig. 4). However, the value of R2 for the exponential relationship suggested above is much less than the linear relationship. Specifically, the linear relationship for β and βα is R2 = 0.9469, and R2 = 0.9687, respectively (Table 4). However, for the exponential relationship, the values of R2 decrease to 0.9047, and −1.4153, respectively. Thus, it could be concluded that the linear relationship is more favorable than the exponential relationship for 103kW.

3.4. Overall view on the correlation result

An overall presentation of R2 is displayed in Fig. 5. It shows that βα has the best correlation with the hygroscopicity parameters (W3h, W, 1/k, and 103kW) compared to other property parameters (ENT, α, β, π*, and ΔE) of ILs. Particularly, R2 for the linear relationship between βα and W3h reaches the highest value, i.e., 0.9875, while both β and α have less correlation with the hygroscopicity than βα.

The correlation coefficient ρ is also investigated for the above relationship. The results show that the tendency of R2 and ρ is identical, i.e., βα > β > α > ΔEENT ≫ π* (Fig. 5 and 6). But the difference of ρ between the above correlations is not as significant as that of R2 (Fig. 5 and 6). However, ρ could directly show a positive or negative correlation. It could be seen from Fig. 6 that ENT and α are negatively correlated to all the parameters of hygroscopicity. ΔE, β and βα are positively correlated to all the parameters of hygroscopicity, while π* is positively correlated to W3h, W, and 1/k, and negatively correlated to 103kW.

All the hygroscopicity parameters are more strongly related to βα (positive correlation) than β (positive correlation) and α (negative correlation) (Fig. 5 and 6). Namely, a higher value of βα contributes to higher hygroscopicity. In addition, β is more strongly related to hygroscopicity than α (Fig. 5 and 6). This indicates that water mainly interacts with the anion rather than the cation, because β and α are mainly determined by the anion and cation in terms of the imidazolium-based ILs, respectively.48 Our previous report also suggested that the anion played a more important role in water sorption.27

4. Conclusion

The correlations between hygroscopicity with solvatochromic parameters (ENT, α, β, π*, and βα) and electric field (ΔE) of ILs were investigated. The parameters of hygroscopicity include water sorption capacity within 3 h (W3h), saturated water sorption capacity (W), the degree of difficulty to reach water sorption equilibrium (1/k), and water sorption rate (103kW).

The most important finding is that βα is strongly positively correlated to all parameters of hygroscopicity, β, α, ENT, and ΔE are moderately correlated to the hygroscopicity, and π* is nearly not correlated to these parameters. These findings are very helpful for predicting hygroscopicity of ILs. They also us a hint that for designing hydrophilic or hydrophobic ILs, βα of the ILs should be paid enough attention. It would also be helpful to synthesize new ILs when water is present intended for a homogenous or heterogeneous chemical reaction, phase separation, drying agent, water proof materials, and so on. Note that only eight imidazolium-based ILs varying in anion were investigated because of the greatest popularity and most abundant data for imidazolium salts. More work still needs to be done for other kinds of ILs.

Acknowledgements

This work was supported by the Plan Project of Science and Technology of Beijing Municipal Education Committee (KM201210016007) and the National Natural Science Foundation of China (21173267).

References

  1. J. S. Wilkes, Green Chem., 2002, 4, 73–80 RSC.
  2. Y. Chen, Y. Cao, Y. Shi, Z. Xue and T. Mu, Ind. Eng. Chem. Res., 2012, 51, 7418–7427 CrossRef CAS.
  3. T. Mu and B. Han, in Structure and Bonding, Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 2014, vol. 151, pp. 107–140 Search PubMed.
  4. M. J. Earle, J. Esperanca, M. A. Gilea, J. N. C. Lopes, L. P. N. Rebelo, J. W. Magee, K. R. Seddon and J. A. Widegren, Nature, 2006, 439, 831–834 CrossRef CAS PubMed.
  5. C. Maton, N. De Vos and C. V. Stevens, Chem. Soc. Rev., 2013, 42, 5963–5977 RSC.
  6. Q. Chen, A. Xu, Z. Li, J. Wang and S. Zhang, Green Chem., 2011, 13, 3446–3452 RSC.
  7. W. N. Liu, Y. C. Hou, W. Z. Wu, S. H. Ren, Y. Jing and B. G. Zhang, Ind. Eng. Chem. Res., 2011, 50, 6952–6956 CrossRef CAS.
  8. X. Zhang, H. Dong, Z. Zhao, S. Zhang and Y. Huang, Energy Environ. Sci., 2012, 5, 6668–6681 CAS.
  9. Y. Zhang, S. Zhang, X. Lu, Q. Zhou, W. Fan and X. P. Zhang, Chem.–Eur. J., 2009, 15, 3003–3011 CrossRef CAS PubMed.
  10. J. Sun, J. Wang, W. Cheng, J. Zhang, X. Li, S. Zhang and Y. She, Green Chem., 2012, 14, 654–660 RSC.
  11. S. Ren, Y. Hou, S. Tian, X. Chen and W. Wu, J. Phys. Chem. B, 2013, 117, 2482–2486 CrossRef CAS PubMed.
  12. D. Xiong, H. Wang, Z. Li and J. Wang, ChemSusChem, 2012, 5, 2255–2261 CrossRef CAS PubMed.
  13. J. Li, J. Zhang, Y. Zhao, B. Han and G. Yang, Chem. Commun., 2012, 48, 994–996 RSC.
  14. J. Li, J. Zhang, B. Han, L. Peng and G. Yang, Chem. Commun., 2012, 10562–10564 RSC.
  15. K. Ding, Z. Miao, B. Hu, G. An, Z. Sun, B. Han and Z. Liu, Langmuir, 2010, 26, 10294–10302 CrossRef CAS PubMed.
  16. K. Ding, Z. Miao, B. Hu, G. An, Z. Sun, B. Han and Z. Liu, Langmuir, 2009, 26, 5129–5134 CrossRef PubMed.
  17. Q. Zhang, N. Wang, S. Wang and Z. Yu, J. Phys. Chem. B, 2011, 115, 11127–11136 CrossRef CAS PubMed.
  18. Q. Zhang, N. Wang and Z. Yu, J. Phys. Chem. B, 2010, 114, 4747–4754 CrossRef CAS PubMed.
  19. L. Zhang, Z. Xu, Y. Wang and H. Li, J. Phys. Chem. B, 2008, 112, 6411–6419 CrossRef CAS PubMed.
  20. Z. Wang, L. Fu, H. Xu, Y. Shang, L. Zhang and J. Zhang, J. Chem. Eng. Data, 2012, 57, 1057–1063 CrossRef CAS.
  21. W. Li, Z. Zhang, B. Han, S. Hu, Y. Xie and G. Yang, J. Phys. Chem. B, 2007, 111, 6452–6456 CrossRef CAS PubMed.
  22. M. G. Freire, C. M. S. S. Neves, I. M. Marrucho, J. A. P. Coutinho and A. M. Fernandes, J. Phys. Chem. A, 2010, 114, 3744–3749 CrossRef CAS PubMed.
  23. S. J. Zhang, J. M. Zhang, K. Dong, Y. Q. Zhang, Y. Q. Shen and X. M. Lv, Chem.–Eur. J., 2006, 12, 4021–4026 CrossRef PubMed.
  24. R. P. Swatloski, S. K. Spear, J. D. Holbrey and R. D. Rogers, J. Am. Chem. Soc., 2002, 124, 4974–4975 CrossRef CAS PubMed.
  25. Y. Chen, Y. Cao, X. Lu, C. Zhao, C. Yan and T. Mu, New J. Chem., 2013, 37, 1959–1967 RSC.
  26. Y. Cao, Y. Chen, L. Lu, Z. Xue and T. Mu, Ind. Eng. Chem. Res., 2013, 52, 2073–2083 CrossRef CAS.
  27. Y. Cao, Y. Chen, X. Sun, Z. Zhang and T. Mu, Phys. Chem. Chem. Phys., 2012, 14, 12252–12262 RSC.
  28. Y. Cao, X. Sun, Y. Chen and T. Mu, ACS Sustainable Chem. Eng., 2014 DOI:10.1021/sc4003246.
  29. L. K. Hauru, M. Hummel, A. W. King, I. Kilpeläinen and H. Sixta, Biomacromolecules, 2012, 13, 2896–2905 CrossRef CAS PubMed.
  30. T. V. Doherty, M. Mora-Pale, S. E. Foley, R. J. Linhardt and J. S. Dordick, Green Chem., 2010, 12, 1967–1975 RSC.
  31. Y. Wu, T. Sasaki, K. Kazushi, T. Seo and K. Sakurai, J. Phys. Chem. B, 2008, 112, 7530–7536 CrossRef CAS PubMed.
  32. J. Palgunadi, S. Y. Hong, J. K. Lee, H. Lee, S. D. Lee, M. Cheong and H. S. Kim, J. Phys. Chem. B, 2011, 115, 1067–1074 CrossRef CAS PubMed.
  33. P. J. Carvalho and J. A. Coutinho, Energy Environ. Sci., 2011, 4, 4614–4619 CAS.
  34. S. Zhang, Y. Zhang, X. Ma, L. Lu, Y. He and Y. Deng, J. Phys. Chem. B, 2013, 117, 2764–2772 CrossRef CAS PubMed.
  35. S. G. Zhang, R. Shi, X. Y. Ma, L. J. Lu, Y. D. He, X. H. Zhang, Y. T. Wang and Y. Q. Deng, Chem.–Eur. J., 2012, 18, 11904–11908 CrossRef CAS PubMed.
  36. M. J. Kamlet, J. L. M. Abboud, M. H. Abraham and R. Taft, J. Org. Chem., 1983, 48, 2877–2887 CrossRef CAS.
  37. M. J. Kamlet, R. M. Doherty, M. H. Abraham, Y. Marcus and R. W. Taft, J. Phys. Chem., 1988, 92, 5244–5255 CrossRef CAS.
  38. L. Crowhurst, N. L. Lancaster, J. M. Pérez Arlandis and T. Welton, J. Am. Chem. Soc., 2004, 126, 11549–11555 CrossRef CAS PubMed.
  39. R. Bini, C. Chiappe, V. L. Mestre, C. S. Pomelli and T. Welton, Org. Biomol. Chem., 2008, 6, 2522–2529 CAS.
  40. G. Ranieri, J. P. Hallett and T. Welton, Ind. Eng. Chem. Res., 2008, 47, 638–644 CrossRef CAS.
  41. T. P. Wells, J. P. Hallett, C. K. Williams and T. Welton, J. Org. Chem., 2008, 73, 5585–5588 CrossRef CAS PubMed.
  42. M. Ab Rani, A. Brant, L. Crowhurst, A. Dolan, M. Lui, N. Hassan, J. Hallett, P. Hunt, H. Niedermeyer and J. Perez-Arlandis, Phys. Chem. Chem. Phys., 2011, 13, 16831–16840 RSC.
  43. Y. Chen, J. Han, T. Wang and T. Mu, Energy Fuels, 2011, 25, 5810–5815 CrossRef CAS.
  44. P. G. Jessop, D. A. Jessop, D. Fu and L. Phan, Green Chem., 2012, 14, 1245–1259 RSC.
  45. F. Di Francesco, N. Calisi, M. Creatini, B. Melai, P. Salvo and C. Chiappe, Green Chem., 2011, 13, 1712–1717 RSC.
  46. M. Klähn, C. Stüber, A. Seduraman and P. Wu, J. Phys. Chem. B, 2010, 114, 2856–2868 CrossRef PubMed.
  47. L. E. Ficke and J. F. Brennecke, J. Phys. Chem. B, 2010, 114, 10496–10501 CrossRef CAS PubMed.
  48. S. K. Shukla, N. D. Khupse and A. Kumar, Phys. Chem. Chem. Phys., 2012, 14, 2754–2761 RSC.
  49. M. J. Muldoon, C. M. Gordon and I. R. Dunkin, J. Chem. Soc., Perkin Trans. 2, 2001, 433–435 RSC.
  50. T. Welton, L. Crowhurst, P. R. Mawdsley, J. M. Perez-Arlandis and P. A. Salter, Phys. Chem. Chem. Phys., 2003, 5, 2790–2794 RSC.
  51. H. Tokuda, K. Hayamizu, K. Ishii, M. A. B. H. Susan and M. Watanabe, J. Phys. Chem. B, 2004, 108, 16593–16600 CrossRef CAS.

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