Characterization of mesoporous aluminosilicate materials by means of inverse liquid chromatography

Estimation of the properties of mesoporous aluminosilicates in various environments is important when assessing their sorption capacity. Using inverse liquid chromatography (ILC), Hansen solubility parameters (HSP) and linear free energy relationship (LFER) parameters were calculated to determine the properties of aluminosilicates in a protic and an aprotic system.

Journal of Chromatography A 1610 (2020) 460544 Contents lists available at ScienceDirect Journal of Chromatography A journal homepage: www.elsevier.com/locate/chroma Characterization of mesoporous aluminosilicate materials by means of inverse liquid chromatography K Adamska∗, A Voelkel, M Sandomierski ´ , Poland Poznan University of Technology, Institute of Chemical Technology and Engineering, ul Berdychowo 4, 60-965 Poznan a r t i c l e i n f o Article history: Received 17 July 2019 Revised 11 September 2019 Accepted 14 September 2019 Available online 14 September 2019 Keywords: Mesoporous aluminosilicates Inverse liquid chromatography Surface characterization Hansen solubility parameters Linear free energy relationship a b s t r a c t Estimation of the properties of mesoporous aluminosilicates in various environments is important when assessing their sorption capacity Using inverse liquid chromatography (ILC), Hansen solubility parameters (HSP) and linear free energy relationship (LFER) parameters were calculated to determine the properties of aluminosilicates in a protic and an aprotic system, using water and acetonitrile as the mobile phase, respectively The calculated Hansen parameters, reflecting the ability of the material under investigation to different types of intermolecular interactions, slightly differ depending on the mobile phase used It was found that in the presence of water the surface of aluminosilicates shows a weaker ability to interact, as evidenced by negative or near-zero e, s, a, b, v coefficients Additionally, it was found that the Si/Al ratio in aluminosilicates structure has little effect on the determined parameters © 2019 The Authors Published by Elsevier B.V This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Introduction Applicability of many organic and inorganic solids is often determined by their surface sorption properties Biomaterials and chromatographic stationary phases should be without a doubt classified to this group of materials In 1992 the first information on new family of mesoporous materials which have an ordered structure was reported [1] These silica materials with hexagonal, cubic or linear arrangement of mesopores structure (M41S) were synthesized under hydrothermal conditions from silicate/aluminosilicate gels which contain organic surfactant molecules as templates [1,2] Mesoporous materials are popular due to their unique properties and application possibilities in many fields of science and technology Years of research dedicated to improving the properties of these ordered materials led to the new low-ordered mesostructures [3] One of these are MSU materials characterized by higher surface area and better thermal stability than M41S The abbreviation MSU refers to mesoporous silica, mesoporous alumina and mesoporous aluminosilicates [4,5] Initially mesoporous materials were synthesized from “zeolite seeds” of zeolites such as faujastic and templates (alkyl ammonium bromides) [6,7] Despite many studies on mesoporous materials there are few reports on their preparation from a solid silicon source Solid silicon sources provide ∗ Corresponding author E-mail address: Katarzyna.Adamska@put.poznan.pl (K Adamska) more silicon to the reaction environment than the most commonly used sodium silicates The new direction of the synthesis of mesoporous materials is their preparation from less toxic and less expensive substrates Application during synthesis other substrates, templates and various Si/Al ratio has a major impact on the properties (surface area, pore size and volume) of the resulting aluminosilicates what further influences the sorption properties of the material [8,9] The new procedure of manufacturing aluminosilicate materials of different Si/Al ratio was proposed [10] Authors suggested the use of Aerosil 200V, sodium aluminate, silicon dioxide as silicon/aluminium sources and hexadecyltrimethylammonium bromide as crystal template as substrates The surface characteristic and the results of sorption experiments for hydrocarbons on studied mesoporous materials allow to indicate the crucial surface parameters for adsorption process However, these materials were characterized as dry solids During separation procedures, e.g extraction process, the particles of the sorbent are surrounded by water, hydrocarbon solvent of water solution containing salt molecules It may significantly influence the surface properties of mesoporous species Therefore, it seems to be vital to estimate mesoporous materials sorption ability in real system i.e in environment where it usually “works” Biomaterial is surrounded by body fluid, while during the use of mesoporous materials as sorbents the process is carried out by using mobile phase in the form, e.g of dilute water solution of inorganic salts There is a number of techniques, which enable to carry out the characteristic of surface, including FTIR, Raman spectroscopy, https://doi.org/10.1016/j.chroma.2019.460544 0021-9673/© 2019 The Authors Published by Elsevier B.V This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) K Adamska, A Voelkel and M Sandomierski / Journal of Chromatography A 1610 (2020) 460544 X-ray diffraction, contact angle measurements and others [11–13] Unfortunately, the measurements methodology of all mentioned techniques not allow to observe the influence of environment, surrounding examined material on sorption properties Depending on physicochemical properties of liquid environment, the sorption ability of material will be changed A large number of other analytical methods can be used to study the sorption properties of nanomaterials in solutions (e.g liquid NMR, analytical ultracentrifugation, isothermal titration calorimetry [14–16] Inverse liquid chromatography (ILC) is a method of surface characterization, where an examined material is situated in chromatographic column It allows to examine the changes of surface properties in diversified environment The principle of measurements is based on the determination of the retention factors for the test solutes, having specified physicochemical properties These analytes are introduced into the chromatographic system separately The column is filled with the examined material, which in this case would be one of the mesoporous materials The test solutes are dissolved in specified solvent which is the same as applied mobile phase and then, every individual test compound is introduced to the chromatographic column Depending on the force of interactions between the test solute and the examined surface, the solutes leave the column with different retention times, which is the basis for physicochemical characterisation The retention parameter is a result of all occurring interactions in the system i.e solute – solvent, solute – stationary phase and solvent – stationary phase There are few procedures, involving inverse liquid chromatography usually used for physicochemical characterization of commercially available stationary phases e.g surface excess isotherms or surface energies, silanol activity and hydrophobicity or using an aromatic sulphonic acids as a test compounds [17–21] One of the most relevant and commonly applied mathematical model, that considers a retention parameter as a result of all interactions occurring in the chromatographic system is known as a linear free energy relationship [22] In the condensed phases strong attractive forces arises between molecules, expressed as molar cohesive energy It is defined as the molar internal energy and is related to the evaporation energy at a given temperature or internal pressure: Ecoh = U= H − RT (1) where: Ecoh – cohesive energy, U –evaporation energy, H – enthalpy of vaporization, R – gas constant, T – temperature For liquids, assuming that the intramolecular properties are the same in the gaseous and liquid state, the molar cohesive energy can be represented as the sum of two factors: (a) molar evaporation energy needed to convert a moll of liquid into saturated vapour, (b) the energy required to transfer saturated vapour to an infinite volume at a constant temperature, i.e the energy needed to completely separate the particles: −E = g U l + V =∞ V =Vpar ∂U ∂V dV (2) T The cohesive energy related to the molar volume is called the cohesion energy density, expressed as: c= −E V (3) The concept of the solubility parameter was proposed by Scatchard, Hildebrand to regular solutions, i.e solutions that not show an entropy effect upon mixing In practice, such type of solutions are rare The proposed solubility parameter referred to the systems in which cohesion resulted only from dispersive forces It is defined as the square root of cohesive energy density (CED) [23] √ δ= c= Ecoh = Vm H − RT Vm (4) where: δ - solubility parameter, R- gas constant, T- temperature, H - enthalpy of vaporization, Vm - molar volume In 1966, Hansen proposed the concept of a solubility parameter, referring to the systems in which aside from dispersion interactions, polar and hydrogen bonding interactions may exist The basic equation representing Hansen’s assumptions is: Ecoh = Ed + E p + Eh (5) where: d - dispersive, p - polar, h - hydrogen bonding The total cohesion energy includes the energetic contribution brought by dispersive (non-polar), polar and hydrogen bonding (specific) interactions Dividing the energy by the molar volume: Ep Ecoh E E = d + + h Vm Vm Vm Vm (6) a relationship, describing the total solubility parameter (Hildebrand solubility parameter), is obtained as the sum of the dispersive δ d , polar δ p and hydrogen bonding δ h components: δT2 = δd2 + δ 2p + δh2 (7) δ T is also called the corrected solubility parameter It is assumed that materials having similar values of Hansen’s parameters show high mutual affinity In the case of volatile substances the value of the solubility parameter can be determined using the enthalpy of evaporation from the Eq (4) [24,25] However, for more complex systems or nonvolatile materials, it was necessary to develop other procedures to determine the solubility parameter One of the methods is to observe the dissolution capacity of a compound (e.g a polymer) in solvents with known value of the Hildebrand solubility parameter [26] It is assumed that the solubility parameter of the tested material (dissolved substance) is approximately equal to the solubility parameter of the solvent, in which the test material dissolves or mixes with it in all proportions, without changing the enthalpy and volume A similar procedure is the measurement of polymer swelling e.g for cross-linked polymers, as well as semi-crystalline materials [27] Conducted observations of the studied systems enable to classify selected solvents for good, i.e those that show a stronger interaction with the tested material causing, for example, dissolution, swelling, suspension and the bad, in which no changes are observed In the case of dye/solvent systems, the analysis is made on the basis of determining the degree of suspension or sedimentation Such characteristics of the systems studied are based on relatively strong adsorption by some liquids compared to others Based on knowledge of the chemical structure of the compound, the solubility parameter can be calculated using the socalled additive methods They are based on the assumption that the total cohesion energy is the sum of energy contributed by each functional group of the compound molecule They enable the estimation of the total solubility parameter value and its individual components [28] Additive methods have found wide application [29–33] The calculated solubility parameter values for hydrocarbons and other compounds are acceptable, however, in many cases, e.g for large functional groups located around a central atom, the obtained data may be affected by a large error In molecules in which, for example, spatial effects or couplings may exist, it can be difficult to clearly determine the total cohesion energy It should be noted that in molecules in which there are several strongly interacting functional groups (e.g hydroxyl groups) additional intramolecular interactions, affecting the total cohesion energy of the molecule, may appear apart from intermolecular interaction [29] There are also other, indirect methods for determining the solubility parameter using, for example, molecular modeling Smidsrod and Guillet [34] were the first to apply the inverse gas chromatography technique in studies of the interaction between K Adamska, A Voelkel and M Sandomierski / Journal of Chromatography A 1610 (2020) 460544 the solvent (test compound) and the polymer as the stationary phase As a result of interaction between them the obtained retention data are used to calculate the solubility parameter δ from the Flory-Huggins interaction parameter χ1∞,2 The procedure proposed by Guillet has used Price [35,36] in his research to determine the solubility parameter for compounds with low molecular weights According to his assumption, the total solubility parameter resulted from the shares of two factors – dispersive δ d and polar δ p interactions Voelkel and Janas [37] have extended the group of test compounds to apply the three-parameter Hansen equation The solubility parameter found the application in the description of the properties of diluted solutions, especially non-polar There are a number of relations that combine the solubility parameter with other physicochemical quantities, e.g surface tension or thermal expansion coefficient, so the concept of solubility parameter can be used in many fields to interpret some phenomena including mixing, adsorption and dissolution processes [38–41] The Hansen solubility parameter has found wide application in the selection of a solvent or solvent system for a particular material in the industry coatings industry, cleaning agents or printing inks [39,42–44] Solubility parameters have been applied extensively in the pharmaceutical sciences The use of this parameter in the pharmaceutical industry has been described in detail by Hancock et al [45], showing how this factor is used to assess the properties of unknown materials, the impact of technological processes on the properties of materials, as well as estimation of interactions and incompatibilities between materials It can be used also to assess the bioavailability/solubility of various types of active substances [46–49] The concept of the Hansen Solubility Parameter (HSP) has been also used in the studies on the affinity between the adsorbent and organic solvent [50], a dispersion of carbon fillers and polymer matrix [51] or to determine the inter-molecular interactions in ionic liquid/solvent system [52] Karger et al [53] described various chromatographic processes using the concept of solubility parameter The authors used components of solubility parameters, responsible for different types of intermolecular interactions to describe retention in various types of chromatography: gas - solid, gas - liquid, liquid - solid and liquid - liquid The general description of the model was based on evaporation, dissolution, mixing and adsorption processes taking place in the chromatographic system These considerations gave the background of our investigations The energy of interactions between solid adsorbent (ad) and sorbate (test solute) (i) ( Elsc ) is given by the Eq (8) [53] One should take into account also interaction between the molecules of adsorbing test solute and molecules of mobile phase (j) EA Elsc = −n j/ad − ( E s )i/ j + EA i/ j + EA (8) i/ad where: j = mobile phase; ad – adsorbent; i = solute; EA – energy of adsorption, Es – solubilization energy The respective energetic contribution may be expressed in terms of components of solubility parameter [53]: ( E s )i/ j = V i δ j i − 2δdi δdj − 2δoi δoj − 2δin δinj − 2δai δbj − 2δaj δbi EA i/ad = Vi δdi δdad + δoi δoad + δinad δdi + δini δdad + δai δbad + δaad δbi (10) Elsc = E A Ai − i/ad Aj E A parameter corresponding to dispersive, orientation forces, inductive, proton donor ability and proton acceptor ability interactions, respectively Elsc = −RT lnVNi (12) VNi – net retention volume of the test solute “i” in ILC experiment It leads to Vi δdi δ add + δoi δoad + δinad δdi + δini δdad + δai δbad + δaad δbi − Ai i V Aj δdj δdad + δoj δoad + δinad δdj + δinj δdad + δaj δbad + δaad δbj = − RT lnVNi (13) and finally to −RT lnVNi = δdad Vi δdi + δini − δi δj Ai , Aj – molecular area of “i” and “j”; – solubility parameter of “i” and “j”; indices d, o, in, a, b – denote component of solubility δai − Ai j δ Aj a + δoad δoi − Ai j δ Aj o −RT lnVNi ad = δdadW + δoadW + δin W + δbadW + δaadW Vi (15) + δbad + δaad δbi − Eq (15) is polynomial where: δdi + δini − W1 = W2 = δoi − Ai j δ Aj o W3 = δdi − Ai j δ Aj d W4 = δai − Ai j δ Aj a W5 = δbi − Ai j δ Aj b Ai Aj δdj + δinj However, there is a lack of the respective data for components expressing the ability to inductive and orientation interactions Therefore, we have adapted the idea of Hansen solubility parameter to solve this problem For the system in liquid-solid chromatography (LSC) one obtains: Eqs (11) and (12) remain unchanged and δai + δbi = δhi (16) δaj + δbj = δhj (17) δai + δbi · δaj + δbj = δai δaj + δai δbj + δbi δaj + δbi δbj (18) but δai δa = and δbi δb = as the proton donor ability or proton acceptor ability of test solute and mobile phase molecules not influence the magnitude of their interactions This leads to j δhi · δhj = δai δbj + δbi δaj (11) j/ad δdj + δinj (14) δdi − Ai j δ Aj d Ai Aj Ai j δ Aj b ad + δin j (9) Vi i ad − δdi δ add + δ ip δ ad p + δh δh (19) Ai i V Aj j ad = −RT lnVNi δdj δ add + δ pj δ ad p + δh δh (20) K Adamska, A Voelkel and M Sandomierski / Journal of Chromatography A 1610 (2020) 460544 We have introduced the component corresponding to ability to polar interactions (δ ip ) instead of orientation and inductive forces – due to the unavailability of such data in the literature Vi δ add δdi − Ai j δ Aj d + δ ad p δ ip − Ai j δ Aj p + δhad δhi − Ai j δ Aj h = −RT lnVNi −RT lnVNi = δ add Vi (21) δdi − Ai j δ Aj d + δ ad p δ ip − Ai j δ Aj p + δhad δhi − Ai j δ Aj h (22) The values in brackets on the right side of Eq (22) are constant for the given adsorbent, the test solute and the mobile phase (molecular area of the molecule of mobile phase is required) The values of HSP for the adsorbent (examined material) are unknown and these values might be calculated by using multilinear regression A more detailed description of HSP determination is given in Section 2.4 The linear free energy relationship (LFER) was used, which involves five independent parameters characterising physiochemical properties of the examined surface [54] Since 1980’s the mathematical correlation of the retention parameters with physicochemistry of solute-sorbent interaction has attracted more attention Abraham and co-workers adapted Kamlet and Taft solvatochromic methods to chromatographic analysis, giving it the form of linear free energy relationship (LFER) [20] In this mathematical relationship, the retention parameter depends on solute solvation process, which has been identified and dissected into four types of solute-solvent interaction: cavity formation-dispersive interaction, dipolarity-polarizability interaction and acidity or basicity hydrogen bonding interaction In the case of liquid chromatography, one can observe three types of interactions: solute-stationary phase, solute-mobile phase and stationary phase-mobile phase All these interactions have a major influence on the observed retention parameter One of more widely accepted symbolic representations of LFER model in the form of multiple linear regression equation was presented by Abraham: log k = c + eE + sS + aA + bB + vV (23) where: log k is the logarithm of the solute retention factor, c is the linear regression coefficient The capital letters E, S, A, B and V corresponds to the solute descriptors, independent on the mobile/stationary phase used; E is the excess molar refraction, S – dipolarity/polarizability descriptors, A and B correspond to the solute hydrogen bond acidity and basicity respectively, and V is the McGowan volume of the solute The lowercase letters e, s, a, b, v are the system parameters reflecting the difference in solute interaction between the mobile and stationary phase Therefore, the value of the above-mentioned parameters might be useful for description of the physicochemical properties of material surface (in a given chromatographic conditions: mobile phase composition and temperature) and estimation of the surface ability to different types of intermolecular interactions The aim of the study was to introduce the new procedure for the estimation of Hansen Solubility Parameters Moreover the goal of this work was to estimate physicochemical properties of mesoporous materials surface in aquatic and non-aqueous systems To estimate those properties, we planned to use five descriptors (e, s, a, b, v) of linear free energy relationship adopted for liquid chromatography as well as HSPs data Experimental 2.1 Materials Mesoporous aluminosilicates with a different Si/Al ratio (Table 1) were prepared according to the following procedure (Table 2): NaOH (A) and NaAlO2 (B) were dissolved in the distilled water (C) Then, a silicon source: silicon dioxide (D) or Aerosil 200V (D) was added to NaOH (E) dissolved in the distilled water (F) Next silicon and aluminum mixtures were mixed in different ratios and stirred (700 rpm) for hour and then heated and stirred at 100 °C for 24 h Subsequently mixture was mixed with solution of hexadecyltrimethylammonium bromide (CTAB, 18 g in 572 ml of distilled water) After h stirring H2 SO4 was added to obtain pH = 9–10 and then mixture was stirred for 24 h The samples were crystallized for 48 h at 100 °C The resulting materials were filtered, washed with distilled water and dried The last step was removal of the template by calcination at 540 °C for h A detailed analysis of the materials is presented in the following publications [55–57] The real Si/Al ratio, given in Table 1, was determined from EDS results obtained using EDS Octane SDD detector made by EDAX The maximum size of agglomerated particles is 60 μm for AS4 material [55,56] In the case of others it does not exceed 20 μm 2.2 ILC experiments ILC experiments were conducted by using Dionex Ultimate 30 0 liquid chromatograph equipped with refractive detector (Shodex, Ltd USA) Empty stainless steel column (2.0 mm i.d × 100 mm) were used Columns were filled with materials in the dry state using a semi-automated column packer designed for packing of inverse gas chromatographic columns (Surface Measurements System Ltd London, UK) Column ends were sealed with stainless steel frit inserts After packing, the column were conditioned at the measuring temperature of 30 °C and with a mobile phase flow of 0.2 ml/min before the experiment started, in order to stabilize the pressure in the column and the base line of the detector The mobile phase used in experiments were acetonitrile for HPLC (Sigma-Aldrich) and distilled water All test compounds were dissolved in proper mobile phase at concentration 10 mg/ml The injection volume was 10 μl Test solutes were injected separately and due to this on ILC chromatograms single peaks were recorded Examples are presented in Fig Retention factor is given as: k= t tR − t0 = R t0 t0 (24) where: k is the retention factor, t R – the corrected retention times, t0 – the retention time of the non-retained substance Table Description of the examined materials Material abbreviation Silicon source Assumed Si/Al ratio Real Si/Al ratio AS1 AS2 AS4 M4 silicon dioxide silicon dioxide silicon dioxide Aerosil 200V 18:2 13:7 7:13 7:13 13.16 2.61 0.71 0.69 K Adamska, A Voelkel and M Sandomierski / Journal of Chromatography A 1610 (2020) 460544 Table Descriptors of the test solutes [59–62] Table The amount of substrates used during the synthesis [g] Material A B C D E F AS1 AS2 AS4 M4 0.9 3.15 5.85 5.85 13 13 16 56 104 104 18 13 7 9.4 6.79 3.66 3.66 40 29 15.5 15.5 Test solute Descriptor 1,3-diaminopropane 1,3-propanediol 1,4-dioxane 1-propanol Acetic acid Acetonitrile Acetophenone Aniline Benzonitrile Butanone Caffeine Cyclohexanone Cyclohexanol Diethyl ether Ethyl acetate Geraniol N,N-dimethylformamide Phenol Propylamine Pyridine Tetrahydrofuran E S A B V 0.446 0.397 0.329 0.236 0.265 0.237 0.818 0.955 0.742 0.166 1.500 0.403 0.460 0.041 0.106 0.513 0.367 0.805 0.225 0.631 0.289 0.610 0.910 0.750 0.420 0.640 0.900 1.010 0.960 1.110 0.700 1.600 0.860 0.540 0.250 0.620 0.630 1.310 0.890 0.350 0.840 0.520 0.430 0.770 0.000 0.370 0.620 0.070 0.000 0.260 0.000 0.000 0.000 0.000 0.320 0.000 0.000 0.390 0.000 0.600 0.160 0.000 0.000 1.140 0.850 0.640 0.480 0.44 1.739 0.480 0.500 0.330 0.510 1.330 0.560 0.570 0.450 0.450 0.660 0.740 0.300 0.610 0.452 0.480 0.731 0.649 0.681 0.590 0.4648 0.404 1.014 0.816 0.871 0.688 1.363 0.861 0.904 0.731 0.747 1.490 0.647 0.775 0.631 0.675 0.622 2.3 LFER calculations Each test compound was injected five times Q-Dixon Test was applied to reject of outliers The average value of retention time was used to calculate the log k The LFER coefficients were calculated according to Eq (23) using the test compound descriptors given on Table 2.4 HSP calculations Fig Caffeine chromatograms for AS2 material, mobile phase (a) water, (b) acetonitrile The retention time of the non-retained substance was calculated according to the equation: V0 = F · t0 (25) t0 = V0 /F (26) where: V0 – the void volume, F – the flow rate [ml/min] The void volume of the filled column was determined by the pycnometric method [58] Using such procedure four different solvents having different densities were used: acetonitrile, dioxane, heptane and dichloromethane (Sigma-Aldrich) The void volume was calculated from the following equation: V0 = (w1 − w2 )/(d1 − d2 ) (27) where: w1 , w2 are the mass of the column filled with solvent [g] with different densities d1 and d2 Group of selected test solutes with different chemical structure and properties i.a polarity, electron donor-acceptor were chosen for ILC experiments All test solutes were at least of analytical grade: aniline, butanone, diethyl ether, phenol, pyridine, propylamine (AVANTOR), benzonitrile, cyklohexanol, cyclohexanone, 1,3-diaminopropane, 1,4-dioxane, geraniol, caffeine, acetic acid, N,N-dimethyloformamide, ethyl acetate, propanol, 1,3-propanediol, tetrahydrofurane (Sigma-Aldrich), acetophenone (Fluka) HSPs parameters were found by solving Eq (22) One should collect the retention data for series of the test solutes The set of Eq (22) equal to the number of applied test solutes is obtained Molar volume of the test solute, molecular area of adsorbing test solute, molecular area of the molecule of mobile phase as well as HSPs data for test solute are collected in Table Molecular area of test solutes was calculated using procedure proposed by Diaz et al [63] assuming a spherical molecular shape in a hexagonal close-packing configuration [64]: A = 1.09 × 1014 M ρN 2/3 (28) where: M – molecular mass, ρ – density, N – Avogadro number Results and discussion The main purpose of the work was to estimate the physicochemical characteristic of mesoporous materials surface in aquatic and non-aqueous systems The experiments were carried out in two polar solvents of various chemical nature - protic water and aprotic acetonitrile The physicochemical characteristics were calculated using the values of retention coefficients calculated for a range of the test compounds Application of Hansen solubility parameters concept allowed the determination of the ability of a materials surface to different intermolecular interaction, whereas parameters calculated from Abraham model reflect the behaviour of the materials in different systems Differences in the properties of the examined, materials depending on the mobile phase used can be observed by comparing the data of retention factors, obtained for a series of test K Adamska, A Voelkel and M Sandomierski / Journal of Chromatography A 1610 (2020) 460544 Table Physicochemical data for HSPs calculation Physicochemical parameter∗ Test solute δd 1,3-diaminopropane 1,3-propanediol 1,4-dioxane 1-propanol Acetic acid Acetonitrile Acetophenone Aniline Benzonitrile Butanone Caffeine Cyclohexanone Cyclohexanol Diethyl ether Ethyl acetate Geraniol N,N-dimethylformamide Phenol Propylamine Pyridine Tetrahydrofuran Ibuprofen Water ∗ [MPa0.5 ] [MPa0.5 ] δp [MPa0.5 ] δh A [m2 ]∗ 10−19 V [cm3 mol−1 ] 13.9 16.8 17.5 16.0 14.5 15.3 18.8 20.1 18.8 16.0 19.5 17.8 17.4 14.5 15.8 16.3 17.4 18.5 16.0 19.0 16.8 16.6 15.6 12.9 13.5 1.8 6.8 8.0 18.0 9.0 5.8 12.0 9.0 10.1 8.4 4.1 2.9 5.3 4.1 13.7 5.9 4.9 6.5 5.7 6.9 16.0 14.7 23.2 9.0 17.4 13.5 6.1 4.0 11.2 3.3 5.1 13.0 5.1 13.5 5.1 7.2 11.3 11.3 14.9 8.6 5.9 8.0 10.0 42.3 7.27 5.16 7.33 5.61 3.28 2.73 13.6 8.35 10.6 8.04 25.0 10.7 10.9 10.8 9.56 30.2 6.00 7.73 6.71 6.50 6.59 39.7 10.60 74.1 72.5 85.7 75.1 57.6 52.9 99.2 91.6 103.2 90.2 157.9 104.2 105.7 104.8 98.6 173.5 77.0 88.9 83.0 80.9 81.9 199.0 18.0 HSPiP software Table Values of retention factor - k of test solutes for materials; mobile phase – acetonitrile and water Test solute 1,3-diaminopropane 1,3-propanediol 1,4-dioxane 1-propanol Acetic acid Acetonitrile Acetophenone Aniline Benzonitrile Butanone Caffeine Cyclohexanone Cyclohexanol Diethyl ether Ethyl acetate Geraniol N,N-dimethylformamide Phenol Propylamine Pyridine Tetrahydrofuran Acetonitrile k AS1 0.185 0.790 0.320 nd 0.357 nd 0.203 0.239 0.197 0.223 0.916 0.241 0.330 0.239 0.208 0.226 0.839 0.195 0.166 0.834 0.305 Water k AS2 ∗ nd 0.308 0.341 0.403 0.151 nd 0.189 0.348 0.144 0.263 0.771 0.165 0.618 0.217 0.159 0.666 0.389 0.510 0.489 nd 0.298 AS4 M4 AS1 AS2 AS4 M4 0.119 nd 0.212 nd 0.103 nd 0.118 0.209 0.117 0.139 1.152 0.137 0.189 0.130 0.124 0.820 0.863 0.181 0.110 0.104 0.174 0.136 0.119 0.171 nd 0.167 nd 0.135 0.227 0.130 0.140 0.965 0.154 0.306 0.141 0.142 0.818 0.753 0.476 0.128 0.450 0.174 nd 0.632 0.620 0.596 0.599 0.605 nd 0.626 nd 0.660 0.667 0.603 0.615 nd nd nd 0.655 0.656 nd 0.618 0.661 nd 0.195 0.256 0.241 0.222 0.211 nd 0.257 nd 0.200 0.350 0.255 0.200 nd nd nd 0.206 0.215 nd 0.255 0.215 nd 0.238 0.231 0.234 0.235 0.210 nd 0.242 nd 0.233 0.289 0.212 0.233 nd nd nd 0.239 nd nd 0.241 0.208 nd 0.014 0.072 0.011 0.166 0.013 nd 0.284 nd 0.115 0.310 0.063 0.255 nd nd nd 0.203 nd nd 0.113 0.112 (nd∗ ) – no data (no retention data obtained) solutes in water and acetonitrile (Table 5) with a standard deviation of 0.001–0.01 Considering water the retention factors values of the test compounds are very similar, what indicates lower selectivity of the system In the case of acetonitrile, more varied values of retention factors were obtained The most retained test solutes in acetonitrile was caffeine This may be due to the fact that caffeine has two functional groups - the tertiary amine and amide groups They can form hydrogen bonds with hydroxyl group of aluminosilicates just using the lone pair on the nitrogen In water such interactions between caffeine and the surface of aluminosilicates can be disturbed as a result of the formation of dimers or higher order aggregates by caffeine in aqueous media It can be observed that ability of the materials to a specific type of interaction, described using the Hansen parameters, slightly differ depending on the mobile phase used (Tables and 7) The materials in acetonitrile characterizes slightly higher values of HSP data Higher values for dispersive interactions are observed, whereas values for polar and hydrogen bonding are almost the same Material AS2 exhibits the highest values for polar, hydrogen bonding and total solubility parameter both in water and acetonitrile as mobile phase from all of the examined materials In water as mobile phase the ability to interactions is insignificantly lover as evidenced by the lower values of the HSP components, what can be caused by the formation of the hydration layer K Adamska, A Voelkel and M Sandomierski / Journal of Chromatography A 1610 (2020) 460544 Table Hansen solubility parameters for examined mesoporous materials (mobile phase – acetonitrile) δd Material [MPa0.5 ] AS1 AS2 AS4 M4 15.93 16.32 15.69 16.66 ± ± ± ± δp δh [MPa0.5 ] 0.12 0.13 0.11 0.12 13.19 13.68 12.25 12.65 ± ± ± ± 0.09 0.11 0.08 0.19 δT [MPa0.5 ] [MPa0.5 ] 12.50 ± 0.12 12.84 ± 0.12 12.33 ± 0.12 9.83 ± 0.12 24.16 24.87 23.41 23.12 ± ± ± ± 0.09 0.05 0.06 0.10 Table Hansen solubility parameters for examined mesoporous materials (mobile phase – water) δd Material [MPa0.5 ] AS1 AS2 AS4 M4 16.52 16.27 15.62 15.54 ± ± ± ± δp δh [MPa0.5 ] 0.13 0.12 0.11 0.18 12.23 13.09 12.23 12.18 ± ± ± ± 0.08 0.10 0.05 0.10 δT [MPa0.5 ] [MPa0.5 ] 10.02 ± 0.08 12.72 ± 0.09 12.10 ± 0.07 9.33 ± 0.10 23.52 24.45 23.23 21.84 ± ± ± ± 0.14 0.15 0.14 0.22 Table Abraham parameters for examined mesoporous materials (mobile phase – water) Material e s a b v AS1 AS2 AS4 M4 0.027 0.024 0.003 −0.405 0.037 −0.071 0.037 1.748 −0.039 −0.046 0.023 0.424 −0.014 0.0427 −0.055 −3.214 −0.031 0.008 −0.002 1.802 Table Abraham parameters for examined mesoporous materials (mobile phase – acetonitrile) Material e s a b v AS1 AS2 AS4 M4 −0.141 0.151 −0.464 0.104 0.530 −0.264 0.878 0.401 0.039 0.362 0.251 0.243 0.329 0.291 0.561 −0.146 −0.317 0.348 −0.078 −0.312 Conclusions Mesoporous materials were examined by means of inverse liquid chromatography Hansen solubility parameters and Abraham parameters were used to express the ability of mesoporous aluminosilicates to interact with different environment The relatively weak influence of the composition (Si/Al ratio) of these materials on the presented characteristics was found Much more important was the influence of the environment of material (the mobile phase used) The comparison of the values of both groups of estimated parameters showed that the presence of protic solvent decreases the activity of examined material The findings of this paper are important as they present the ability to characterize the material which may change their properties in changing surrounding Calculated parameters may be useful in assessing the suitability of mesoporous materials in sorption processes during the solidphase extraction process from various solutions Declaration of Competing Interest on the surface, blocking the active group and reducing the ability to interaction in the presence of water In water as mobile phase together with the decrease Si/Al ratio (materials AS1-AS4, M4), the decrease of δ d is observed According to ref [65] e, s, a, b, v parameters reflect the difference in interaction in solute/mobile phase and solute/stationary phase systems A positive values of the parameters indicate that given type of interaction, described by Abraham parameters, is more favorable for the stationary phase However, if the given type of interaction is more significant between solute and mobile phase, the values are negative Therefore, a positive values are taken to consideration to characterize the properties of the stationary phase (investigated material) Considering the data received for water, given in Table it can be concluded, that OH groups on the surface of materials can form hydrogen bonds with a protic solvent As a result, the aluminosilicate surface in these conditions has a limited ability to interact with the test solutes, as shown by close to zero or negative values of Abraham parameters For M4 the highest values for s, a and v parameter are observed This indicates that such surface is involved in dipole-dipol (s) interactions Additionally the highest value of a coefficient (a = 0.424) indicates higher basicity of the surface, which may affect stronger interaction with hydrogen-bond donor solutes A positive value of v term indicates, that the test solute will preferentially transfer from the mobile phase to the stationary phase Comparing the data obtained for acetonitrile (Table 9), they are higher than for water In addition, positive values of Abraham parameters are not close to zero, as in the case of water This denotes the greater ability of the surface for interaction with test solutes The positive value of a coefficient indicates higher basicity of aluminosilicates surface in the presence of acetonitrile, which should reflect stronger interactions with hydrogen-bond donor solutes For almost all materials its acidic properties (parameter b) are little higher than basic (parameter a) Based on this, the ability of the surface to interaction with basic solutes should be stronger The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper Acknowledgement This work was supported by the National Science Centre, Poland under research project no UMO-2015/17/B/ST8/02388 References [1] J.S Beck, J.C Vartuli, W.J Roth, M.E Leonowicz, C.T Kresge, K.D Schmitt, C.T.W Chu, D.H Olson, E.W Sheppard, A new family of mesoporous molecular sieves prepared with liquid crystal templates, J Am Chem Soc 114 (1992) 10834–10843, doi:10.1021/ja0 053a020 [2] C.T Kresge, M.E Leonowicz, W.J Roth, J.C Vartuli, J.S Beck, Ordered mesoporous molecular sieves synthesized by a liquid-crystal template mechanism, Nature 359 (1992) 710–712, doi:10.1038/359710a0 [3] C.-Y Chen, S.-Q Xiao, M.E Davis, Studies on ordered mesoporous materials III Comparison of MCM41 to mesoporous materials derived from kanemite, Microporous Mater (1995) 1–20, doi:10.1016/0927-6513(94)0 077-9 [4] E Prouzet, F Cot, C Boissière, P.J Kooyman, A Larbot, Nanometric hollow spheres made of MSU-X-type mesoporous silica, J Mater Chem 12 (2002) 1553–1556, doi:10.1039/b111236h [5] K.S Triantafyllidis, T.J Pinnavaia, A Iosifidis, P.J Pomonis, Specific surface area and I-point evidence for microporosity in nanostructured MSU-S aluminosilicates assembled from zeolite seeds, J Mater Chem 17 (2007) 3630–3638, doi:10.1039/B705233B ˙ nski, ´ [6] B Strzemiecka, M Kasperkowiak, M Łozy D Paukszta, A Voelkel, Examination of zeolites as fragrance carriers, Micropor Mesopor Mat 161 (2012) 106–114, doi:10.1016/j.micromeso.2012.05.024 [7] Y Liu, T.J Pinnavaia, Assembly of hydrothermally stable aluminosilicate foams and large-pore hexagonal mesostructures from zeolite seeds under strongly acidic conditions, Chem Mater 14 (2002) 3–5 https://doi.org/10.1021/ cm0109544 [8] Zhaia S-R, J-L Zhenga, D Wua, Y-H Suna, F Deng, CTAB-assisted fabrication of mesoporous composite consisting of wormlike aluminosilicate shell and ordered MSU-S core, J Solid State Chem 178 (2005) 85–92, doi:10.1016/j.jssc 2004.09.034 [9] S Wang, Ordered mesoporous materials for drug delivery, Micropor Mesopor Mat 117 (2009) 1–9, doi:10.1016/j.micromeso.2008.07.002 [10] K Poniedziałek, K Bielicka-Daszkiewicz, M Sandomierski, A Voelkel, New mesoporous aluminosilicates used for solid-phase extraction of hydrocarbons and fragrance compounds, Anal Lett 51 (2017) 2026–2038, doi:10.1080/ 0 032719.2017.1407329 8 K Adamska, A Voelkel and M Sandomierski / Journal of Chromatography A 1610 (2020) 460544 [11] E.M Harnett, J Alderman, T Wood, The surface energy of various biomaterials coated with adhesion molecules used in cell culture, Colloids Surf B 55 (2007) 90–97, doi:10.1016/j.colsurfb.2006.11.021 [12] H Tanaka, T Watanabe, M Chikazawa, K Kandori, T Ishikawa, Surface structure and properties of calcium hydroxyapatite modified by hexamethyldisilazane, J Colloid Interf Sci 206 (1998) 205–211, doi:10.1006/jcis.1998.5634 [13] B Cengiz, Y Gokce, N Yildiz, Z Aktas, A Calimli, Synthesis and characterization of hydroxyapatite nanoparticles, Colloids Surf A 322 (2008) 29–33, doi:10.1016/j.colsurfa.2008.02.011 [14] M Schönhoff, NMR studies of sorption and adsorption phenomena in colloidal systems, Curr Opin Colloid Interface Sci 18 (2013) 201–213, doi:10.1016/j cocis.2013.03.004 [15] A Bekdemir, F Stellacci, A centrifugation-based physicochemical characterization method for the interaction between proteins and nanoparticles, Nat Commun (2016) 1–8, doi:10.1038/ncomms13121 [16] G Baier, C Costa, A Zeller, D Baumann, C Sayer, P.H.H Araujo, V Mailänder, A Musyanovych, K Landfester, BSA adsorption on differently charged polystyrene nanoparticles using isothermal titration calorimetry and the influence on cellular uptake, Macromol Biosci 11 (2011) 628–638, doi:10.1002/ mabi.2010 0395 [17] P Jandera, S Bocian, M Molikova, B Buszewski, Characterization of the properties of stationary phases for liquid chromatography in aqueous mobile phases using aromatic sulphonic acids as the test compounds, J Chromatogr A 1216 (2009) 237–248, doi:10.1016/j.chroma.2008.11.055 [18] B Buszewski, S Bocian, A Felinger, Excess isotherms as a new way for characterization of the columns for reversed-phase liquid chromatography, J Chromatogr A 1191 (2008) 72–77, doi:10.1016/j.chroma.2007.11.074 [19] S Bocian, M Paca, B Buszewski, Characterization of new N,O–dialk phosphoramidate-bonded stationary phases for reversed-phase HPLC – retention and selectivity, Analyst 138 (2013) 5221–5229, doi:10.1039/C3AN00371J [20] P Vajda, G Guiochon, Surface excess isotherms of organic solvent mixture in a system made of liquid carbon dioxide and a silica gel surface, J Chromatogr A 1308 (2013) 139–143, doi:10.1016/j.chroma.2013.07.113 [21] I Bednar, E Berger, N.L Krajnc, J Vidic, A Podgornik, A Jungbauer, R Tscheliessing, Surface energies of monoliths by inverse liquid chromatography and contact angles, Langmuir 30 (2014) 5435–5440, doi:10.1021/la50 0809 [22] M.H Abraham, A Ibrahim, A.M Zissimos, Determination of sets of solutes descriptors from chromatographic measurements, J Chromatogr A 1037 (2004) 29–47, doi:10.1016/j.chroma.20 03.12.0 04 [23] J Hildebrand, R Scott, Solubility of Non-Electrolytes, third ed., Reinhold Publishing Corp, New York, 1949 [24] C.M Hansen, A Beerbower, Solubility parameter, in: A Standen (Ed.), Kirk-Othmer Encyclopedia of Chemical Technology, Interscience, New York, 1971 [25] J.H Hildebrand, J.M Prausnitz, R.L Scott, Regular and Related Solutions, Van Nostrand-Reinhold, New York, 1970 [26] V.N.S Pendyala, S.F Xavier, Prediction of a synergetics blend composition range based on polymer-solvent interactions, Polymer 38 (1997) 3565–3572, doi:10.1016/S0 032-3861(96)0 0920-2 [27] A.F.M Barton, CRC Handbook of solubility parameter and other cohesion parameters, CRC Press, Boca Raton, Florida, 20 0 [28] D.W van Krevelen, Properties of Polymers, Elsevier, Amsterdam, 1990 [29] C.V.S Subrahmanyam, K Prakas Ravi, P Rao Sundu, Estimation of the solubility parameter of trimethoprim by current methods, Pharm Acta Helv 71 (1996) 175–183, doi:10.1016/0 031-6865(96)0 015-5 [30] M Terada, R.H Marchessault, Determination of solubility parameters for poly(3-hydroxyalkanoates), Int J Biol Macrom 25 (1999) 207–215, doi:10 1016/0 031-6865(96)0 015-5 [31] P Sakellariou, R.C Rowe, E.F.T White, The solubility parameters of some cellulose derivatives and polyethylene glycols used in tablet film coating, Int J Pharm 31 (1986) 175–177, doi:10.1016/0378- 5173(86)90229- [32] L.G Martini, P Avontuur, A George, R.J Willson, P.J Crowley, Solubility parameter and oral absorption, Eur J Pharm Biopharm 48 (1999) 259–263, doi:10.1016/S0939-6411(99)0 055-7 [33] S Verheyen., P Augustijns, R Kinget, G van den Mooter, Determination of partial solubility parameters of five benzodiazepines in individual solvents, Int J Pharm 228 (2001) 199–207, doi:10.1016/S0378- 5173(01)00838- [34] O Smidsrod, J.E Guillet, Study of polymer-solute interactions by inverse gas chromatography, Macromolecules (1969) 272–277, doi:10.1021/ ma60 09a012 [35] J.E Guillet, Studies of polymers structure and interactions by automated inverse gas chromatography, Polym Math Sci Eng 58 (1988) 645 [36] G DiPaola-Baranyi, J.E Guillet, Estimation of polymer solubility parameters by gas chromatography, Macromolecules 11 (1978) 228–235, doi:10.1021/ ma60061a043 [37] A Voelkel, J Janas, Solubility parameters of broad and narrow distributed oxyethylates of fatty alcohols, J Chromatogr A 645 (1993) 141–151, doi:10 1016/0021- 9673(93)80628- L [38] D.J David, T.F Sincock, Estimation of miscibility of polymer blends using the solubility parameter concept, Polymer 33 (1992) 4505–4514, doi:10.1016/ 0032- 3861(92)90406- M [39] C Carr, The use of solubility parameters in the coatings industry, Polym Paint Col J 181 (1991) 112 [40] C.D Vaughan, Using solubility parameter in cosmetic formulation, J Soc Cosmet Chem 57 (1985) 319–333 [41] D Karst, Y Yang, Using the solubility parameter to explain disperse dye sorption on polylactide, J Appl Polym Sci 96 (2005) 416–422, doi:10.1002/app 21456 [42] C.M Hansen, Hansen solubility parameters, A User’s Handbook, CRC Press, Boca Raton, Florida, 20 0 [43] D Rasmussen, E Walmström, HSP-solubility parameter: a tool for development of new products – modeling of the solubility of binders in pure and used solvents, Surf Coat Int PT B-C (1994) 323–333 [44] C.M Hansen, 50 Years with solubility parameters – past and future, Progr Org Coat 51 (2004) 77–84, doi:10.1016/j.porgcoat.2004.05.004 [45] B.C Hancock, P York, R.C Rowe, The use of solubility parameters in pharmaceutical dosage form design, Int J Pharm 148 (1997) 1–21, doi:10.1016/ S0378- 5173(96)04828- [46] J Breithreutz, Prediction of intestinal drug absorption properties by three-dimensional solubility parameter, Pharm Res 15 (1998) 1370–1375 [47] B Hossin, K Rizi, S Murdan, Application of Hansen solubility parameters to predict drug–nail interactions, which can assist the design of nail medicines, Eur J Pharm Biopharm 102 (2016) 32–40, doi:10.1016/j.ejpb.2016.02.009 [48] A Ahmad, M Raish, K.M Alkharfy, I.A Alsarra, A Khan, A Ahad, B.L Jan, F Shakeel, Solubility, solubility parameters and solution thermodynamics of thymoquinone in different mono solvents, J Mol Liq 272 (2018) 912–918, doi:10.1016/j.molliq.2018.10.104 [49] E Mohammadian, M Barzegar-Jalali, E Rahimpour, Solubility prediction of lamotrigine in cosolvency systems using Abraham and Hansen solvation parameters, J Mol Liq 276 (2019) 675–679, doi:10.1016/j.molliq.2018.12.043 [50] N Fujiwara, H Yamamoto, Evaluation of adsorption of organic solvents to modified hydrophobic silica adsorbents based on Hansen solubility parameter, Sep Purif Technol 210 (2019) 907–912, doi:10.1016/j.seppur.2018.08.034 [51] W Zhu, J Ma, X Nan, P Osei Lartey, Y Yang, Study on dispersion of reduced graphene oxide on physical performance of Polyvinylidene fluoride composites by Hansen solubility parameters, Colloid Polym Sci 297 (2019) 213–224, doi:10.10 07/s0 0396- 018- 4456- y [52] T Ban, L Cai-Lian, Q Wang, Determination of the solubility parameter of allyl imidazolium-based ionic liquid using inverse gas chromatography and Hansen solubility parameter in practice, J Mol Liq 271 (2018) 265–273, doi:10.1016/j molliq.2018.08.095 [53] B.L Karger, L Snyder, C Eon, Expanded solubility parameter treatment for classification and use of chromatographic solvents and adsorbents, Anal Chem 50 (1978) 2126–2136, doi:10.1021/ac50036a044 [54] K Kadlec, K Adamska, Z Okulus, A Voelkel, Inverse liquid chromatography as a tool for characterisation of the surface layer of ceramic biomaterials, J Chromatogr A 1468 (2016) 116–125, doi:10.1016/j.chroma.2016.09.032 [55] K Poniedziałek, M Sandomierski, B Strzemiecka, K Bielicka-Daszkiewicz, A Voelkel, Synthesis, characterization, and possible application as sorbents of new low-cost aluminosilicate materials with different Si/Al ratios, Int J Mater Res 10.3139/146.111775 [56] B Strzemiecka, M Sandomierski, A Voelkel, Influence of change of Si/Al ratio on the synthesis of mesoporous aluminosilicates and flexural strength of novolac composites, J Inorg Organomet Polym (2019), doi:10.1007/ s10904- 019- 01076- ˙ nski, ´ [57] B Strzemiecka, M Kasperkowiak, M Łozy D Paukszta, A Voelkel, Examination of zeolites as fragrance carriers, Microm Mesopor Mat 161 (2012) 106– 114, doi:10.1016/j.micromeso.2012.05.024 [58] I Rustamov, T Farcas, F Ahmed, F Chan, R LoBrutto, H.M McNair, Y.V Kazakevich, Geometry of chemically modified silica, J Chromatogr A 913 (2001) 49–63 [59] L Sprunger, A Proctor, W.E Acree Jr., M.H Abraham, Characterization of the sorption of gaseous and organic solutes onto polydimethyl siloxane solidphase microextraction surfaces using the Abraham model, J Chromatgr A 1175 (2007) 162–173, doi:10.1016/j.chroma.2007.10.058 [60] L Sprunger, A Proctor, W.E Acree Jr., M.H Abraham, N Benjelloun-Dakhama, Correlation and prediction of partition coefficient between the gas phase and water, and the solvents dry methyl acetate, dry and wet ethyl acetate, and dry and wet butyl acetate, Fluid Phase Equilib 270 (2008) 30–44, doi:10.1016/j fluid.20 08.06.0 01 [61] L Sprunger, B.H Blake-Taylor, A Wairegi, W.E Acree Jr., M.H Abraham, Characterization of the retention behavior of organic and pharmaceutical drug molecules on an immobilized artificial membrane column with the Abraham model, J Chromatogr A 1160 (2007) 235–245, doi:10.1016/j.chroma.2007.05 051 [62] M.H Abraham, A Ibrahim, W.E Acree Jr., Partition of compounds from gas to water and from gas to physiological saline at 310 K: Linear free energy relationships, Fluid Phase Equilib 251 (2007) 93–109, doi:10.1016/j.fluid.2006.11 007 [63] E Diaz, S Ordonez, A Vega, J Coca, Adsorption characterisation of different volatile organic compounds over alumina, zeolites and activated carbon using inverse gas chromatography, J Chromatogr A 1049 (2005) 139–145, doi:10.1016/j.chroma.2004.07.061 [64] A van Asten, N van Veenendaal, S Koster, Surface characterization of industrial fibers with inverse gas chromatography, J Chromatogr A 888 (20 0) 175– 196, doi:10.1016/S0 021-9673(0 0)0 0487-8 [65] C.F Poole, S.K Poole, Column selectivity from the perspective of the solvation parameter model, J Chromatogr A 965 (2002) 263–299, doi:10.1016/ S0021-9673(01)01361-9  ... Conclusions Mesoporous materials were examined by means of inverse liquid chromatography Hansen solubility parameters and Abraham parameters were used to express the ability of mesoporous aluminosilicates... energies of monoliths by inverse liquid chromatography and contact angles, Langmuir 30 (2014) 5435–5440, doi:10.1021/la50 0809 [22] M.H Abraham, A Ibrahim, A.M Zissimos, Determination of sets of solutes... interactions by inverse gas chromatography, Macromolecules (1969) 272–277, doi:10.1021/ ma60 09a012 [35] J.E Guillet, Studies of polymers structure and interactions by automated inverse gas chromatography,