Accepted Manuscript Title: pider diagram: a universal and versatile approach for system comparison and classification. Application to solvent properties Author: E. Lesellier PII: DOI: Reference:

S0021-9673(15)00242-3 http://dx.doi.org/doi:10.1016/j.chroma.2015.02.017 CHROMA 356275

To appear in:

Journal of Chromatography A

Received date: Revised date: Accepted date:

1-12-2014 4-2-2015 5-2-2015

Please cite this article as: E. Lesellier, Sigmapider diagram: a universal and versatile approach for system comparison and classification. Application to solvent properties, Journal of Chromatography A (2015), http://dx.doi.org/10.1016/j.chroma.2015.02.017 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

1

Σpider diagram : a universal and versatile approach for system comparison

2

and classification. Application to solvent properties

3

E. Lesellier

5

Université d’Orléans, Institut de Chimie Organique et Analytique (ICOA), CNRS UMR 7311,

6

B.P. 6759, rue de Chartres, 45067 Orléans cedex 2, France.

7

[email protected]

ip t

4

8

10

ABSTRACT

cr

9

Classification methods based on physico-chemical properties are very useful in analytical chemistry, both for extraction and separation processes. Depending on the number

12

of parameters, several classification approaches can be used: by plotting two- or three-

13

dimensional maps (triangles, cubes, spheres); by calculating comparison values for one

14

system with reference to another one, i.e. the ranking factor F, or the Neue selectivity

15

difference s2; or with chemometric methods, (principal component analysis - PCA or

16

hierarchical cluster analysis - HCA). All these methods display advantages and drawbacks:

17

some of them are limited by the number of studied parameters (e.g. three for triangle or

18

sphere plots); others require a new calculation when changing the reference point (F; s2),

19

while for chemometric methods (PCA, HCA), the relationships between the clusters and the

20

physico-chemical properties are not always easily understandable.

an

M

ed

pt

21

us

11

From previous studies performed in supercritical fluid chromatography for stationary phase classification on the basis of linear solvation energy relationships (LSER) including

23

five parameters, we developed a classification map called the Σpider diagram. This diagram

24

allows plotting in a two-dimensional map the location of varied systems, having as many

25

parameters as the ones required to get a satisfactory classification. It can be three, five, eight,

26

or any number.

27

Ac ce

22

In the present paper, we apply this diagram, and the calculation mode to obtain this

28

diagram, to different solvent classifications: Snyder triangle, solvatochomic solvent

29

selectivity, Hansen parameters, and also to LSER Abraham descriptors and COSMO-RS

30

parameters. The new figure based on Snyder data does not change the global view of groups,

31

except by the use of corrected data from literature, and allows to add the polarity value onto

32

the map. For the solvatochromic solvent selectivity, it leads to achieve a better view of

33

solvents having no acidic character.

Page 1 of 52

34

For Hansen parameters, the “flattening” of the spherical view down to a single plane

35

could be found easier to use. For COSMO-RS and with Abraham descriptor, a more subtle

36

classification is achieved, mainly due to the use of five parameters instead of three. A strong

37

reversed correlation is established between the Rohrschneider polarity P’ and the normalized

38

V (molecular volume) parameter.

39

The study of the location of solvents used for reversed-phase liquid chromatography and the Arizona system for counter-current chromatography is discussed, as well as the

41

replacement of unsafe solvents by greener ones, or the use of these classifications for the

42

study of compound solubility.

cr

44

Besides, this paper also shows the ability to the spider diagram to plot on a single plane three axes from principal component analyses.

us

43

ip t

40

45

1. Introduction

an

46 47 48

Solvents are extensively used in the field of analytical chemistry, for separation or extraction. They are mainly chosen on the basis of the interactions they are able to develop

50

with analytes, which can be related to analyte solubility, or on solvent miscibility or non-

51

miscibility, in the case of counter-current chromatography (CCC) or liquid-liquid extraction.

52

Of course, it is well known that reversed-phase liquid chromatography (RPLC) mainly uses

53

hydro-organic mixtures with methanol or acetonitrile, normal-phase liquid chromatography

54

(NPLC) and thin-layer chromatography on silica plates both use mixtures of organic solvents,

55

while hydrophilic interaction chromatography (HILIC) essentially relies on acetonitrile-water

56

mixtures. What is more, beyond the necessary solubility, selectivity of the solvent, that is to

57

say the ability to separate classes of compounds, is rather based on subtle differences in

58

interactions between the liquid phase and the compounds. Besides, the change in the

59

stationary phase properties due to mobile phase adsorption should not be neglected in

60

chromatographic processes.

61

Ac ce

pt

ed

M

49

Varied classification systems were developed in the past to rank the solvents based on

62

their estimated interaction capabilities. Two types of scales were essentially used:

63

solvatochromic, based solely on the solvent properties, and eluotropic, measuring the solvent

64

properties in the presence of a stationary phase. For the first type, one can cite the Reichardt’s

65

scale (ET(30), Nile red), the solvent selectivity triangle (Snyder) (SST), the solvatochromic

Page 2 of 52

66

solvent selectivity (SSS, Kamlet and Taft), Hildebrand and Hansen solubility parameters

67

(HSP), Abraham solvation descriptors (LSER) and the COSMO-RS approach.

68

Some of these scales are based on spectroscopic measurements (SSS, ET(30)), whereas

69

others are based on energy measurements (SST, Hildebrand, Hansen, Abraham descriptors),

70

or by using theoretical descriptors on the base of the -potential (Cosmo-RS). Beyond the data, their use for a classification depends on the number of parameters

72

extracted. For two parameters, a plane is sufficient, and its understanding easy. For three data,

73

3D spaces (Hansen sphere) are required, or triangle with normalized data (Teas or Snyder

74

diagrams). For more descriptors, no direct representation is generally used but hierarchical

75

cluster analysis (HCA), or principal component analysis (PCA) are often used to reduce data

76

dimensionality. Both are obtained with a mathematical treatment applied to the parameters,

77

and plotted on one or several 2D graphs by combination of the principal components PCi

78

describing the higher variance percentage (PC1/PC2; PC1/PC3). However, this approach does

79

not evidence a simple relation between the classification and relevant descriptors.

an

us

cr

ip t

71

80

From the Rohrschneider’s gas-liquid partition data [1], Snyder defined the overall

82

measure of solvent strength by the P’ polarity scale, which varies from 0.1 for hexane to 10.2

83

for water [2,3]. The measurement of this gas-liquid partition coefficient is based on the ability

84

of liquids to dissolve six selected probes. These partition coefficients were measured by

85

analyzing the gas phase of a sealed flask maintained at 25°C during several hours, and

86

containing 2 ml of the solvent and 5 µl of the selected probes. They represent the ratio of the

87

concentration of the probes in the dilution solvent and of the concentration of the probes in

88

the gaseous phase above the liquid. Data were thus provided for 81 liquids.

ed

pt

Ac ce

89

M

81

Based on a reduced data set, by using three (ethanol, p-dioxane and nitromethane) of the

90

six initial reference probes (plus n-octane, toluene, 2-butanone), Snyder suggested three

91

parameters, xe, xd and xn respectively describing hydrogen bond basicity (ethanol), acidity (p-

92

dioxane) and the solvent dipolarity (nitromethane). To get these parameters, the values of the

93

gas/liquid partition coefficients (K) are adjusted with regard to the solvent molar volume

94

K’=KVs

95

(1)

96 97

then corrected by subtracting the distribution constant of an alkane (Kv) of the same molar

98

volume

Page 3 of 52

K”= K’/Kv

99

(2)

100 101

Consequently, this scale is a polarity scale, which does not include the capability for dispersive interactions. Other works had earlier shown that dispersive interactions represented

103

between 52 % (acetonitrile) and 70 % (acetone) of the total interactions (cohesion) in pure

104

solvents [4], possibly explaining the more or less accurate relation between the

105

solvatochromic scales which include or not the dispersive interactions. The sum of the

106

logarithm of these three corrected partition coefficients K” provides the P’ polarity value:

107 108

P’ = log(K”)ethanol + log(K”)dioxane + log(K”)nitromethane

110

(3)

us

109

cr

ip t

102

Then the ratios of logarithm of these adjusted partition coefficients and P’ :

an

111 112

M

113

(4)

114

determine xe (the part of basicity), xd (the part of acidity), xn (the part of dipole) for each

116

solvent. The sum of the three last parameters (xi) is equal to 1. For instance, for ethyl acetate,

117

the values are: xe = 0.36; xd = 0.22 and xn = 0.42, meaning that ethyl acetate mainly interacts

118

through dipole-dipole interactions (xn), and as an acceptor in hydrogen bonding (xe).

119

Obviously this statement depends on the solute properties, and could be valid for non-polar

120

compounds.

pt

Ac ce

121

ed

115

Then, each solvent was plotted in a selectivity triangle depending on these three relative

122

values. In this figure, solvents having the same properties were located in a close area. Eight

123

groups were defined. The drawbacks of this scale were discussed previously [5,6]. In

124

particular, this scale leads to classify alcohols solvents as basic, and aromatic solvents are

125

classified as acidic. These inconsistencies were also reported to justify the introduction of the

126

solvatochromic solvent selectivity approach [7].

127

In this approach, the three solvatochromic parameters (, and *; hydrogen bond

128

acidity, basicity and polarity-polarizability) were obtained by spectroscopic UV-visible

129

measurements [8]. The solvatochromic parameters were normalized. For instance, * = 0 for

130

cyclohexane and 1 for dimethylsulfoxide (DMSO) [9]. The  parameter is calculated based on

Page 4 of 52

131

a reference (hexamethylphosphamide) equal to 1 [10], whereas the  parameter is calculated

132

based on another reference (methanol) equal to 1 [11]. The values were also corrected for

133

dispersion interactions by referencing to an alkane of similar size, and then normalized.

134

However, the sum of the three solvatochromic parameters can be any values.

135

The correlations between the Snyder and the solvatochromic parameters were studied [7] and were rather disappointing. Another triangle was used to plot the data allowing the

137

solvent classification. However, due to the absence of acidity for aromatic solvents or amides,

138

amines, esters, a lot of solvents were located on a line joining the basic and dipolar summits

139

of the triangle, leading to a poor discrimination of a large number of solvents.

cr

140

ip t

136

The Hansen parameters [12,13] were derived from the Hildebrand ones [14].

Hildebrand described the use of solubility parameters (), which is related to cohesive energy

142

density (CED) through the energy of vaporization ΔE per unit volume Vm:

an

us

141

M

143

(5)

ed

144

pt

145

It is also related to four partial solubility parameters covering dispersive (d), dipole (o),

147

proton acceptor (b) and proton donor (a), based on free energy of vaporization of the pure

148

solvents per molar volume unit [15].

149

Ac ce

146

By extension, Hansen suggested that the total solubility parameter was the sum of three

150

contributions, dispersive (d), polar (p) (Keesom and Debye) and hydrogen bonding (h)

151

(donor and acceptor) [16]:

152 153

(6)

154 155

This simplification of the Hildebrand theory which described five partial solubility

156

parameters, also took into account the dispersive interactions, which were not included in the

157

Snyder and solvatochromic triangles. They were based on the energy of vaporization per unit

Page 5 of 52

volume, which is related to the cohesive energy of the liquid solvent. This energy is composed

159

by dispersive (Ed), polar (Ep) and hydrogen bonding (Eh) interactions. Thus compared to the

160

previous scales discussed, that scale added dispersive interactions, and joined the hydrogen

161

bond acidity and basicity. The representation of these parameters can be achieved by using a

162

three-dimensional space, which is rather uncommon. By calculating fractional parameters,

163

again normalized units, a triangle plot called Teas diagram [17,18] was used similarly to the

164

SST and the SSS parameters. A general discussion about the use of selectivity triangles

165

emphasized the varied uses of such plots [19].

ip t

158

The solvation parameter model describes five interactions by using five descriptors

167

related to the compound properties (Abraham descriptors) [20-21]. E is the excess molar

168

refraction, related to the presence of n- and -electrons resulting in charge transfer, -

169

interactions or dipole – induced dipole interactions; S stands for the presence of dipoles and

170

polarizability; A and B describe hydrogen bond acidity and basicity, and V is McGowan’s

171

volume, related to dispersive interaction and cavity energy formation.

us

an

172

cr

166

The V descriptor is calculated based on the compound structure (atoms and bonds). Instead of the V descriptor suited for transfer between condensed phases, the L descriptor can

174

be used for transfer from a gas to a condensed phase. L is related to the gas-liquid partition

175

coefficient with n-hexadecane as solvent at 25°C. This descriptor is well suited for the

176

investigation of gas chromatography systems. E is calculated from the refractive index, A and

177

B are determined by liquid-liquid partition, and S from liquid-liquid partition and

178

chromatographic measurements [22].

ed

pt

179

M

173

These descriptors are known for thousands of compounds. Linear solvation energy relationships (LSERs) calculated with these descriptors were extensively used to describe

181

liquid-phase and gas-phase chromatographic systems [23]:

182 183 184 185

Ac ce

180

X = c + vV + eE + sS + aA + bB

(7)

X being the studied property. The coefficients obtained from such models (e, s, a, b, v

186

or l) are related to the descriptors allowing the comparison of numerous stationary and mobile

187

phases [24] or solvents [25], or biological partition systems. However, due to the large

188

number of descriptors (five), no classification map can be easily drawn and these system

189

comparisons were generally achieved by using cluster analysis or ranking methods [24,25].

190

PCA approach seems ineffective because none of the resulting score plots provide a useful

Page 6 of 52

191

solvent classification, despite the high variance percentage (93%) described by the four

192

principal components [25].

193

The works of Klamt [26-32] reported that COSMO-RS calculates the dielectric

195

screening charges and energies of a van der Waals-like molecular surface. This calculation

196

considers the solute embedded in a virtual conductor, and uses a gas-phase reference energy.

197

From the plot of the -potentia, i.e. the plot of the chemical potential s() vs. the polarization

198

charge density of the surface fragment (), hydrophobicity and hydrogen bonding (donor,

199

acceptor) properties of a solvent can be calculated. The partition coefficients of a solute into a

200

solvent can be represented as a linear combination of -moments. The set of some relevant

201

moments allows to compare any solvents. Recently, 61 descriptors were extracted and treated

202

by PCA analyses (due to the great number of descriptors) [33,34]. These works classified

203

solvents into 10 clusters having various properties by using three principal components (PC1,

204

PC2, PC3), i.e. by a 3D space, which represented 85% of the data variance. PC1 and PC2

205

were closely related to hydrogen bond donor and acceptor, whereas PC3 could be correlated

206

both to lipophobicity and dipolarity. Moreover, some loss of information (15%) is noticed

207

when reducing the data set from 61 to 3 parameters [33], showing that the use of a reduced set

208

of data could achieve a performing classification.

cr

us

an

M

ed

209

ip t

194

The comparison of two solvent classifications [35] was also studied, by comparing the results provided by the use of the five LSER descriptors (E, S, A, B, V) and the five

211

COSMOments of Klamt’s [30-32]: Sig2 (overall electrostatic polarity) and Sig3 (asymmetry

212

of the  profile) for polarity/polarizability, Hb don3 for hydrogen bond acidity, HB acc3 for

213

hydrogen bond basicity, and CSA for the surface area. It showed a large overlap of the

214

information content, despite a different distribution of the properties [35].

Ac ce

215

pt

210

Correlation between these five COSMOments and the Hansen parameters was also

216

checked [36], showing ability of the COSMOments to describe the Hansen parameter. We

217

must point out that both the LSER and Hansen approaches are heuristic ones, i.e. based on

218

numerous experiments, whereas the COSMO-RS one is rather a theoretical one, based on

219

time-consuming calculation. Such as previous conclusions, these studies [35,36] also indicate

220

that the solvent properties space can be well described by a five-dimensional space, and that

221

the use of the five basic moments avoids the over-parametrization when training the artificial

222

neutral network [36]

223

Page 7 of 52

224

The goal of this paper is to provide a new visual approach to the classification of

225

solvents, based on published data describing their chemical properties. Previously, we have

226

extensively used this classification called spider (or star) diagram for the classification of

227

supercritical fluid chromatography systems, which showed its ability for comparing numerous

228

and diverse data such as those describing very different chromatographic systems.

230

We applied this approach for numerous solvent classification systems, to provide a simple way of comparing the solvents used in varied separation and extraction systems.

ip t

229

231

233

cr

232

2. Material and methods

235

us

234

All data were collected in the literature and are presented in Tables 1 to 6. The spider diagram construction was first presented elsewhere [37]. Basically, a star comprising as many

237

branches as necessary (depending on the number of parameters, from three to five in this

238

paper) must first be plotted, with equal angle spacing between the branches. For models using

239

five parameters, they should not be placed at random but setting side by side those that are the

240

most positively correlated (for instance E and S, Sig2 and Sig3, or A and B, Hb Acc3 and Hb

241

Don3), in opposition those that are negatively correlated, while the least correlated ones

242

should be placed in an orthogonal fashion if possible. For LSER descriptors, other

243

arrangements of axes were studied, for instance E, A, B, S, V or B, S, A, E, V, but none of

244

these other arrangements provided a better separation of the groups of solvents in regards to

245

the one presented. The centre of the star serves as centre of the diagram, and each point is

246

plotted according to its parameters (Figure 1). The parameters can be viewed as the

247

coordinates of a solvation vector

248

point of this solvation vector. The way of reading this figure is not obvious at first sight as

249

anyone familiar with principal component analysis score plots would be tempted to interpret

250

the proximity of a point and an axis as an indication of the dominant factor. However, it is not

251

the case. The star is only represented to indicate the origin of the reference space and the

252

directions that allowed placing the points, but only the distances between the points are

253

significant. On figure 1, the continuous blue lines support the five values of the E, S, A, B, V

254

descriptor of formamide, making a geometrical figure, and the blue dotted lines indicate the

255

point displacement from the centre to the extremity of the solvation vector. For hexane, in

Ac ce

pt

ed

M

an

236

Ei, Si, Ai, Bi, Vi) [38], and each point is placed at the end-

Page 8 of 52

256

brown of figure 1, the displacement only follows the V axis because all the values for other

257

descriptors are equal to zero.

258

In the case of LSERs, we had shown that it was preferable to plot normalized

259

parameters. Normalization can be achieved by dividing the coordinates by the length of the

261

solvation vector, which is calculated as the square root of the sum of squares of all

262

coordinates. This way, the comparisons were related to the chromatographic system’s

263

selectivity rather than system’s retention. For other classification systems, other normalization

264

modes can be used.

cr

ip t

260

Moreover, to indicate the overall strength of interactions, the points are preferably

266

presented as bubbles, with the size of the bubbles being related to the length of the solvation

267

vector (in the case of LSERs) or to polarity parameters as further detailed below. Thus when

268

two bubbles are close in the spider diagram, it indicates that the solvents have close

269

selectivity, while the largest bubble indicates the strongest interactions.

an

us

265

The spider diagram for the Snyder and the solvatochromic data sets display the same

270

axes location as for the triangles in referenced literature. Examples of use of these

272

classification maps will be presented and discussed.

M

271

276 277 278

Results and discussions

3.1. Snyder’s solvent selectivity triangle (SST)

pt

275

3.

Ac ce

274

ed

273

Figure 2 shows the spider diagram obtained from the SST data (table 1). Each point

279

represents one solvent, and the bubble size is related to the polarity parameter P’. The small

280

black point into each group is the barycentre indicating the average value of all the solvents

281

included in one group. The group number, as well as the circles surrounding these groups are

282

the same as the ones used by Snyder. This number will also be used for figures 3 and 4,

283

plotting respectively the solvatochromic and the Hansen parameters.

284

Obviously, the relative position of each group to the other is the same as on the Snyder

285

triangle. One exception is for group 5 which is located close to the dipolar corner on the

286

Snyder triangle whereas it is located between groups VII and VIII on the spider diagram.

287

However, this change is not related to the data treatment, as it was announced by a re-

Page 9 of 52

288

evaluation of Snyder data [7], after the shift of chloroform and methylene chloride based on

289

new data values.

290

Besides, the addition of polarity values through the bubble size clearly indicates the high polarity of water in regards of the one of methanol and acetonitrile, the two organic

292

solvents classically used in RPLC. When going from methanol or acetonitrile towards water,

293

the polarity of the mobile phase increases, explaining the decrease in the eluotropic strength

294

observed in RPLC.

295

ip t

291

In this case, the introduction of the polarity P’ in the spider diagram can compensate for the lack of dispersive interactions, because there is a reversed relationship between polarity

297

(in the sense of the P’ calculation) and the dispersive interactions.

To conclude, for the Snyder’s data, the use of the spider diagram does not change the

us

298

cr

296

visual use of this classification, except by taking into account the solvent polarity. The

300

drawbacks of the measurements are identical, i.e. due to the choice of the three solvent probes

301

selected to assess the distribution coefficients.

302

3.2. Solvatochromic solvent selectivity

M

303 304

Figure 3 shows the spider diagram obtained from the solvatochromic parameters (table

ed

305

an

299

2). The spider diagram representation displays a clear advantage in regards to the triangle

307

graph: the aromatic solvents (group VII) and the amide solvents (group III) are not stacked

308

along an axis, but scattered in the selectivity space depending on their different basic

309

character. The alcohols (group II) clearly display their acidic and basic properties; acetic acid

310

is located with water due to its acidic character, whereas ethylene glycol and benzyl alcohol

311

are far from group VIII. The chlorinated solvents (group V) are always located between

312

groups VII and VIII, but some overlapping is observed between groups VIb and VII on the

313

one hand, or groups VIa and III on the other hand. To simplify diagrams from solvatochromic

314

data, the plot of the polarity π* vs. basicity βwas suggested in the goal to replace common

315

solvents by greener ones [40]. This simplification works only because the data set is first

316

divided into two groups: protic and non-protic solvents, otherwise the lack of the acidic value

317

α would lead to an overlapping of alcohols with a lot of non-protic solvents.

318

Ac ce

pt

306

The position of hexane, limonene and p-cymene, all close to the center of the spider

319

diagram because of their low values for each of the three plotted parameters, evidences the

320

lack of a parameter accounting for dispersive interactions in the SSS model.

321

Page 10 of 52

322

3.3. Hansen parameters

323 324

Figure 4 shows the spider diagram using the Hansen parameters (table 3). Because of the fusion of the two components of hydrogen bonds (acidic and basic), the value of h for

326

water is very high, as well as that of alcohols and acids, although to a lesser extent. Due to the

327

presence of a parameter accounting for dispersive interactions, the other organic solvents are

328

located in a narrower area than for the two previous diagrams (Fig.2 and Fig. 3). The addition

329

of dispersive interactions to the data set (with regards to the two previous scales discussed

330

above), and the mixture between acidic and basic hydrogen bonds both induce some

331

overlapping of several groups: II and IV, which contain alcohols and acetic acid, I (ester) and

332

VI, V (Chlorinated), VIb (nitrile) and VII (aromatic). The resulting clusters are thus not very

333

satisfying on the base of the groups defined by Snyder. However, this figure shows other

334

clusters including solvents having close behavior: methyl-t-butyl ether (MTBE) /

335

tetrahydrofuran (THF) / ethyl acetate / di-ethyl ether, or di-propyl ether / chloroform /

336

limonene, or benzene / toluene / carbon tetrachloride.

an

us

cr

ip t

325

As an example, the solubility of fullerene C60 is plotted on the same figure: it is located

338

very close to hexane, benzene, toluene, or carbon tetrachloride, probably indicating acceptable

339

solubility of C60 in these solvents. This is an example that the spider diagram can be used in

340

the goal to select suitable solvents for purification steps, or to study its compatibility in

341

polymers to provide new materials, due to its unusual optical and redox properties [41].

342

Besides, DMSO, which is often used as a dilution solvent before chromatographic analysis to

343

improve analyte solubility, is located at the centre, showing the ability to this solvent to

344

develop varied interactions with solutes.

Ac ce

pt

ed

M

337

345

The spider diagram can also be used when looking for environmentally-friendly

346

solvents for extraction processes in agreement with sustainable development (table 4).

347

Beyond solvent toxicity (dioxane, formaldehyde and acetonitrile), the question of “green

348

solvents” can be related to varied points: energy required to manufacture and number of

349

chemical steps in the synthesis (for instance, both of them are high for THF), energy to

350

distillate, ozone layer depletion, production from renewable resources etc [40, 42].

351

For natural compounds extracted from rapeseeds (fatty acids, triglycerides, tocotrienols,

352

tocopherols, sterols) [43], Figure 5 shows, as could have been expected, that alcohols are less

353

adapted than hexane for the extraction of lipophilic compounds. As was reported by the

354

authors, our diagram shows that hexane can be replaced by limonene, p-cymene or α-pinene

355

which all display close properties. The location of carbon dioxide [47] in the same area of the

Page 11 of 52

356

diagram shows its possible successful use to ensure high recovery of the studied compounds,

357

avoiding the concentration step of the extraction liquid when using terpenes as extractive

358

solvents. The Hansen parameters were recently used for the selection of green solvents to be used

360

in industrial processes [46]: triterpenes (α-terpineol), propylene carbonate, solketal, isosorbide

361

and glycol derivatives. For instance, the replacement of methylene chloride, largely used in

362

normal-phase and non-aqueous reversed-phase HPLC is of a prime interest [48].

ip t

359

Figure 5 shows that solketal or a mixture of carbon dioxide and methanol (90:10 v/v)

363

could be used instead of pure methanol. Unfortunately, the viscosity of solketal is about

365

eleven times the viscosity of water, meaning that no HPLC pumping system could provide the

366

necessary pressure to deal with such high viscosity [49]. On the opposite, the low viscosities

367

of CO2-organic solvent mixtures permit the use of both long column lengths and high flow

368

rates to ensure short and efficient analyses [50-54]. In fact, most green solvents are currently

369

applied to extraction processes or selective solvation, while their employment in

370

chromatography remains rather limited [43-47].

an

us

cr

364

However, in a recent publication, the use of propylene carbonate instead of acetonitrile

M

371

for HILIC was reported [55]. Because propylene carbonate is not fully miscible to water,

373

ternary mixtures with ethanol were prepared. Despite some drawbacks, such as the increase in

374

the pressure drop (the viscosity of propylene carbonate is equal to 2.4 cp at 25°C), some

375

reduction of both the chromatographic efficiency and the mass spectrometer sensitivity, the

376

use of propylene carbonate instead of acetonitrile for HILIC chromatography is affordable for

377

polar compounds. Moreover, it is suitable for large volume injection in bioanalytical analyses

378

[56].

pt

Ac ce

379

ed

372

The Hansen parameters are generally used to assess the solubility of compounds in a

380

solvent. For instance, Figure 6 shows the spider diagram for paracetamol solubility.

381

Compared to Hansen sphere, it is rather easier to find suitable solvents that show close

382

proximity to the paracetamol point (they are circled on Fig. 6). Nevertheless, as suggested

383

previously, it does not ensure the relevance of the calculated parameters with regards to the

384

real solubility data [57,58]. These data, as well as those further described in the present paper,

385

do not describe the effect of different polymorphs (crystalline forms) of a drug, which can

386

modify their respective solubility [59,60], and do not take account of the varied models used

387

to describe solvent properties.

388 389

Page 12 of 52

390

3.4. Abraham descriptors

391 392

Generally, the studies carried out with the LSER model use the model coefficients e, s, a, b, and v or l to describe the properties of the chromatographic or solubility systems. The

394

classification of solvents commonly used for separation processes, from values (e, s, a, b, l)

395

obtained for gas-to-solvent transfer of, was made by hierarchical cluster analysis [25]. For the

396

classification of these thirty-six solvents, seven clusters were defined, together with four

397

independent solvents (trifluoroethanol, water, dimethylsufoxide and dimethylformamide)[25].

398

Figure 7 shows these values (e, s, a, b, l) plotted for 30 solvents on the spider diagram. The

399

same seven clusters and the independent solvents are indicated on Figure 7. The location of

400

the clusters from the spider diagram is well in accordance with the one gained from the HCA

401

[25], but provides an additional information as it shows position of the solvents relative to

402

each type of interactions. For instance, acetonitrile and propylene carbonate are strongly

403

dipolar, displaying higher s values than alcohols or ketones, esters and ethers (KEE) (acetone,

404

dioxane…), whereas they are less acidic than alcohols and KEE. However, due to the

405

projection of five coefficients, which obviously causes some loss of information, acetonitrile

406

and propylene carbonate appear closer to KEE, whereas the HCA [25] indicated that they are

407

equally far from alcohols and KEE.

410

cr

us

an

M

ed

409

Nevertheless, this comparison shows the ability of the spider diagram for classifying the solvents on the basis of their chemical properties. Rather than plotting gas-to-liquid partition data (e, s, a, b, v), the molecular descriptors

pt

408

ip t

393

of the solvent molecules (E, S, A, B, V)(table 5), indicating their capabilities for defined

412

interactions, can also be compared (Figure 8). It is worth noting that these descriptors were

413

developed to be used as solute scales, not solvent scales, which can change the value of the

414

parameters (e.g., the basicity of bulk solvent hydrogen bond donors versus their behavior at

415

infinite dilution).

416 417

Ac ce

411

The bubble size here is defined by the ratio V/U. U is the length of the solvation vector associated to the chromatographic descriptor, and calculated from equation (8):

418 419

Ui 

E i2  S i2  Ai2  B i2  V i 2

(8)

420 421 422

This vector length is a valuable tool to compare the strength of the interaction capabilities for each solvent.

Page 13 of 52

423

Figure 9 shows the relationship between P’, the Rohrschneider polarity, and either V or V/U. Obviously, the slope is negative because polarity, i.e. polar interactions, and the

425

McGowan volume V, i.e. the molecular volume indicating capabilities for dispersive

426

interactions are opposite. However, the regression coefficient is significantly improved when

427

using the normalized V/U rather than the V value, explaining our choice of V/U for the

428

diagram in Figure 8. With this parameter, the point size is inversely related to polarity,

429

explaining that the bubble representing water is the smallest, whereas the size for alkanes is

430

the largest and equal to 1. Additionally, the use of V/U is useful to compare the polarity of

431

solvents for which no P’ values are available.

cr

ip t

424

In a general way, several groups of solvents are rather well distinguished and their

433

relative location makes sense (Fig. 8). Moreover, the relative position of the solvents in Figure

434

8 is similar to the one observed Figure 7, indicating that the molecular descriptor values (E, S,

435

A, B, V) are appropriate to compare solvent properties.

an

436 437

us

432

As expected, water is located at the bottom right, showing its high acidity (A is large) and weak hydrophobicity (V is low). In the vicinity of water are located alcohols, then acetic

439

acid and formamide. Nitriles (like acetonitrile) display higher dipole interactions and are

440

located at the right-hand-side of the plot, above alcohols. Alkanes, with high hydrophobicity,

441

are naturally at the opposite of the figure, on the left, close to the V axis. Aromatic solvents

442

are at the top of the diagram, around the E axis. THF, 1,4-dioxane, acetone and ethyl acetate,

443

which were generally located in the same cluster or in close clusters in other diagrams (Fig. 2

444

and Fig. 3), are also located in the same group here, at the center of the spider diagram. As

445

expected, Figure 10a shows that these four solvents display close values for all the five

446

descriptors. Compared to previous figures, an improvement in the classification can be

447

noticed for instance for nitrobenzene and nitromethane. These two solvents were classified

448

with toluene and benzene in the Snyder diagram (Fig. 2), whereas nitrobenzene is now closer

449

to benzonitrile, and nitromethane to acetonitrile. With regards to the Hansen plot (Fig. 4), the

450

use of five descriptors provides a better discrimination of MTBE and ethyl-acetate, two

451

solvents which are often used for liquid extraction of plant matrices [65,66], ethyl-acetate

452

having a higher dipole (S) value (Fig. 10a).

Ac ce

pt

ed

M

438

453

However, some discrepancies are noticed. First, the location of ethers that are placed

454

close to alcohols. Figure 10b shows the descriptor values for 1-butanol, dimethyl-, diethyl-

455

and dipropyl-ether. Ethers have zero values for E and A descriptors, whereas 1-butanol

456

displays comparable values for these two descriptors. Due to the spatial arrangement selected

Page 14 of 52

457

for the five descriptors in the spider diagram, E and A axes are almost opposite. As a result,

458

the two combinations, close values for E and A (1-butanol) or zero values for E and A (ethers)

459

yield a close location for these solvents, because they also display almost identical values for

460

S, B and V. A similar issue can be reported by comparing the location of pyridine and chlorinated

461

solvents (methylene chloride, chloroform and carbon tetrachloride). Figure 10c shows that

463

pyridine has greater B, E and S values than chloroform, but the combination of the three

464

differences, arranged in opposite directions (B vs. E and S), leads to a close location of the

465

two solvents, whereas the solvents properties are not exactly identical. However a close

466

location of chlorinated solvents and pyridine was also reported on the Hansen diagram (Fig.

467

3).

cr

us

468

ip t

462

Finally, Figure 10d shows the comparison of descriptors for acetic acid and ethylene glycol, which are mainly differing be the E and B values, but once again, these small

470

differences compensate on the diagram leading to a very close location.

471

an

469

The relationship between the miscibility and the location of the solvents onto the spider diagram can be discussed. In the group including THF, 1,4-dioxane, acetone and ethyl acetate,

473

the first three are miscible with water, whereas the solubility of ethyl acetate in water is only

474

partial (around 10% at ambient temperature). MTBE is not miscible to water neither. When

475

looking at the descriptor values (Fig.10a), one can see that whatever the values of E, S, A and

476

B, the two non-miscible solvents (MTBE and ethyl acetate) display V values higher than 0.7,

477

whereas the three others (THF, 1,4-dioxane and acetone) have lower V values. This would

478

indicate that solvent miscibility to water is first related to dispersive interactions, i.e. is not

479

related to the global location onto the diagram but to specific solvent properties. There are

480

two exceptions to this hypothesis, for chlorinated solvents: chloroform and methylene

481

chloride (V is respectively equal to 0.617 and to 0.494) (Table 5). However, among the

482

solvents having a V value lower than 0.7, chloroform and methylene chloride do not display

483

basic properties, probably explaining, in that case, the non miscibility between water and

484

these two solvents. However, we may point out that the location of chloroform and

485

dichloromethane in the diagram is far away of water. We can thus conclude that good

486

miscibility to water is ensured with small molecular volume, together with hydrogen-bond

487

acceptor capabilities.

Ac ce

pt

ed

M

472

488 489 490

This diagram (Figure 11) also allows comparing the location of the solvents used, either for RPLC (water, methanol, acetonitrile, THF) or for the Arizona system of solvents used in

Page 15 of 52

491

counter-current chromatography (heptane - ethyl acetate – methanol - water) [65,68-69], or

492

for other CCC solvent systems (chloroform – methanol - water or ethyl acetate- 2-butanol -

493

water)[70] and liquid-liquid extraction solvents.

494

As regards RPLC, we have already pointed out that the high acidic character of water together with a low V value leads to a location at the bottom right of the diagram (Fig. 8). The

496

V value for the three organic solvents usually combined to water in RPLC (methanol,

497

acetonitrile or THF) is lower than 0.7, ensuring their good miscibility. Besides, Figure 12a

498

shows that the E and B values of the three RPLC organic solvents are not strongly different,

499

explaining their relative proximity in the spider diagram. It also displays that the three organic

500

solvents are complementary, methanol being the most acidic, and acetonitrile displaying the

501

greatest dipole value. The use of THF, having the greatest V value, favors the higher

502

solubility of most of the organic compounds through dispersive interactions, explaining its

503

high eluting strength in RPLC.

cr

us

an

504

ip t

495

Figure 12b shows the changes in the descriptor values for the four solvents used in the Arizona system for counter-current chromatography (hexane, ethyl acetate, methanol, water).

506

Good miscibility between hexane and ethyl acetate is related to the high V descriptor, which

507

shows that these two solvents are able to interact by dispersive interactions. Basic character

508

and dipole interactions favor mixing between ethyl acetate and methanol, whereas hydrogen

509

bond acidity, basicity, and dipole interactions allow good miscibility between methanol and

510

water.

ed

Some attempts were done to replace heptane by limonene, in the goal to reduce the

pt

511

M

505

toxicity of the solvents used in CCC [71]. Limonene is a cyclic monoterpene coming from

513

orange and citrus stripper oil, easily available and renewable, which is considered as a greener

514

solvent than alkanes. This study showed that the use of limonene instead of heptane in CCC

515

slightly favored the solubility of benzene, toluene and diethylphthalate. When comparing the

516

location of the two solvents (limonene and heptane) to the ones of the extracted compounds

517

(benzene, toluene and diethylphthalate) (Fig. 11), it appears that limonene is as close to these

518

three compounds as is heptane. This example evidences the usefulness of the spider diagram

519

in the search of sustainable substitute solvents for various applications.

Ac ce

512

520 521

3.5. COSMO-RS descriptors

522 523 524

Figure 13 displays the classification obtained from the five COSMOments CSA, Sig2, Sig3, Hb acc3 and Hb don3 reported in other papers [30-32,35](table 6). Such as for Abraham

Page 16 of 52

descriptors, these values are calculated for isolated compounds, but were previously used for

526

solvent classification by using PCA [33-34]. The spatial arrangement of the five axes is close

527

to the one used for the LSER classification, as CSA and V are related to the surface or volume

528

of the molecule; Sig2, Sig3, E and S are related to polarizability and dipole interactions; Hb

529

acc3, Hb don3, A and B are related to hydrogen bond donors and acceptors. A comparison

530

between these two data sets showed the large overlap of the two descriptor sets, although the

531

chemical information was differently distributed among the descriptors [35]. Indeed, the E

532

term is not well correlated to the COSMOments, whereas if A and B are mainly depending on

533

Hb don3 and Hb acc3, Sig2 and Sig3 are also related to A and B, meaning that the chemical

534

content of Sig2 and Sig3 probably includes a part of A and B information.

cr

ip t

525

Figure 13 can be compared to Figure 8 in terms of relevance of clusters. Despite the

536

similar ordering of the five parameters (V, E, S, A, B) and (CSA, Sig2, Sig3, Hb don3, Hb

537

acc3), the location of the clusters is not identical. Some of these clusters are well separated

538

from the others (as alkanes, chloro-alkanes and aromatics), but others overlap (as alcohols and

539

non-protic dipolar solvents ethylacetate, THF, acetonitrile and acetone). Several papers report

540

the good relevance of this classification on the base of hydrogen bonds, but also underline its

541

weakness for taking into account the dipolar character and the dispersive interactions [33,

542

72,73].

an

M

ed

543

us

535

With the data of the PCA used for this classification based on the COSMO-RS data[33], we can also apply the spider diagram to plot the coordinates of the first three principal

545

components (F1, F2 and F3) on the same figure (figure 14). It shows that this type of spider

546

diagram can also be applied to present results from PCA with only one figure including the

547

three main components F1, F2 and F3, whereas this type of result is presented with bi-

548

dimensional plots, F1 vs F2 or/and F1 vs F3. Figure 15 shows the spider diagram for classical

549

solvents from this PCA coordinates. This classification, obtained on the basis of 61 COSMO-

550

RS descriptors, displays some surprising ranking, for instance, nitromethane is located in the

551

same group as water; alkanes and aromatics are together in a group of non-polar solvents;

552

acetone, acetonitrile, DMSO and 1,4-dioxane are located in the same group; N-

553

methylformamide is clustered with methanol. On another hand, methanol is not included in

554

the same group as ethanol.

Ac ce

pt

544

555 556 557

4. Conclusion

Page 17 of 52

558 559 560 561

The spider diagram approach has been applied for classification of solvents on the basis of their physico-chemical properties. This diagram provides a universal way to compare the varied and well known classification scales, and overcomes some difficulties in the representation of properties

563

encountered with other visual presentations (triangles, spheres, cubes), for instance in the case

564

of zero values for one parameter (solvatochromic parameters), or for a parameter number

565

higher than three (Abraham descriptors, COSMOments). Obviously, this new presentation

566

does not modify the raw data, i.e. their relevance or their failure. At least, it allows to add to

567

the studied interactions one more parameter, i.e. the polarity P’ or the total solubility

568

parameter for the Snyder, the solvatochromic or the Hansen parameters, which is represented

569

through bubble size on the map.

us

cr

ip t

562

Applied to Abraham descriptors, and with combination of the V/U parameters, it leads

571

to a simple view of the solvent groups having similar or different properties. By comparison

572

with the diagrams using three parameters, this classification, using five parameters, is slightly

573

different, and the shift of some solvents with regards of other classification scales makes

574

sense, from a chemist’s point of view. Besides, the plot of COSMOments could appear less

575

relevant than the plot based on Abraham descriptors, because of the overlapping of some

576

clusters.

M

ed

577

an

570

Finally, the spider diagram permits to compare a lot of scales of solvent properties by using an identical treatment of data , and is very useful to select suitable solvents with

579

regards of the desired analytical method, for extraction, separation or purification approaches,

580

and for solubility studies, or as an aid for choosing greener solvents.

Ac ce

581

pt

578

Despite the simplicity of the spider diagram classification, and whatever the scales used,

582

the future limitations of its use will be related to the calculation of parameters or descriptors

583

for new solvents or for mixtures of solvents, for instance for modifier/CO2 mixtures.

584 585 586

References

587 588

[1] L. Rohrschneider, Solvent characterization by gas-liquid partition coefficient of selected

589

solutes, Anal. Chem. 45 (1973) 1241-1247.

590

[2] L.R. Snyder, Classification of the solvent properties of common liquids, J. Chromatogr.

591

Sci. 16 (1978) 223-234.

Page 18 of 52

[3] L.R. Snyder, Classification of the solvent properties of common liquids, J. Chromatogr. 92

593

(1974) 223-230.

594

[4] E.F. Meyer, T.A. Renner, K.S. Stec, Cohesive energies in polar organic liquids. II. The n-

595

alkyl nitriles and the 1-chloroalkanes, J. Phys. Chem. 75 (1971) 642-648.

596

[5] L. Szepesy, Possibilities and pitfalls in defining selectivity in HPLC, Chromatographia, 51

597

(2000) S98-S107.

598

[6] V.J. Barwick, Strategies for solvent selection - A literature review, Trends Anal. Chem. 16

599

(1997) 293-309.

600

[7] S.C. Rutan, P.W. Carr, W.J. Cheong, J. H. Park, L.R. Snyder, Re-evaluation of the solvent

601

triangle and comparison to solvatochromic based scales of solvent strength and selectivity, J.

602

Chromatogr. 462 (1989) 21-37.

603

[8] M.J. Kamlet, R.W. Taft, P.W. Carr, M.H. Abraham, J. Chem. Soc. Faraday Trans. I,

604

Linear solvation energy relationships. Part 9-Correlation of gas/liquid partition coefficients

605

with the solvatochromic parameters, π*, α and β, 78 (1982) 1689-1704.

606

[9] M.J. Kamlet, J. L. Abboud, R.W. Taft, The solvatochromic comparison method. 6. The π*

607

scale of solvent polarities, J. Am. Chem. Soc. 99 (1977) 6027-6038.

608

[10] M.J. Kamlet, R.W. Taft, The solvatochromic comparison method. I. The β scale of

609

solvent hydrogen-bond acceptor (HBA) basicities, J. Am. Chem. Soc. 98 (1976) 377-383.

610

[11] M.J. Kamlet, R.W. Taft, The solvatochromic comparison method. II. The α scale of

611

solvent hydrogen-bond acceptor (HBD) acidities, J. Am. Chem. Soc. 98 (1976) 2886-2894.

612

[12] C.M. Hansen, The three dimentional solubility parameters - Key to paint component

613

affinities. I. Solvents, plasticizers, polymers and resins, J. Paint Technol. 39 (1967) 104-117.

614

[13] C.M. Hansen, Solvent for coating, Chem. Tech. 2 (1972) 547-553.

615

[14] J.H. Hildebrand, J.M. Prausnitz, R.L. Scott, Regular and Related Solutions, Van

616

Nostrand-Reinhold, New York, 10970.

617

[15] J.H. Hildebrandt, R.L. Scott, The solubility of non-electrolytes, 3rd ed. Dover,

618

Publications, New York, 1964.

619

[16] C.M. Hansen, Hansen solubility parameters: A user’s handbook, CRC press, Inc., Boca

620

Raton FL, 2007.

621

[17] J.P. Teas, Graphic analysis of resin solubility, J. Paint Technol. 40 (1968) 19-25.

622

[18] E. Stefanis, C. Panayiou, Prediction of Hansen solubility parameters with a new group-

623

contribution method, Int. J. Thermphys. 29 (2008) 568-585

624

[19] A.R. Johnson, M.F. Vitha, Chromatographic selelctivity triangles, J. Chromatogr. A 1218

625

(2011) 556-586.

Ac ce

pt

ed

M

an

us

cr

ip t

592

Page 19 of 52

[20] M.H. Abraham, Scales of solutes hydrogen-bonding: their construction and application to

627

physicochemical and biochemical processess, Chem. Soc. Rev. 22 (1993) 73-83.

628

[21] M.H. Abraham, A. Ibrahim, A. M. Zissimos, Determination of solute descriptors from

629

chromatographic measurement, J. Chromatogr. A 1037 (2004) 29-47.

630

[22] C. Poole, S.N. Atapattu, S.K. Poole, A.K. Bell, Determination of solute descriptors by

631

chromatographic method, Anal. Chim. Acta 652 (2009) 32-53.

632

[23] M. Vitha, P.W. Carr, The chemical interpretation and practice of linear solvation energy

633

relationships in chromatography, J. Chromatogr. A 1112 (2006) 143-194.

634

[24] C.F. Poole, S.K. Poole, Column selectivity from the perspective of the solvation

635

parameter model, J. Chromatogr. A 965 (2002) 263-299.

636

[25] C. Poole, T. Karunasekara, Solvent classification for chromatography and extraction, J.

637

Planar Chromatogr. 25 (2012) 190-199.

638

[26] A. Klamt, Conductor-like screening model for real solvents; a new approach to the

639

quantitative calculation of solvation phenomena, J. Phys. Chem. 99 (1995) 2224-2235.

640

[27] A. Klamt, V. Jonas, T. Burger, J.C.W. Lohrenz, Refinement and parametrization of

641

COSMO-RS, J. Phys. Chem. A 102 (1998) 5074-5085.

642

[28] A. Klamt, F. Eckert, Cosmo-RS: a novel and efficient method for the a priori prediction

643

of thermophysical data of liquids, Fluid Phase Equi. 172 (2000) 43-72.

644

[29] F. Eckert, A. Klamt, Fast solvent screening via Quantum chemistry: Cosmo-RS

645

approach, AIChe 48 (2002) 369-385.

646

[30] C. Mehler, A. Klamt, W. Peukert, Use of COSMO-RS for the prediction of adsorption

647

equilibria, AIChe 48 (2002) 1093-1099.

648

[31] A. Klamt, F. Eckert, M. Hornig, Cosmo-RS: a novel view to physiological solvation and

649

partition questions, J. Comput. Aid. Mol. Des. 15 (2001) 355-365.

650

[32] A. Klamt, F. Eckert, M. Diedenhofen, Prediction of soil sorption coefficient with a

651

conductor-like screening model for real solvents, Env. Tox. Chem. 21 (2002) 2562-2566.

652

[33] M. Durand, V. Molinier, W. Kunz, J.M. Aubry, Classification of organic solvents

653

revisited by using the COSMO-RS approach, Chem. Eur. J. 17 (2011) 5155-5164.

654

[34] L. Moity, M. Duran, A. Benazzouz, C. Pierlot, V. Molinier, J.M. Aubry, Panorama of

655

sustainable solvents using the COSMO-RS approach, Green Chem. 14 (2012) 1132-1145.

656

[35] A. M. Zissimos, M.H. Abraham, A. Klamt, F. Eckert, J. Wood, A comparison between

657

the two general stes of linear free energy descriptors of Abraham and Klamt, J. Chem. Inf.

658

Comput. Sci. 42 (2002) 1320-1331.

Ac ce

pt

ed

M

an

us

cr

ip t

626

Page 20 of 52

[36] G. Jarvas, C. Quellet, A. Dallos, Estimation of Hansen solubility parameters using

660

multivariate non linear QSPR modeling with COSMO Screening charge density moment,

661

Fluid Phase Equi. 309 (2011) 8-14.

662

[37] C. West, E. Lesellier, Characterisation of stationary phases in subcritical fluid

663

chromatography by the solvation parameter model II. Comparison tools, J. Chromatogr. A

664

1110 (2006) 191-199.

665

[38] Y. Ishihama, N. Asakawa, Characterization of lipophilicity scales using vectors from

666

solvation energy descriptors, J. Pharm. Sci. 88 (1999) 1305-1312.

667

[39] J.H. Clarck, D. J. Macquarrie, J. Sherwood, A quantitative comparison between

668

conventional and bio-derived solvents from citrus waste in esterification and amidation

669

kinetic studies, Green Chem. 14 (2012) 90-93.

670

[40] P. G. Jessop, Searching for green solvents, Green Chem. 13(2011)1391-1398.

671

[41] C. M. Hansen, A. L. Smith, Using Hansen solubility parameters to correlate solubility,

672

Carbon 42 (2004) 1591-1597.

673

[42] C. Capello, U. Fisher, K. Hungerbühler, What is a green solvent? A comprehensive

674

framework for the environmental assessments of solvents, Green Chem. 9 (2007) 927-934.

675

[43] Y. Li, F. Fine, A-S. Fabiano-Tixer, M. Albert-Vian, P. Carre, X. Pages, F. Chemat,

676

Evaluation of alternative solvents for improvement of oil extraction from rapeseeds, C. R.

677

Chimie, 17 (2014) 212-217.

678

[44] T. Fardi, E. Stefanis, C. Panayiotou, S. Abbott, S. van Loon, Artwork conservation

679

material and Hansen solubility parameters: a novel methodology towards critical solvent

680

selection, J. Cul. Heritage, 2013, in press

681

[45] web address: https://pirika.com

682

[46] A. Benazzouz, L. Moity, C. Pierlot, M. Sergent, V. Molinier, J. M. Aubry, Selection of a

683

greener set of solvent evenly spread in the Hansen space by space-filling design, Ind. Eng.

684

Chem. Res., 52 (3013) 16585-16597.

685

[47] M.W. Rowe, J. Phomakay, J.O. Lay, O. Guevara, K. Srinivas, W.K. Hollis, K.L.

686

Steelman, T. Guilderson, T.W. Strafford Jr., S.L. Chapman, J.W. King, Application of

687

supercritical carbon dioxide solvent mixtures for removal of organic material from

688

archeological artifacts for radiocarbon dating, J. Supercri. Fluids, 79 (2013) 314-323.

689

[48] J. P. Taygerly, L.M. Miller, A. Yee, E.A. Peterson, A convenient guide to help select

690

replacement solvents for dichloromethane in chromatography, Green Chem. 14 (2012) 3020-

691

3025.

Ac ce

pt

ed

M

an

us

cr

ip t

659

Page 21 of 52

[49] J. I. Garcia, H. Garcia-Martin, E. Pires, Glycerol based solvents: synthesis, properties and

693

applications, Green Chem. 16 (2014) 1007-1033.

694

[50] F. S. Deschamps, E. Lesellier, J. Bleton, A. Baillet, A. Tchapla, P. Chaminade,

695

Glycolipid class profiling by packed column subcritical fluid chromatography, J. Chromatogr.

696

A 1040 (2004) 115-121.

697

[51] K. Gaudin, E. Lesellier, P. Chaminade, D. Ferrier, A. Baillet, A. Tchapla, Retention

698

behaviour of ceramides in sub-critical fluid chromatography in comparison with non-aqueous

699

reversed-phase liquid chromatography, J. Chromatogr. A 883 (2000) 211-222.

700

[52] E. Lesellier, A. Latos, A. Lopes de Oliveira, Ultra high efficiency/low pressure

701

supercritical fluid chromatography with superficially porous particles for triglyceride

702

separation, J. Chromatography A 1327 (2014) 141-148.

703

[53] D.A. Roston S. Ahmed, D. Williams, T. Catalano, Comparison of drug substance

704

impurity profiles generated with extended length columns during packed-column SFC, J.

705

Pharm. Bioned. Ana. 26 (2011) 339-355.

706

[54] V. Abrahamsson, I. Rodriguez-Meizoso, C. Turner, Determination of carotenoids in

707

microalgae using supercritical fluid extraction and chromatography, J. Chromatogr. A 1250

708

(2012) 63-68.

709

[55] F. Tache, S. Udrescu , F. Albu, F. Micale, A. Medvedovici, Greening pharmaceutical

710

applications of liquid chromatography through using propylene carbonate-ethanol mixtures

711

instead of acetonitrile as organic modifier in the mobile phases, J. Pharm. Biomed. Anal., 75

712

(2013) 230-238.

713

[56] M. Cheregi, F. Albu, S. Udrescu, N. Raducanu, A. Medvedovici, Grenner bioanalytical

714

approach for LC/MS-MS assay of enalapril and enalaprilat in human plasma with total

715

replacement of acetonitrile throughout all analytical stages, J. Chromatogr. B 927 (2013) 124-

716

132.

717

[57] R.A. Granberg, A. C. Rasmussen, Solubility of paracetamol in pure solvents, J. Chem.

718

Eng. Data 44 (1999) 1391-1395.

719

[58] F.L. Mota, A.P. Carneiro, A.J. Queimada, S. P. Pinho, E. A. Macedo, Temperature and

720

solvent effects in the solubility of some pharmaceutical compounds: measurements and

721

modeling, Eur. J. Pharm. Sci. 37 (2009) 499-507.

722

[59] V. Jouyban, M. Khoubnasabjafari, F. Martinez, A. Pena, A. Jouyban, Solubility of drugs

723

in ethyl acetate-ethanol mixtures at various temperatures, J. Drug Del. Sci. Tech. 22 (2012)

724

545-547.

Ac ce

pt

ed

M

an

us

cr

ip t

692

Page 22 of 52

[60] J. Barra, F. Lescure, E. Doelker, P. Bustamante, The expanded Hansen approach to

726

solubility parameters. Paracetamol and cotric acid in individual solvents, J. Pharm.

727

Pharmacol. 49 (1997) 644-651.

728

[61] J. Jover, R. Bosque, J. Sales, Determination of abraham solute parameters from

729

molecular stucture, J. Chem. Inf. Comput. Sci. 44(2004)1098-1106

730

[62] T. Karunasekara, C. F. Poole, Determination of descriptors for flagrance compounds and

731

liquid-liquid partition, J. Chromatogr. A 1235(2012)159-165

732

[63] M. H. Abraham, R. E. Smith, R. Luchtefeld, A.J. Boorem, R. Luo, W.E.Acree Jr.,

733

Prediction of solubility of solubility of drugs and other compounds in organic solvents, J.

734

pharmacy. Sci. 99(2010) 1500-1515

735

[64] M.H. Abraham; R. Kumarsingh; J. E. Cometto-Miniz, W. S. Cain, /. Rosés, E. Bosch, M.

736

L. Diaz, The determination of solvation descriptors for terpenes and the prediction of nasal

737

purgency thresholds, J. Chem. Soc. Perkin Trans.2, (1998)2405-2411

738

[65] N. Zga, Y. Papastamoulis, A. Toribio, T. Richard, J.C. Delaunay, P. Jeandet, J.H.

739

Renault, J.P. Monti, J.M. Mérillon, P. Waffo-Téguo, J. Chromatogr. B 877 (2009) 1000-1004.

740

[66] T. Michel, E. Destandau, G. Le Floch, M.E. Lucchesi, C. Elfakir, Antimicrobial,

741

antioxydant and phytochemical investigations of sea buckthorn (Hippophaë rhamnoides l.)

742

leaf, stem, root and seed, Food Chem. 131 (2012) 754-760.

743

[67] A. Berthod, M. Hassom, M.J. Ruiz-Angel, Alkane effect in the Arizona liquid systems

744

used in counter-current chromatography, Anal. Bioanal. Chem. 383 (2005) 327-340.

745

[68] A. Marston, K. Hostettmann, Developments in the application of counter-current

746

chromatography to plant analysis, J. Chromatogr. A 1112 (2006) 181-194.

747

[69] A. Berthod, T. Maryutina, B. Spivanov, O. Shpigun, I.A. Sutherland, Countercurrent

748

chromatography in analytical chemistry, Pure Appl. Chem, 81 (2009) 355-387.

749

[70] Y. Lu, A. Berthod, R. Hu, W. Ma, Y. Pan, Screening of complex natural extracts by

750

countercurrent chromatography using a parallel protocol, Anal. Chem. 81 (2009) 4048-4059.

751

[71] K. Faure, E. Bouju, P. Suchet, A. Berthod, Use of limonene in countercurrent

752

chromatography: A green alkane substitute, Anal. Chem. 85 (2013) 4644-4650.

753

[72] H. Grensemann, J. Gmelhing, Performance of a conductor-like screening model for real

754

solvents model in comparison to classical group contribution methods, Ind. Eng. Chem. Res.

755

44 (2005) 1610-1624.

756

[73] T. Mu, J. Rarey, J. Gmelhing, Performance of COSMO-RS with sigma profiles from

757

different model chemistries, Ind. Eng. Chem. Res. 46 (2007) 6612-6629.

Ac ce

pt

ed

M

an

us

cr

ip t

725

758

Page 23 of 52

762 763

Figure captions

764 765

Figure 1. Example showing how to place a point in the spider diagram with five axes.

766

Figure 2. Spider diagram based on Snyder values Xn, Xd, Xe (data from table 1). The point

768

size is the Rohrschneider polarity P’.

ip t

767

769

Figure 3. Spider diagram based on solvatochromic parameters (data from table 2).

771

The point size is the Rohrschneider polarity P’.

cr

770

us

772

Figure 4. Spider diagram based on Hansen parameters d, p, h (data from table 3). The

774

point size is the total parameter t.

an

773

775 776

Figure 5. Spider diagram based on Hansen parameters for green solvents (data from table 4).

M

777

Figure 6. Spider diagram based on Hansen parameter showing the solubility (s) of

779

paracetamol in varied solvents at 30°C (indicated in parenthesis). The first solubility value

780

comes from ref. [57], and the second from ref. [58]. The solvents in the blue circle indicate

781

good solubility.

pt

782

ed

778

Figure 7. Spider diagram based on the LSER system coefficients (e,s,a,b,v) measured for gas-

784

to-liquid transfer [data from ref. 25].

785

Ac ce

783

786

Figure 8. Spider diagram based on Abraham descriptors E,S,A,B,V (data from table 5). The

787

point size is the V/U ratio.

788 789

Figure 9. Relationship between P’ (Rohrschneider polarity) and V (McGowan molecular

790

volume) (blue squares and interrupted line) or V/U (U defined in equation (8)) (red diamonds

791

and line) for numerous classical solvents.

792 793

Figure 10. Comparison of Abraham descriptor values for various solvents.

794

a/ yellow =acetone; green = THF; red = Dioxane; Blue = ethyl-acetete; light blue =MTBE

Page 24 of 52

795

b/ yellow = chloroform; green = pyridine

796

c/ green = 1-butanol; yellow = di-methyl-ether; orange = di-ethyl-ether; red = di-propyl-ether

797

d/ yellow = acetic acid; green = ethylene-glycol

798

Figure 11. Spider diagram for the Abraham descriptors and various analytical systems: water

800

– methanol – THF - acetonitrile: reversed-phase liquid chromatography solvents (RPLC);

801

heptane – ethyl acetate – methanol - water: Arizona counter current chromatography (CCC)

802

solvents; water - 2-butanol – ethyl acetate and water – methanol - chloroform: other CCC

803

systems.

cr

ip t

799

804

Figure 12. Abraham descriptor values for solvents used in RPLC and CCC.

806

(a) RPLC; green = water; pink = MeOH; blue = acetonitrile; light blue =

807

(b) Arizona Counter Current Chromatography; yellow = limonene; green = hexane; pink =

808

ethyl-acetate; blue = MeOH; light blue = water

809

Figure 13. Spider diagram based on COSMOments (data from table 6).

M

810

an

us

805

811

Figure 14. Spider diagram for 153 solvents based on principal components coordinates

813

F1,F2,F3 extracted from COSMOments (based on values from ref. [33]).

814

ed

812

Figure 15. Spider diagram for classical solvents based on principal components coordinates

816

F1,F2,F3 extracted from COSMOments (based on values from ref. [33]).

818

Ac ce

817

pt

815

Page 25 of 52

*Highlights (for review)

Highlights

The pider diagram unify the presentation of solvents properties This simple presentation is applied for scales having both 3 or five parameter

This classification is useful for separation, solubility and extraction methods

Ac

ce pt

ed

M

an

us

cr

This classification can be use for replacing toxic solvents by green ones

ip t

Snyder, Kamlet/Taft, Hansen, COSMO-RS and LSER scales are compared

Page 26 of 52

Ac

ce pt

ed

M an

us

cr

i

Figure

Figure 1

Page 27 of 52

i cr us

Xe Basic

M an

Ethanol Ether I

II

IV

ce pt

Trifluoro Water ethanol

Methanol

ed

Acetic acid Ethylene glycol Benzylic alcohol

Nitrobenzene

Ac

VIII

III

Formamide

VIa VIb

Chloroform

Xd Acidic Figure 2

CCl4 V CH2Cl2

VII

THF Di-methyl formamide pyridine Ethyl acetate Dioxane Acetone Acetonitrile Benzonitrile Chloroethane Toluene

Nitromethane

Xn Dipolar Page 28 of 52

i cr

b

Ethylene Dimethyl acetamide basic glycol THF

Ethanol

us

M an

I

Ether

II

Dioxane

Ac VIII

Figure 3

ed

Acetonitrile

Nitrobenzene

Glycerol

Nitromethane V

p* Dipolar

Water Chloroforme

Trifluoro ethanol

VIb VII

Glycerol J. Acetic acid

Acidic

Acetone

VIa

Limonene

ce pt

a

DMSO

III

p-Cymene

Formamide

Dimethyl formamide

IV

Hexane

Methanol

Ethyl acetate

CH2Cl2

Page 29 of 52

i Water

M an

us

hydrogen bond

cr

dh

Methanol

Glycerol Ethylene glycol

Formamide

ce pt

ed

VIII

MTBE

IV

Ethyl acetate VIa

THF

Dioxane

Ac

Dimethyl formamide

II

Propylene glycol Ethanol Acetic acid Acetone Benzylic alcohol

CO Chloroforme Pyridine I III 2 VII CH2Cl2 DMSO Acetonitrile Benzene V dp VIb Toluene CCl4 Polar C60 dd Benzonitrile Nitrobenzene Hexane Nitromethane Dispersive

Figure 4

Page 30 of 52

i cr

dh

M an

us

hydrogen bond

CO2/MeOH 90/10

ed

Ethyl lactate

Ac

ce pt

glycerol trimethylether Polym. Linseed oil

Ethanol Solketal iso-propanol n-Butanol

dp

Dimethyl Glycerol isosorbide carbonate CO2

Polar Propylene carbonate

Figure 5

Glycerol a-terpineol triacetate Cholesterol TG Tocotrienol

FA

Hexane

Glycerol limonene triet/trilutyl p-cymene ether CH2Cl2 a-pinene

Pine resin Tocopherols Sterols

dd Dispersive Page 31 of 52

cr

i Water(17/32)

us

dh

M an

hydrogen bond

Methanol(371)

Acetic acid (83)

ce pt

ed

Ethylene glycol

Dimethyl formamide(1012)

Ac

Paracétamol

dp

DMSO(1132) Acetonitrile(32)

Polar

Figure 6

Ethanol(232/242) Butanol(132)/propanol(93) Acetone(111/140) THF(155)

Ethyl acetate(10/32) Dioxane(17) CH2Cl2(0,3) Toluene(0,3)

CCl4(0,9/22) d d Dispersive

Page 32 of 52

i cr us

e hexane heptane CCl4 -1,500

cyclohexane

benzene

toluene

M an

s

l

ed

CH2Cl2

b

1-Octanol

1-butanol

acetonitrile 1,4 dioxane acetone

THF

di-ethylether

Ac

Propylene carbonate

ethyl acetate MTBE

ce pt

chloroform

2-propanol ethanol methanol

2-butanol 1-propanol

diméthyl formamide

DMSO formamide

ethylene glycol

trifluoroethanol

water

a

Figure 7

-6,000

Page 33 of 52

i cr

E

us

nitrobenzene

tert-butylbenzene

a terpineol

benzene

toluene CCl4 cyclohexane

ed

limonene

M an

p-cymene a-pinene

heptane hexane

Pyridine chloroform CH2Cl2

Phenyl ethanol

THF

ce pt

V

Propylene carbonate

1-octanol

ethyl acetate

nitromethane acetonitrile

S

diméthyl formamide

propionitrile acetone

1-butanol

-1,500

di-propylether MTBE

Paracetamol

1,4 dioxane

di-methylether

Ac

benzonitrile

anisole p-xylene

ethanol

1-propanol 2-butanol 2-propanol methanol

di-ethylether

Glycerol

formamide

ethylene glycol acetic acid trifluoroethanol

1,3 propane diol

B

water

Figure 8 -1,000

A

Page 34 of 52

i cr us M an

P’

12

y = -9,7611x + 10,112 R² = 0,836

10

ed

8

4 2

Ac

0

ce pt

6

-2

Figure 9

y = -7,8912x + 9,5509 R² = 0,5794

0,0

0,2

0,4

V/U

0,6

0,8

1,0

1,2

1,4

Page 35 of 52

i cr c

0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

S

A

b

E

Figure 10

S

A

B

E

V

0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

Ac

1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

B

ce pt

E

ed

M an

us

a

1,0 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0,0

V

S

A

B

V

d

E

S

A

B

V Page 36 of 52

i cr

E

us

Di-ethylphthalate

nitrobenzene

p-cymene

toluene p-xylene

a-pinene

limonene

M an

tert-butylbenzene

benzonitrile

anisole

benzene

CCl4

Pyridine

chloroform CH2Cl2

ed

cyclohexane

V

Alcool Phenyl ethanol benzylique hexane 1,4 dioxane

a terpineol

ce pt

THF

heptane

acetone

diméthyl formamide

S

nitromethane

Acetonitrile propionitrile

di-methylether

ethyl acetate

formamide

di-ethylether

Ac

-1,500

di-propylether

MTBE 2-butanol 2-propanol

ethanol

methanol

water

B Figure 11

acetic acid

-1,000

A

Page 37 of 52

cr

i S

A

B

Ac

ce pt

E

ed

M an

1,0 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0,0

us

a

V

1,4 1,2 1,0 0,8 0,6 0,4 0,2 0,0

b

E

Figure 12

S

A

B

V Page 38 of 52

i cr

Sig2

Glycerol

us

DMSO

CSA 1-Octanol

Phenyl ethanol

a-terpineol

M an

Propylene carbonate

Benzylic alcohol benzonitrile

Sig3

ethylene glycol

1,4 dioxane

diméthyl formamide

ed

formamide 1-butanol nitrobenzene acetic acid ethyl acetate 1-propanol Pyridine anisole 2-propanol 2-but-ol acetone di-ethylether ethanol MTBE p-xylene acetonitrile

ce pt

-1,000

Ac

toluene di-propylether THF nitromethane benzene

heptane hexane

Hb acc3

CH 2Cl2 chloroform

water

methanol

di-methylether

Hb don3

CCl4 cyclohexane

Figure 13

-0,700 Page 39 of 52

i cr

IX. Organic acidic

M an

us

F3

X. Polar structured

ce pt

ed

I. Strong electron-pair donor bases

Ac

VI. Asymetric halogenated hydrocarbon

VII. Amphiprotic VIII. Polar protic

II. Weak electron-pair donor bases

F2

Electron pair donor V. non polar

Figure 14

III. aprotic dipolar IV. aprotic highly dipolar

F1 Page 40 of 52

Hydrogen donor

i

cr

F3

Methanol

Acetic acid

Ethylene glycol

1-Octanol 1-propanol 1-Butanol

ce pt

Propylene glycol

Ethanol

ed

Water

M an

us

Trifluoro ethanol

2-propanol 2-butanol Glycerol Formamide N-methyl CH2Cl2 formamide Cyclohexane Hexane Benzonitrile Pyridine CCl4 DiEt Dipr Heptane MTBE ether Nitromethane Ether THF benzene Propionitrile Etac Acetonitrile Toluene Nitrobenzene 1,4-Dioxane Acetone Propylene DMSO carbonate F2

Ac

Chloroform

Figure 15

F1 Page 41 of 52

ed

octanol n-butanol Isopropanol n-propanol ethanol Methanol

Xd 0,13 0,14 0,1 0 0,17 0,18 0,17 0,19 0,19 0,22

Xe 0,53 0,48 0,51 0 0,58 0,54 0,57 0,54 0,52 0,48

us

Xn 0,34 0,38 0,39 0,2 0,25 0,28 0,26 0,27 0,29 0,31

M an

Solvents diethyl ether dibuthylether di-isopropyl ether

cr

i

Tables

0,41 0,36 0,4 0,41

0,19 0,22 0,21 0,2

0,4 0,42 0,39 0,38

benzyl alcohol ethylene glycol acetic acid

0,31 0,28 0,3

0,29 0,29 0,3

0,4 0,43 0,41

CH2Cl2

0,4

0,33

0,27

CCl4 chloroform dichloethane

0,34 0,34 0,49

0,4 0,35 0,21

0,26 0,31 0,3

ethyl acetate dioxane acetone

0,42 0,4 0,42

0,22 0,23 0,23

0,36 0,37 0,35

Ac

ce pt

THF pyridine dimethylformamide N,N dimetacetamide

Page 42 of 52

i 0,43 0,45 0,45 0,43 0,42 0,4

ed

ce pt

formamide trifluoroethanol water

cr

nitrobenzene toluene benzene anisole benzylic ether nitromethane

0,3 0,27 0,25

Xd 0,26 0,25

Xe 0,32 0,33

0,29 0,27 0,28 0,29 0,28 0,31

0,29 0,28 0,27 0,27 0,3 0,28

0,33 0,33 0,37

0,36 0,4 0,37

us

Xn 0,41 0,42

M an

Solvents benzonitrile acetonitrile

Ac

Table 1. Data of solvent strenght selectivity (Snyder). Xn : dipole; Xd : acidity; Xe : basicity; P’ : Rochschneider polarity

Page 43 of 52

0,47 0,48

0,79 0,76

0,88 0,95

0,54 0,6

0,83 0,93

0,77 0,62

pyridine dimethylformamide THF DMSO

0,87 0,88 0,58 1

0 0 0 0

0,64 0,69 0,55 0,74

CH2Cl2 chloroethane chloroforme CCl4

0,82 0,81 0,58 0,28

0,2 0 0,44 0,00

0 0 0 0,00

ethyl acetate dioxane acetone

0,55 0,55 0,71

0 0 0,08

0,45 0,37 0,48

N,N dimetacetamide ethylene glycol

0,49 0,92

0 0,52

0,57 0,9

benzonitrile acetonitrile nitrobenzene

0,9 0,75 1,01

0 0,19 0

0,41 0,31 0,39

ed

Ac

a 0 0 0

i

cr

n-butanol isopropanol n-propanol ethanol methanol

M an

p* 0,27 0,24 0,27

us

b 0,47 0,46 0,49

ce pt

Solvents diethyl ether dibuthylether di-isopropyl ether

Page 44 of 52

i

b 0,11 0,1 0,22 0,41 0,25 0 0,13

1,51 1,17 1,12 0,71 0,62 0,94

0 0,47 0,45 0,44 0,51 0,51

us

cr

0,73 1,09 0,64 0,97 1,21 1

a 0 0 0 0 0,12 0 0

ce pt

ed

trifluoroethanol Water acetic acid formamide glycerol glycerol Jessop[40]

p* 0,55 0,59 0,73 0,8 0,85 0,16 0,39

M an

Solvents toluene benzene anisole benzylic ether nitromethane limonene[39]) p cymene[39]

Ac

Table 2. Data of solvatochromic solvent selectivity. p* : polarity-polarisability ;a : hydrogen-bond acidity; ;b: hydrogen-bond basicity. The Rochschneider polarity values are in table 1. For Limonene, p-cymene, hexane, and glycerol this value was arbitrary set at 5 for plotting the point.

Page 45 of 52

i dd

dp

14,5 17,4

2,9 3,7

n-butanol isobutanol n-propanol ethanol methanol MTBE THF pyridine dimethylformamide

16 15,1 16 15,8 15,1 14,8 16,8 19 17,4

cr

Solvents diethyl ether dibenzylether

dh

5,7 5,7 6,8 8,8 12,3 4,3 5,7 8,8 13,7

15,8 16 17,4 19,4 22,3 5 8 5,9 11,3

23,20 22,73 24,60 26,52 29,61 16,20 19,46 21,75 24,86

18,4 17 14,5 16,8

6,3 11 8 9,4

13,7 26 13,5 23,3

23,79 32,95 21,37 30,22

dichlorométhane CCl4 chloroforme dichloethane

18,2 17,8 17,8 16,6

6,3 0 3,1 8,2

6,1 0,6 5,7 0,4

20,20 17,81 18,95 18,52

ethyl acetate dioxane acetone

15,8 19 15,5

5,3 1,8 5,3

7,2 7,4 11,7

18,15 20,47 20,13

benzonitrile ACN

17,4 15,3

9 18

3,3 6,1

19,87 24,40

Ac

M an

ed

ce pt

benzylic alcohool ethylene glycol acetic acid propylene glycol

us

5,1 7,4

dt 15,64 19,27

Page 46 of 52

dd

dp

cr

i Solvents nitrobenzene toluene benzene anisole nitromethane hexane

20 18 18,4 17,8 15,8 15

8,6 1,4 0 4,1 18,8 0

4,1 2 2 6,8 5,1 0

dt 22,15 18,16 18,51 19,49 25,08 15,00

formamide water fullerene C60[41]

17,2 15,6 19,7

26,2 16 2,9

19 42,3 2,7

36,65 47,84 20,09

us

M an

Ac

ce pt

ed

dh

Table 3. Data of Hansen parameters for common solvents. dd: dispersion parameter; dp :polar parameter; dh:hydrogen bond parameter; dt: total parameter

Page 47 of 52

10,6

cr

i ce pt

Ac

CO2/MeOH 90/10 [47]

dh

1,8 1,3 2,3 4,7 0,9 1,8 0 3,1 1,6 6 16,4 6,3 3,8 18 7,6 25,5 7,1 11,3 7,2 5,7 4,5 4,8 3,8

4,3 2,2 2,4 2,2 2,8 3,2 3,5 5,7 4,5 7 10,2 5,7 10 4,1 12,5 17,4 7,5 27,2 19,3 7,5 9,1 3,6 3,2

5,3

12,4

M an

17,2 16,4 17,3 15,8 16,6 17,2 17,5 16,5 18,7 16 18,4 15,7 17 20 16 17,9 17,6 17,4 16 15,5 16,5 15,5 15,7

dp

us

dd

ed

Solvents limonene a-pinene p-cymene TG[43] tocopherol[43] sterols[43] a-tocotrienol[43] fatty Acid (FA)[43] pine resin [44] polym. Lindseed oil[44] DMSO [45] carbon dioxyde [45] a-terpineol[46] propylene carbonate[46] ethyl lactate[46] glycerol carbonate[46] dimethyl isosorbide[46] glycerol[45] solketal[46] glyceroyl trimethylether[46] glycerol triacetate[46] glycerol triethylether[46] glycerol tributether[46]

a

dt 17,82 16,60 17,62 16,63 16,86 17,59 17,85 17,73 19,30 18,47 26,68 17,85 20,09 27,22 21,68 35,69 20,41 34,21 26,08 18,14 19,37 16,62 16,47 11,85

Table 4. Data of Hansen parameters for « Green » solvents. a : calculated from data in ref. [47]

Page 48 of 52

benzene toluene tert-buthyl-benzene p-xylene

0,61 0,601 0,619 0,613

0,52 0,52 0,49 0,52

0 0 0 0

methanol ethanol 1-propanol 1-butanol 2-butanol 2-propanol

0,278 0,246 0,236 0,224 0,217 0,212

0,44 0,42 0,42 0,42 0,36 0,36

ed

ce pt

Ethylene glycol

B 0 0 0

V 0,954 1,095 0,845

U 0,954 1,095 0,904

0,14 0,14 0,18 0,16

0,716 0,857 1,28 0,998

1,084 1,177 1,515 1,291

0,43 0,37 0,37 0,37 0,33 0,33

0,47 0,48 0,48 0,48 0,56 0,56

0,308 0,449 0,59 0,731 0,731 0,59

0,878 0,898 0,973 1,062 1,065 0,972

i

A 0 0 0

cr

S 0 0 0,1

us

E 0 0 0,305

M an

Solvents hexane heptane cyclohexane

0,46

0,76

0,6

0,69

0,507

1,372

0,237 0,313 0,162 0,313 0,319 1,12

0,9 0,95 0,9 1,31 1,037 1,66

0,07 0,06 0,02 0 0 0,91

0,32 0,31 0,36 0,74 0,6 0,93

0,404 0,424 0,545 0,647 0,697 1,17

1,066 1,131 1,124 1,667 1,422 2,659

acetone ethyl acetate THF 1,4-dioxane

0,179 0,106 0,289 0,329

0,7 0,62 0,52 0,75

0,04 0 0 0

0,49 0,45 0,48 0,64

0,547 0,747 0,622 0,681

1,031 1,075 0,985 1,243

CH2Cl2

0,387

0,57

0,1

0,05

0,494

0,855

CHCl3

0,425

0,49

0,15

0,02

0,617

0,908

CCl4

0,458

0,38

0

0

0,739

0,949

Ac

ACN nitromethane propionitrile dimethyl-formamide propylene-carbonate paracetamol

Page 49 of 52

i 0,784

0,83

benzo-nitrile nitrobenzene

0,742 0,871

1,11 1,11

anisole pyridine

0,708 0,631

0,75 0,84

water acetic acid formamide

0 0,265 0,468

cr

phenyl-ethanol

B 0,45 0,58 0,41 0,59

V 0,731 1,01 0,45 0,872

U 0,895 1,176 0,666 1,074

0,3

0,66

1,057

1,717

0 0

0,33 0,28

0,871 0,891

1,628 1,692

0 0

0,29 0,52

0,916 0,675

1,41 1,353

0,45 0,65 1,3

0,82 0,61 0,62

0,35 0,44 0,6

0,167 0,465 0,265

1,013 1,129 1,65

0,488 0,446 0,553 0,607

0,28 0,14 0,61 0,49

0 0 0,2 0

0,45 0,12 0,7 0,19

1,323 1,257 1,42 1,28

1,506 1,346 1,796 1,511

glycerol 1,3 propane diol trifluoroethanol

0,512 0,397 0,015

0,76 0,89 0,6

0,47 0,77 0,57

1,43 0,87 0,25

0,707 0,649 0,502

1,899 1,649 1

diethyl-phthalate

0,729

1,4

0

0,88

1,711

2,489

ce pt

Ac

limonene a pinene a terpineol p-cymene

A 0 0 0 0

us

S 0,25 0,16 0,27 0,21

M an

E 0,041 0,006 0 0,024

ed

Solvents Diethyl-ether Propyl-ether Dimethylether MTBE

Table 5. Data for Abraham descriptors (from ref. 20,21, 61 -64) Page 50 of 52

cr

i Ac

us

Hb don3 0 0 0 0 0 2,14 1,99 1,69 1,98 1,411 1,4 0 0 1,211 0 0 0 4,482 2,014 0 0 0 0 1,75 0 3,83 0 3,85 0 3,62 0

M an

Sig3 0,43 0,69 0,399 -0,53 1,51 20,28 23,48 21,95 22,83 19,726 25,68 17,76 36,052 28,24 -15,81 30,64 38,25 51,318 17,66 6,164 -21,19 8,58 29,65 10,34 11,075 -3,29 -2,95 13,14 58,94 16,09 2,13

ed

sig2 7,92 8,92 5,71 28,26 28,58 53,58 53,53 52,02 57,13 48,4627 51,42 50,057 47,91 45,04 32,85 31,43 35,789 59,114 73,8 52,471 26,79 44,13 46,833 66,66 51,248 71,99 9,91 75,299 61,74 88,38 55,794

ce pt

Solvents hexane heptane cyclohexane benzene toluene methanol ethanol 1-propanol 1-butanol 2-butanol 2-propanol ACN acetone ethyl acetate CH2CL2 diethyl ether THF dioxane phenyl ethanol nitrobenzene chloroforme anisole pyridine benzylic alcohol benzo nitrile acetic acid CCl4 water dimethyl formamide formamide nitromethane

HB acc3 0 0 0 0 0 4,22 4,47 3,92 4,27 3,5425 4,178 1,346 2,793 0 0 2,63 3,509 0 3,58 0,241 0 0,425 3,39 2,84 0,858 1,97 0 5,75 5,99 5,82 0,219

CSA 156,9 167,8 126,0 118,7 135,8 66,4 85,9 104,2 128,4 125,8 106,4 82,6 102,7 129,7 98,5 130,3 111,8 120,5 161,5 149,4 117,5 150,4 117,0 150,0 145,7 92,3 134,2 43,1 115,9 76,7 88,4

Page 51 of 52

i cr us Hb don3 2,837 0 0 1,752 9,74 0 1,09 0 2,591 1,987

M an

Sig3 28,07 31,52 30,25 10,341 83,528 33,35 20,599 26,87 38,481 22,83

ed

sig2 82,61 33,48 31,48 66,667 85,92 34,24 50,55 72,86 100,289 57,13

HB acc3 5,514 3 2,64 2,839 0,03 3,188 2,61 1,707 6,402 4,2717

CSA 97,6 163,7 89,9 150,0 111,7 139,6 187,4 124,2 121,6 197,0

Ac

ce pt

Solvents ethylene glycol propyl ether dimethylether benzy alcohol DMSO MTBE a terpineol propylene carbonate glycerol 1-octanol

Table 6. Data for COSMOment s(from ref. 33-35) Page 52 of 52