Molecular Simulation and Experimental Characterization of Ionic

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Molecular Simulation and Experimental Characterization of Ionic Liquid Based Co-Solvent Extraction Solvents Santosh R. P. Bandlamudi, Michael John Cooney, Georgianna L. Martin, and Kenneth M. Benjamin Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.6b03931 • Publication Date (Web): 08 Feb 2017 Downloaded from http://pubs.acs.org on February 14, 2017

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Industrial & Engineering Chemistry Research

Molecular Simulation and Experimental Characterization of Ionic Liquid Based Co-Solvent Extraction Solvents Santosh R. P. Bandlamudi1, Michael J. Cooney2, Georgianna L. Martin2,3 and Kenneth M. Benjamin1,4 1

South Dakota School of Mines and Technology, Department of Chemical and Biological Engineering, Rapid City, South Dakota, USA 57701 2 University of Hawaii at Manoa, Hawaii Natural Energy Institute, Honolulu, Hawaii, USA 96822 3 Current address: Hawaii Pacific University, Department of Mathematics, Honolulu, Hawaii USA 96813 4 Corresponding author: [email protected]; Phone: (+1) 605 394-2636; Fax: (+1) 605 394-1232

Abstract The use of ionic liquid/polar covalent molecule co-solvent mixtures is a significant new pathway to extract and separate oils and other high value components from algae and other biooil bearing biomass. In this work, light scattering and steady state fluorescence polarization techniques were utilized to characterize co-solvent behavior at the micro- and macro-scales, and their measurements correlated to lipid extraction yields.

In addition, molecular dynamics

simulations were employed to probe the fundamental interactions between ionic liquid and polar covalent molecules in solution.

Both experimental and computational (radial distribution

functions and clustering analysis) data confirmed the increased aggregation between IL cations and anions with increasing methanol concentration.

For the 1-ethyl-3-methyl imidazolium

[EMIM] methyl sulfate [MeSO4] - methanol system, this ion aggregation appears to correlate with experimentally observed decreases in maximum lipid extraction yields for methanol concentrations above 80 vol%. At these concentrations, the methanol appears to completely surround the aggregates thereby removing the effect of the IL and diminishing extraction yield. The successful simulation of experimental observations not only provides new knowledge on the

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relationship between molecular-level co-solvent structure and its impact upon the solubility of the extracted material, which in this case are lipids, but also the ability to improve future design of ionic liquid based co-solvent mixtures for a broad range of extractable materials.

1.0 Introduction Increased demand for energy, implementation of tougher environmental standards due to global warming, and diminishing natural resources have increased interest in alternative and renewable sources. Generally, oil bearing biomass sources, such as oil-seeds and microalgae, require an extraction step to efficiently remove the oil from their membranes, internal tissues, or inclusion bodies. Traditional extraction solvents can be effective, but require energy intensive complimentary processes (i.e. heat addition, sonication, lysing agents) along with secondary downstream separation steps (e.g., the evaporation of organic solvents such as hexane).1 Complicating this issue is the fact that lipid-derived fuels are not classified as high value products. As such, a major push over the past several years has been to decrease the costs of downstream separations of the lipid product and to increase the potential for co-extraction and recovery of additional value added products. Co-solvent mixtures comprised of an ionic liquid and polar covalent molecule (PCM, e.g. methanol) have been proposed to extract and separate lipids from oil bearing biomass (e.g. oil seeds, microalgae) at low temperature and atmospheric pressure.

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One key element to this

system is the rapid auto-partitioning of the extracted lipids to their own separate immiscible phase. More, as shown in in Figure 1, it was discovered that maximum extraction yields were realized only at intermediate compositions of the ionic liquid ([EMIM][MeSO4]) and the polar

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covalent molecule (methanol), with yields falling dramatically as the composition approached pure solutions of either the IL or methanol.3

Figure 1: Oil yield as a function of co-solvent composition. The x-axis begins with data obtained from experiments using pure methanol and ends with data obtained from experiments using pure ionic liquid ([EMIM][MeSO4], 98%, Sigma) present in the co-solvent extraction solution. Inset Key: Canola reflects data on canola oil seeds, Safflower reflects data from safflower oil seeds and Jatropha reflects data from Jatropha oil seeds.3

These surprising results suggested that either the (i) the extraction/separation process needed the chemical properties of both solvents and/or (ii) dynamic molecular interactions between the cosolvent molecules dramatically changed solvent properties as a function of composition. To date only a speculative understanding exists as to why both components are needed for maximum extraction efficiency, as well as the mechanism underlying the post extraction auto-partitioning behavior.

A molecule-level understanding of the roles of the IL cation, IL anion, and polar

covalent molecule, as well as the molecular-scale solvent environment they create during the extraction and auto-partitioning steps, would greatly improve our ability to design these cosolvent systems.

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Recent research has focused on understanding and characterizing the properties of pure ILs, their solubilities4,5 and cosolvent interactions with various organic solvents.6-17 It has been


reported that ion aggregation in pure ILs can be microheterogeneous18-20, with cation aggregation becoming more pronounced as alkyl chain length extends beyond eight carbon units. The presence of PCMs with ILs alters the local fluid structure of ILs. Several experimental6-13,17 and


simulation14-17 studies on imidazolium based ILs have reported on the aggregation behavior of ion pairs (cation-anion) in both pure ILs and cosolvent modified IL systems. All of these studies reported that ILs tend to aggregate at higher concentration of PCMs.

In spite of these

preliminary findings, there has been no attempt to connect IL/PCM structure and properties to any particular separation/extraction system. In this work, we address and describe the unique molecular scale solvent environments of


[EMIM][MeSO4]/PCM mixtures and their connection to lipid extraction from biomass through a combination of molecular simulation and experimentation. Results suggest a good ability to describe solvent molecule interactions and solvent properties on a molecular level and to correlate these findings to the prediction of macro-scale extraction of lipids and their separation from oil-bearing biomass.

2.0 Experimental Methods 2.1 Solvent System

In our work we have chosen the RTIL 1-ethyl-3-methyl imidazolium methyl sulfate [EMIM][MeSO4]. The cation [EMIM] acts as a fluorophore with a peak absorption at 340 nm and emission at 370 nm.

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2.2 Steady State Polarization

The steady state fluorescence polarization, Pss, was measured using an ISS PC1 spectrofluorometer (ISS, Champaign, IL) using methods established previously.21 Specifically, Pss was calculated as a function of the observed parallel (Ill) and perpendicular intensities (I⊥), as defined in Equation 1.

PSS =

I II − I ⊥ I II + I ⊥

(1)

For molecular interactions that completely retard the rotation of cation, Pss approaches 1.0. Where molecular interactions are completely absent, Pss approaches 0.0.

In application,

however, the limits range between 0.2 and 0.5.

2.2 Light Scattering

Light scattering (via photon counting) was measured using previously established methods.21

Specifically, co-solvent solutions of varying volume ratio of ionic liquid and

methanol were excited at 340nm and the light (i.e. photons) scattered at an angle of 90º were measured using a photon countering detector set at 340 nm (ISS PC1 Photon Counting spectrophotometer).

2.3 Density

Density measurements were made by weighting known aliquots of the ionic liquid/methanol mixture (as purchased from the manufacturer) onto high precision scales and noting the mass.

Each density measurement represents an average of three separate

measurements with a standard deviation (around the mean) of no more than 2%.

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3.0 Computational Methods 3.1 Molecular Models

This computational study primarily focuses on the pure ionic liquid [EMIM][MeSO4] and mixture structure and properties of [EMIM][MeSO4] with various polar covalent molecules (PCM): methanol, acetic acid, acetone, chloroform, DMSO, and 1-propanol. The molecular structures of [EMIM] and [MeSO4] are shown in Figure 2. Prior to this study, there have been no reported investigations of the thermodynamic and structural properties of these IL-PCM cosolvent mixtures. The simulation results are compared directly to experimental data and other published simulation data, where possible.

a.

b.

Figure 2: Molecular models for ionic liquid ions: a. [EMIM] and b. [MeSO4]. The labeled carbons (C2, C4, and C5) on the EMIM cation correspond to the force field developed by Kelkar and Maginn.23

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All of the molecular components (ILs, PCMs) involved in this study obey force fields (CHARMM, OPLS, GROMACS, etc.) of a similar functional form. Each of these molecular models includes energy terms for bond stretching, bond angle bending, dihedral angle interactions, and non-bonded interactions.

The non-bonded interactions are modeled with

Lennard-Jones n-6 type potentials for van der Waals forces, plus Coulomb’s law for electrostatic interactions. Equations (2) – (7) below summarize these force field terms and expressions: E (r N ) = E bond + E angle + E dihedral + E ab,nonbonded E bond =

∑ K (r − r ) r

(2)

2

(3)

2

(4)

eq

bonds

E angle =

∑ kθ (θ − θ ) eq

angles

= ∑ ∅ 1 + ∅ − = ∑ −

on a on b

E ab ,nonbonded =

 

∑ ∑  4ε i

i



 12   6  2   σ ij  −  σ ij   + q iq j e  ij      rij  rij   rij    

(5) (6)

(7)

For equation (7), ϵij is LJ well depth, rij is separation distance, σij is the distance at which interatomic particle potential is zero, and qi and qj are partial charges of atomic sites i and j, respectively. Cross-interaction parameters between species are computed from Lorentz-Berthelot combining rules.22 More details on the various force fields used are summarized below:

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[EMIM]: For this cation, the fully flexible, all atom model developed by Kelkar and Maginn

was used.23 The bond stretch and bond angle bending terms are controlled by standard harmonic potential (equations 3 and 4). The dihedral angle energy was controlled by a CHARMM dihedral potential (equation 5).24 The improper torsional contributions were modeled with a standard harmonic potential (equation 6).

[MeSO4]: For MeSO4 anion the force field parameters were taken directly from Lopes et al.25

The functional forms for bond stretch and bond angle bending are the same as for the EMIM cation, while the dihedral potential was controlled by standard OPLS potential (equation 8).26

E dihedral =

V1 V V V [1 + cos (φ )] + 22 [1 − cos (2φ )] + 23 [1 + cos (3φ )] + 24 [1 − cos (4 φ )] 2

(8)

It should be noted that the force fields chosen for [EMIM] cation and [MeSO4] anion are such that both the ion species are charge neutral when combined (net ion charges of equal value and opposite sign).

Methanol: The TraPPE-UA force field was used for methanol.10 In this case, the CH3 group was

treated as single pseudoatom. The bonds between the atoms were fixed (rigid) while the bond angle bending was controlled via the standard harmonic potential (equation 4). In addition, several different PCMs were investigated to determine their respective roles in the local solvent microstructure, and because they were shown to be promising alternative cosolvents for lipid extraction.2 The PCMs used and their corresponding force fields are shown in Table 1.26-28

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Polar Covalent Force Field Reference Molecule Acetic Acid OPLS 8 Acetone OPLS 8 Chloroform OPLS-UA 11 Dimethyl sulfoxide OPLS 8 Isopropyl alcohol OPLS 8 Table 1: Polar covalent molecules and relevant force fields for molecular simulation. We should note that both OPLS and TraPPE-UA utilize the same functional forms for intermolecular forces (Lennard-Jones 12-6 potential for van der Waals; Coulomb’s law for electrostatics), so there are no compatibility issues with mixing these force fields. Further, since TraPPE-UA methanol is a united atom model which has only three sites (lumped CH3 atom), there are no problems with 1-4 interaction scaling (present on OPLS molecules, but absent on TraPPE-UA methanol), which can be another major issue in mixing force fields.

3.2 Molecular Dynamics Simulations and Analysis

The molecular dynamics (MD) simulations were conducted with the LAMMPS software package developed by Sandia National Laboratories.29

The cutoff radius of nonbonded

interactions was set to 10 Angstroms and the long-range electrostatics were handled by the particle mesh Ewald summation method.30,31

All simulations employed periodic boundary

conditions. The simulations were carried out by equilibrating the system in the NPT (isothermalisobaric) ensemble followed by production runs in the NVT (canonical) ensemble. For all simulations, equilibration and production lasted for at least 106 femtoseconds each. Nose-

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Hoover thermostats and barostats, each with dampening factors of 5 femtoseconds, were used to


maintain constant temperature and pressure in the various simulations.32-35 Within the same


molecule, intramolecular nonbonded energies for interaction sites which are separated by exactly three bonds (1-4 interactions) were scaled by 50%, in accordance with normal practice for OPLS type force fields. For pure components, simulations were carried out with 512 molecules and compared to that reported in the literature to ensure accuracy. Mixture simulations contained between 60-1600 polar covalent molecules and 48-124 ionic liquid molecule pairs. In the case of rigid molecules (such as TRAPPE methanol) the bonds were constrained by using the SHAKE algorithm in LAMMPS.36 Separate molecular simulations were conducted specifically to study the aggregation behavior of IL in co-solvent. These simulations were performed using the same force fields and environments as discussed in the sections above.

The simulation box consisted of 50

[EMIM][MeSO4] ion pairs and 450-250 methanol molecules depending on the composition of the system. The simulations were carried out by equilibrating the system in the NPT ensemble via parallel tempering at 400 K, and then re-equilibrating the system at 298 K for 8 ns, followed by production runs at 298 K for 14 ns.

Simulations were carried out a time step of 1

femtosecond and the trajectories were saved every picosecond. The clustering (aggregation) analysis performed on these simulations is described below. To provide a framework for assessing local fluid structure and potential aggregation behavior in solution, radial distribution functions were generated from the MD simulations. Radial distribution functions (rdfs; also known as pair correlation functions), often noted as g(r), are quantities determined by counting the number of atoms pairs within various volume shells around a central atom. In this manner, gij(r) gives the probability of finding an atom of type j a

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distance r from another central atom of type i. In the full formula development (not presented here; the interested reader is referred to the book by Allen and Tildesley37), this probability is computed relative to the ideal gas distribution of atoms in three-dimensional space, which has a g(r) value of unity. Therefore, increases in values of gij(r) indicate there is a greater probability

of finding atom type j near atom type i, and provide a convenient means of discerning increases (or decreases) in aggregation (or nearest neighboring atoms) in molecular systems. Rdf peaks (g(r) values) will be used frequently throughout this paper as the means for describing the extent of aggregation in various IL/PCM mixtures. As an alternative method for determining ion aggregation in solution, cluster formation was tracked during the course of individual MD simulations. A distance-based criterion only (no energetic criteria) was used for defining clusters, based on the distance corresponding to the height of the first peak in the computed radial distribution function. Ions separated by the prescribed distance or less are considered in the same “cluster”, while those separated by larger distances are considered “not clustered”. A K-means clustering algorithm was used to measure and track the cluster formation. Means clustering starts by selecting a set of seed points (in this case, molecules), each of which is assigned to its own cluster. One then iterates over all the other molecules, with each molecule being assigned to the cluster whose centroid is closest. After the assignment of molecules to a cluster, the centroid for the cluster is then recomputed.38 This is done for each new configuration generated within the MD simulation. To provide greater consistency between runs, we choose our collection of initial seed points randomly.

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4.0 Experimental Results and Discussions 4.1 Steady State Polarization

Figure 3 presents polarization data for the [EMIM][MeSO4] – methanol co-solvent system as a function of increasing methanol concentration (volume %, shown as decreasing IL


concentration in the Figure). The steady state polarization begins with an initial large level for pure [EMIM][MeSO4], a value that represents the baseline steady state polarization of the EMIM cation.

The steady state polarization then steadily decreases with methanol addition until

reaching a minimum before finally increasing as methanol concentrations approach and pass 80 vol% methanol. The initial decrease in Pss is attributed to a dilution of the IL by methanol which serves to diminish the strength of the charge interaction between the cation and the anion. This decreased charge interaction decreases the forces retarding the cation’s natural rotation around its own axis, and thus the value of the steady state polarization. As the methanol concentration passes 80 vol%, however, the Pss begins to increase despite the continued dilution with methanol. This increase is attributed to the formation of aggregates of the ionic liquid in the presence of high methanol concentration. Aggregates can increase the Pss in a number of ways. First, as the aggregates form the ions may pack together more tightly and the strength of the charge-charge interaction increases, thus increasing the degree to which the cation’s natural rate of rotation around its own axis is decreased. Second, as the number of aggregates increase, the greater the contribution to the overall average in the polarization measurement. This has the effect of increasing the measured Pss.

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Figure 3: Polarization curve for [EMIM][MeSO4] - methanol mixtures. The x-axis begins with the light scattering obtained from a solution of 90 parts ionic liquid and 10 parts methanol and concludes with light scattering from a solution of 10 parts ionic liquid and 90 parts methanol (on volume basis).

4.2 Light Scattering Figure 4 presents light scattering data for the [EMIM][MeSO4] – methanol co-solvent system as a function of increasing methanol concentration (shown as decreasing IL concentration in the Figure). The amount of light scattered by the co-solvent mixture increases as the volume percent of methanol increases relative to the ionic liquid, leveling off when the methanol volume percent reaches approximately 80 vol%. The increase in light scattered reflects the increasing presence of aggregates forming within the solution, supporting the conclusions from the Pss data that suggested an increase in IL aggregation with increasing methanol concentrations.

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Figure 4. Light scattering of [EMIM][MeSO4] - methanol mixtures. The x-axis begins with the light scattering obtained from a solution of 97 parts ionic liquid and 3 parts methanol and concludes with light scattering from a solution of 10 parts ionic liquid and 90 parts methanol (on volume basis).


5.0 Computational Results and Correlation to Experimental Data 5.1 Volumetric Behavior The structural and thermodynamic properties of the pure ionic liquid [EMIM][MeSO4] and its mixtures with methanol, acetic acid, acetone, chloroform, DMSO, and 1-propanol at 1 bar were investigated using the molecular dynamics (MD) simulation methods and force fields described above. Simulations were conducted both at room temperature (298 K) as well as at the experimental3 extraction temperatures of 338 K. From these MD simulations, we have computed mixture densities and generated radial distribution functions (rdfs) to show local fluid structure. As Figure 5 shows, the MD simulations were found capable of predicting the volumetric behavior of the [EMIM][MeSO4] - methanol mixtures. The simulation result at 298K and 1 bar gave an average density of 1.2126 g/cm3, a value within 2% (1.2366 g/cm3 at 298 K 98% [EMIM][MeSO4], Sigma Aldrich) of experimental measurement. Figure 5 also shows that the [EMIM][MeSO4] - methanol mixture did not deviate from essentially ideal mixtures

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(volumetrically; zero volume of mixing) over the entire composition range. The good agreement with volumetric property behavior implies that the MD simulations can accurately predict the structure of these IL-PCM mixtures.

Figure 5: Mixture densities for the [EMIM][MeSO4] - methanol co-solvent system. Open circles are MD simulation results and closed circles are experimental data.

5.2 Local Fluid Structure of Pure [EMIM][MeSO4] Given

the

agreement

between

computational

and

experimental

macroscopic

thermodynamic properties (e.g., density), MD simulations were further used to provide information regarding molecular-level structure, in particular, the ion aggregation behavior suggested by the light scattering and polarization measurements presented in Figures 3 and 4. This was accomplished first through the application of MD data to generate radial distribution functions between the cation and anion of [EMIM][MeSO4]. As mentioned previously, the radial distribution functions, g(r), best describe the local average number of atoms neighboring a particular atom.

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Radial distribution functions were generated between the three carbons within the ring of the imidazolium cation and the electronegative atoms of the anion (i.e., sulfur in MeSO4), as these atom sites represent the geometric areas of highest negative and positive charge between the ions. These results, presented in Figure 6, serve as a reference baseline point for the structural behavior and extent of IL ion aggregation in additional co-solvent systems. As shown in Figure 6, all the radial distribution function pairs show oscillations within the ideal gas limit at


long range, which strongly indicates the presence of long-range electrostatics in the system. As expected, the strongest interaction (largest g(r) value) occurs for the C2-S atom pair, which possesses the strongest attractive electrostatic force due the largest positive and negative point charges, respectively, within the model system. Integrations of the C2-S cation-anion radial distribution functions (Figure 6) yielded 6.33 cations and 6.5 anions for [EMIM][MeSO4] within the first solvent shell.

g(r)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 0

5

10

15

20

r (A)

Figure 6: Cation-anion rdf for pure [EMIM][MeSO4]. The blue, red, and green lines correspond to C2-S, C4-S, and C5-S atom pairs, respectively. At this point, we would like to acknowledge that the most complete picture of the local structure of [EMIM][MeSO4]/PCM mixtures would require rdfs not only for cation-anion, but for cation-cation, anion-anion, cation-PCM, anion-PCM, and PCM-PCM (especially as it may

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relate to self hydrogen-bonding between PCM molecules and the subsequent effect on ion pairing), too. However, for this work, we focus only on the cation-anion rdfs and interactions from simulation, as they are the only ones for which we can directly compare to our current experimental measurements (polarization and light scattering), as related to the target application of lipid extraction. The complete picture of the local structure and properties (volumetric and thermal; molar and partial molar) of these [EMIM][MeSO4]/PCM mixtures is the subject of another investigation.39

5.3 Local Fluid Structure - [EMIM][MeSO4] - Methanol Mixtures As described in the preceding sections, the degree of ion aggregation is directly related to the height of the cation-anion radial distribution function peaks generated. With this type of information, the degree to which the IL ion aggregation increases with increasing methanol cosolvent concentration can be explored. It has been reported that ILs tend to form aggregates in the presence of water40-45 as well as other polar solvents.6-17,44,46-52 IL aggregation can be of

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various types ranging from single ion pairs to multiple ion clusters to cation aggregation, the latter can be attributed to the length of the cation tail and is predominant in aqueous solutions. In our study, IL aggregation corresponds mainly to ion aggregation (ion pair cation-anion). As methanol is also highly polar, like water, similar aggregation is expected in the presence of methanol.

Figure 7 presents radial distribution functions for the C2-S atom pairs of

[EMIM][MeSO4] at various concentrations of methanol.

In the case of concentrated

[EMIM][MeSO4] solution mild aggregation is observed, as discussed previously in section 5.2. By contrast, a significant increase in aggregation is observed in the presence of high methanol concentration (90 vol%). Specifically, the first (neighbor) peaks for all ion atom pairs increased

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by approximately a factor of four, and the secondary neighbor peaks increased and showed more distinct structure than in the pure [EMIM][MeSO4] solution. These simulated increases in radial distribution peak heights correspond to increases in ion aggregation (more nearest neighbors) and are also in good qualitative agreement with the latter half (methanol concentrations of 50 vol% and above) of the polarization data obtained for [EMIM][MeSO4] - methanol mixture (Figure 3). The increased polarization for [EMIM][MeSO4] - methanol at high methanol concentrations observed in Figure 3 is believed to result from increased resistance to the rotation of the cation around its own axis due to stronger attractive electrostatic interactions. Experimentally, this would suggest a larger extent of anion-cation pairing, consistent with the trends in g(r) peak heights. 12 10 8 g(r)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


6 4 2 0 0

2

4

r (A)

6

8

10

Figure 7: Radial distribution functions for C2-S in [EMIM][MeSO4] - methanol mixtures. The red, green, and blue lines correspond to 10 v/v %, 50 v/v %, and 90 v/v % methanol, respectively.

The simulation data in Figure 7 can be also compared to the experimental light scattering data in Figure 4, which presents the amount of light (measured in photons) of light scattered at 90 degrees as a function of co-solvent composition. As the methanol concentration increased the amount of light scattered likewise increased until reaching a maximum at around 80 vol% methanol after which the amount of light scattered leveled off. From the radial distribution

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function plots in Figure 7, we observe that the peak heights grow in size as methanol concentration increases, predicting a continuous increase in [EMIM][MeSO4] ion aggregation with increased methanol concentrations - in good qualitative agreement with the light scattering data.

Figure 7 also shows how IL ion aggregation continues to increase as methanol

concentration increases above 80% by volume. This type of ion aggregation behavior has also been observed in several studies. Barra et al observed similar IL aggregation behavior at higher concentrations of methanol (xM ~0.8, where x represents mole fraction of the species) for the IL/cosolvent system of trihexyl(tetradecyl)phosphonium chloride [P14,6,6,6][Cl] in methanol.12 Similar findings were reported by Gupta et al on the same IL cosolvent system.16

The

transitional mixture behavior over the entire composition range is defined as bulk IL at higher ILs concentrations to bulk cosolvent at higher cosolvent concentrations, with the IL ions effectively restricted in solvent space by their state of aggregation. (To put it another way, the individual IL ions are not homogeneously distributed throughout the main PCM solvent space.) Simulation studies of ethylammonium nitrate [EAN] and methanol mixtures depicted ion aggregation at xM ~0.7 with smaller aggregates, while beyond xM ~0.85 formation of large clusters were reported. This behavior is attributed to the formation of complex structures and hydrogen bonding. MD studies on 1-butyl-3-methylimidazolium tetrafluoroborate [BMIM][BF4] in methanol and ethanol showed that at higher concentration of methanol individual ion species are isolated, whereas ion pairing is observed in ethanol.14 Looking back upon the extraction yields presented in Figure 1, it can be seen that in the limit of high methanol concentration the extraction yields decrease rapidly. It is suggested by the MD simulations that the rapid drop off in extraction yields corresponds to extreme aggregation of the IL ions, wherein the [EMIM][MeSO4] ions are restricted to a very small volume in solution, effectively reducing the

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solvent (as it pertains to the biomass) properties to pure methanol. This extreme extent of [EMIM][MeSO4] ion aggregation and the realization of essentially “pure” methanol solvent behavior for [EMIM][MeSO4] – methanol mixtures with high methanol concentration is consistent with one other IL - methanol system reported in the literature.46 While the nature of the extreme ion aggregation at large methanol concentrations (as revealed by experiment and MD simulation) explains the decrease in measured lipid extraction yields at large methanol compositions (see Figure 1), it cannot fully explain, or does not correlate with the measured lipid extraction yields at smaller or intermediate methanol compositions. (This lack of correlation will be even further evident in the following subsection 5.4, which presents a more detailed IL ion clustering analysis as a function of methanol composition.) It can therefore be concluded that while IL ion aggregation is important for lipid extractions in [EMIM][MeSO4]/methanol co-solvent systems, it is not the sole, or dominant, driving force for the separation.

To more properly probe the fundamental interactions and mechanisms

controlling lipid solubilization and/or separation at small and intermediate methanol compositions, we are currently conducting MD simulations of lipids molecules within [EMIM][MeSO4]/PCM co-solvent systems, and will report those results soon.

5.4 Clustering Analysis in [EMIM][MeSO4] - Methanol Mixtures As mentioned previously, both experimental data (polarization and light scattering) and simulation rdfs indicate increased IL ion aggregation behavior as the concentration of the methanol increases.

To more directly probe this aggregation behavior computationally, a

clustering analysis was performed.

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During the post analysis of MD trajectories, a K-means clustering algorithm was utilized to study the size and number of clusters of ions (IL cations and anions) formed as the concentration of the co-solvent increased.53,54 Clusters are determined solely from a distance cutoff. In this analysis, the distance cutoff was between 3.15 and 4.8 A, which corresponds to the location of the first peak on the cation-anion rdf (Figure 6). The cluster analysis (Table 2) revealed that the majority of IL ion clusters are either ion pairs (cation-anion “dimers”) or “trimers” (cation-anion-cation or anion-cation-anion complexes) dispersed within the methanol co-solvent, as opposed to larger sized clusters (ion aggregates). In general, as more methanol is added to the co-solvent mixture, the average number of IL ions in a cluster decreases. This state of aggregation is shown visually in the MD snapshot (Figure 8). The clustering results are shown in Figure 9, where percent association is defined as the ratio of number of clusters of IL formed to total number of IL’s in the system. As the concentration of the methanol increased from 65% to 94%, the percent association of IL’s increased from 61% to 75%, and when the concentration of methanol was further increased beyond 94% the percent association of IL’s reduced to 70%. This behavior is consistent with the light scattering experimental data for the IL-methanol co-solvent system, as shown in Figure 4. The experimental data suggested an increase in the scattered light intensity as the concentration of co-solvent increased until leveling off at 80 vol% co-solvent and above. As the intensity of scattered light depends upon the size and/or number of the aggregates within the system, comparison against the K-means clustering simulation data suggests that it is the number of aggregates growing, and not the size of the IL ion aggregates, as the methanol concentration increases (mostly isolated IL pairs; see Table 2). It should be noted that the errors associated with the MD clustering data presented in Table 2 and Figure 9 are 2.6% or less (as represented by the error bars in Figure 9), so the simulation

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generated clustering trend as a function of methanol composition is statistically significant and meaningful. Mole % of MeOH 65 70 75 80 85 86 87 88 89 90 91 92 93 94 95 96 97 98

Number of clusters 30.6099 30.9688 31.9991 33.1801 34.5704 35.4410 35.5470 35.9959 36.5342 36.8186 37.0262 37.1781 37.2466 37.0139 37.6391 37.8291 36.3221 35.1795

Number of % ions in cluster Association 3.1285 0.6122 3.0974 0.6194 2.9813 0.6400 2.8503 0.6636 2.6937 0.6914 2.6283 0.7088 2.6193 0.7109 2.5688 0.7199 2.5178 0.7307 2.4780 0.7364 2.4498 0.7405 2.4062 0.7436 2.3730 0.7449 2.3412 0.7403 2.2816 0.7528 2.2153 0.7566 2.1955 0.7264 2.1479 0.7036

Table 2: K-means clustering analysis for [EMIM][MeSO4]/methanol systems.

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Figure 8: MD snapshot of IL ion aggregates in methanol co-solvent. In this snapshot, the methanol co-solvent atoms are not rendered, to allow better viewing of the IL ion aggregates.

80%

% Association

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75% 70% 65% 60% 55% 20

30

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50

60

70

80

90 100

v/v % of MeOH Figure 9: Cluster analysis from MD simulations at high methanol concentrations (showing agreement with light scattering data in Figure 4).

5.5 Results with Other PCM Solvents The work of Young et al. also looked at the effect of exchanging the identity of the polar

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covalent molecule on overall lipid extraction.2 As the work with [EMIM][MeSO4] clearly showed an aggregation of the anion/cation pairs at methanol concentration above 80 vol%, a feature that reduced the solvent mixture’s extraction efficacy, MD simulations were used to predict whether (or not) a similar aggregation would be observed at high concentrations of alternative polar covalent molecule (e.g., acetone, acetic acid, chloroform, DMSO, and 1propanol).

The results are presented in Figure 10.

In the presence of all polar covalent

molecules, the co-solvent appears to induce aggregation of the IL cation and anion relative to the behavior in pure [EMIM][MeSO4], as shown in Figure 6. However, it should also be noted that the extent of aggregation (as indicated by the height of the rdf first peak, which relates to the free energy of aggregation)55 varies among the different polar covalent molecules. Some polar

Formatted: Font color: Text 1 Formatted: Font color: Text 1

covalent molecules induce large extents of aggregation (chloroform and acetone), while for other polar covalent molecules aggregation is less pronounced (acetic acid). This suggests that the optimal co-solvent environment for lipid extraction will likely require polar covalent molecule compositions different than what was observed for the case of methanol.

20

Acetone Acetic Acid

15

Chloroform DMSO

g(r)

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Methanol

10

Propanol

5 0 0

2

4

6 8 10 r (A) Figure 10: Radial distribution functions between C2-S for various [EMIM][MeSO4] - co-solvent systems at 65°C and 10x co-solvent molar excess.

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Similarly, clustering results for [EMIM][MeSO4] with other PCMs at 10-fold molar

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excess of co-solvent and at 65oC (the experimental lipid extraction conditions of Young et al.2) showed similar aggregation behavior as [EMIM][MeSO4] in methanol at 25oC. The results are shown in Table 3. In the cases of acetic acid, methanol, and propanol, the individual ion species are seen mainly in pairs (cation-anion), whereas extended aggregation of IL ions (towards trimers and tetramers of ions) is seen in acetone, chloroform and DMSO, consistent with the rdf results shown in Figure 10. This comparison highlights the tradeoff in number of clusters formed versus the number of ions in a cluster, during ion aggregation.

Number of Number of ions in Clusters cluster Acetone 28.459 3.380 Acetic Acid 39.425 2.180 Chloroform 26.500 3.674 DMSO 32.767 2.799 Propanol 37.689 2.485 Methanol 37.007 2.457 Table 3: Clustering analysis for PCMs at 65oC and 10x co-solvent molar excess. PCM

6.0 Conclusions The local fluid structures for solvent mixtures of [EMIM][MeSO4] and polar covalent molecules methanol, acetic acid, chloroform, DMSO, or 1-propanol have been investigated using molecular dynamics simulations and the results for methanol were verified using light scattering and steady state polarization measurements.

Simulations were experimentally verified to

accurately predict the densities of [EMIM][MeSO4] - methanol and mixtures as a function of composition. The radial distribution functions generated from these simulations, as well as postsimulation K-means clustering analysis, predicted an increased ion aggregation in

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[EMIM][MeSO4] - methanol which was in agreement with experimental steady state polarization and light scattering data. The ion aggregation appeared to correlate with experimental lipid extraction yields, up to 80 vol% co-solvent concentration (in the case of methanol). At higher co-solvent concentration, the extent of ion aggregation is too strong, and likely limits the interaction of IL ions with lipids in solution, and thus retards the extraction yield.

For

[EMIM][MeSO4] in other co-solvents, ion aggregation was observed above 80 vol% of cosolvent, although the extent of aggregation varied for different co-solvents. This work suggests that a tailored design and/or implementation of these IL/PCM co-solvent systems towards targeted separations requires careful characterization of the micro-heterogeneous nature of the bulk co-solvent phase.

Acknowledgements S. R. P. Bandlamudi, M. J. Cooney and K. M. Benjamin would like to thank Tyndall AFB (AFRL Award IIP-0832549) and the National Science Foundation Industry/University Collaborative Research Center, the Center for BioEnergy Research and Development, for their financial support of this work. In addition, the authors would like to thank Dr. Kevin Hadley for the use of his K-means clustering code for processing MD simulation data.

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TOC Graphic 80%

% Ion Association

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Molecular Simulation and Experimental Characterization of Ionic

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