Computer-Aided Design of Ionic Liquids for High Cellulose Dissolution

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Computer-aided design of ionic liquids for high cellulose dissolution Ngoc Lan Mai, and Yoon-Mo Koo ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.5b00958 • Publication Date (Web): 30 Oct 2015 Downloaded from http://pubs.acs.org on November 3, 2015

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ACS Sustainable Chemistry & Engineering

Computer-aided design of ionic liquids for high cellulose dissolution

Ngoc Lan Mai a,b and Yoon-Mo Koo b,*

a

Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam b

Department of Biological Engineering, Inha University, Incheon 402-751, Korea

*Corresponding author Prof. Yoon-Mo Koo Department of Biological Engineering, Inha University 100 Inharo, Nam-gu, Incheon 402-751, Republic of Korea Email: [email protected] Tel.: +82-32-860-7513 Fax: +82-32-872-4046

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Abstract Ionic liquids (ILs), molten salts that are liquid at room temperature, are considered as a potential green replacement for toxic volatile organic compounds in many applications. These liquids exhibit unique properties such as negligible vapor pressure, non-flammability, wide liquid range, high thermal and chemical stabilities, and high solvating capacities for inorganic, organic, and polymeric compounds. In this article, computer-aided molecular design of potential ILs for cellulose dissolution was performed. A quantitative structure-activity relationship (QSAR) model was first developed to predict cellulose solubility in ILs using the group contribution (GC) and artificial neural network (ANN) methods. A mixed integer nonlinear programming (MINLP) problem was then formulated with an objective function that maximizes the QSAR model. The solution to the MINLP problem given by genetic algorithm (GA) corresponded to the optimal ILs structure for cellulose dissolution. For example, the cellulose solubility in ILs developed in the present work was at least 1.2 times experimentally higher than that of the best ILs reported for cellulose dissolution. In addition, the addition of cellulose compatible organic co-solvent such as DMSO could enhance the cellulose solubility up to 4 times as compared to that of conventional ILs/co-solvent system. Moreover, the similar degree of polymerization of regenerated and native celluloses indicated that the developed ILs in this study were non-derivatizing solvents for cellulose dissolution.

Keywords: Ionic liquids, cellulose, CAMD, QSAR, MINLP, genetic algorithm

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INTRODUCTION Cellulose is the most abundant bio-renewable organic material on Earth. Due to its nontoxic, biodegradable, durable and modifiable properties, cellulose and its derivatives are utilized for various applications in daily life (e.g. fiber, paper, tissues, etc.) and recently in the manufacture of various biofuel and platform chemicals via biological pathways 1. However, one of the most critical needs that cellulose processing technology must be addressed in order for (lingo)cellulose derived fiofuels and materials to become commercially viable is a costeffective and efficient biomass pretreatment technology 2. Dissolution of (lingo)cellulose in chemical solvents is considered not only as promising pretreatment technology but also benefit for the production of (lingo)cellulose-derived materials 3. However, strong inter- and intra-molecular hydrogen bonding between the hydroxyl groups of cellulose fiber makes dissolution in water and conventional organic solvents difficult. Only a few solvents systems can effectively dissolve cellulose, including N,N-dimethylacetamide/lithium chloride mixture (DMAc/LiCl), N,N-dimethylformamide/dinitrogen tetroxide (DMF/N2O4) mixture, Nmethylmorpholine-N-oxide (NMMO), and dimethyl sulfoxide/tetrabutylammonium fluoride (DMSO/TBAF) mixture. However, these solvents are highly toxic, unstable, difficult to recover, and alter cellulose, thus limiting their application on an industrial scale 4. In this context, ionic liquids (ILs), which are organic molten salts with melting points below 100oC, are considered as potential sustainable alternative solvents for cellulose processing 5. Dissolution and functionalization of cellulose in ILs have garnered much attention during the last decade due to the unique and advantageous properties of ILs, including negligible vapor pressure, non-flammability, wide liquid range, high thermal and chemical stabilities, and the ability to dissolve a wide range of inorganic, organic, and polymeric materials 6. Various cellulose-dissolving ILs have been synthesized, and most 3



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consist of an imidazolium, pyridinium, ammonium, phosphonium, or morpholinium-based cation and halide (chloride, fluoride), carboxylate (formate, acetate) alkylphosphate-based anion. In general, both cations and anions in ILs influence dissolution of cellulose. Although the role of anions in ILs is known and cellulose solubility corresponds well with anion basicity, the same cannot be said of cations 1. For instance, anions with good hydrogen bond acceptors (basicity) such as acetate, formate, and chloride are more effective in dissolving cellulose, and this parameter is used as an indicator for experimental screening of ILs for cellulose dissolution. However, the physical properties of ILs can be easily modified by altering the structure of the cation or anion 7. Many works have attempted to design and develop more efficient and low-cost ILs for (lingo)cellulose dissolution 8. It is estimated that the number of potential cation and anion combinations in ILs is approximately 1012-1018 9. Therefore, determination of optimum ILs for cellulose dissolution is a challenging task. Recently, Kahlen et al. previously employed conductor-like screening model for real solvent (COSMO-RS), a quantum chemical based calculation, to model cellulose solubility in ILs. Their approach allows qualitative prediction and selection/screening of ILs capable of dissolving cellulose

10

. To develop a model for quantitative prediction of cellulose solubility

in ILs as well as design/discover novel ILs for cellulose dissolution and functionalization, computer-aided molecular design (CAMD) was employed in this study. CAMD is a promising approach that has been widely applied to design organic materials for various applications. It involves property prediction models (quantitative structure-activity/property relationship, QSAR/QSPR) and optimization algorithms that reverse design molecular structures with predetermined specific properties

11

. CAMD has been used to design ILs

(referred as computer-aided ionic liquids design - CAILD) for use as entrainers for azeotropic separation, electrolytes, and heat transfer fluids 12. In these studies, ILs were considered as a 4


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combination of smaller functional, which were then used to develop property prediction models (group contribution method). A reverse IL design by exhaustive search for structures that consider target ILs properties

12a

or using an optimization algorithm such as the

deterministic 12b or stochastic 12c method was then employed to determine global optimal ILs. For example, Karunanithi et al.

12c

have employed CAILD to design several optimal ILs that

has high electrical conductivity (i.e. 1-methylimidazolium bis(trifluoromethylsulfonyl)imide, for use as electrolytes), high thermal conductivity (i.e. 1-ethyl-3-methylimidazolium tetrafluoroborate, for use as heat transfer fluids), etc. To the best of our knowledge, there has been no report of a reverse design of optimal ILs for cellulose dissolution. Therefore, this study investigated the use of CAILD for reverse design and development of optimal ILs solvents for cellulose dissolution. MATERIAL AND METHODS Materials It is well known that cellulose solubility in ILs depends on several factors regarding the substrate (e.g. cellulose crystallinity, degree of polymerization), solvent (e.g. water content and impurities of ILs), and dissolving conditions (e.g. temperature, conductive heating, microwave heating, etc.). Therefore, for the sake of simplicity, 86 data points regarding Avicel (microcrystalline cellulose with degree of polymerization of 225) solubility in ILs at different temperatures were collected and used as the data set in this study (Table 1). It is worth noted that cellulose solubility is considered as “practical” or “apparent” solubility but not true thermodynamic equilibrium solubility. This is because the dissolution of cellulose in ILs is limited by the viscosity of resulting solution and time required for dissolving process. All chemical reagents used for the synthesis of ILs were obtained from Sigma-Aldrich, 5



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were of analytical grade, and used without further purification. Methods Ionic liquid functional groups Generally, ILs are considered as an additive contribution of four functional groups: cation core, cation side chain, anion core, and anion side chain (Fig. 1). Forty-five functional groups were used to define 84 different ILs and develop predictive models (supporting information). Predictive model for cellulose solubility in ionic liquids Cellulose solubility in ILs is described by the engineering term, wt%, which may mask chemical trends within the data. In other words, this unit may not efficiently describe the effects of molecular structure, especially the role of each ion in the dissolution of cellulose 1. In our previous work, predictive models of cellulose solubility in ILs using CODESSA molecular descriptors based on molar solubility (g/mol) were shown to be more accurate and reliable than models based on mass percent (wt%) 13. However, inverse design of optimal ILs using models based on this quantum chemistry molecular descriptor is challenging. An approach utilizing a fragment-based molecular descriptor or functional groups may work by creating a novel structure that combines structural fragments in different ways

11

. The

functional groups of ILs can therefore be used to establish a predictive model for cellulose solubility in ILs. The multiple linear regression (MLR) method is usually employed to establish a predictive model. However; due to the limited amount of experimental data on cellulose solubility in ILs, an artificial neural network (ANN) approach was used to develop a predictive QSAR model. This approach avoids removal of variables in the MLR model due to data limitations and allows for interpretation of optimal ILs structure by CAMD. In this study, 6


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three-layered feed-forward networks with back-propagation training function were constructed in order to predict cellulose solubility in ILs using the Neural Network Toolbox implemented in the MATLAB 8.3.0.532 (R2014a) version. ANN topology contained 46 nodes in the input layer, 10 nodes in the hidden layer, and one node in the output layer. A symmetric sigmoidal transfer function and linear transfer function were employed in the hidden layer and output layer, respectively (Fig. 2). This multiplayer perceptron is the most common type of ANN topology that enables nonlinear estimation in the model 14. The models were constructed using the training set of compounds, and a validation subset was as an indication of model performance using Levenberg–Marquardt back propagation training algorithms and mean-squared error performance function. To avoid “overtraining” phenomena, obtained ANN models were first internally validated by the leave-many-out cross-validation technique and then externally validated. A data set containing 80 ILs was used to train the ANN, including 64 ILs for training, eight ILs for post-training analysis (internal validation), and eight ILs for testing (external validation). Moreover, a test set consisting of six ILs not used in ANN training was used to assess performance of the ANN. ANN models were statically evaluated by coefficient of determination (R2) and mean absolute error (MAE) N

1 MAE= |Pi -Xi | N

(Eq. 1)

i=1

where N, Pi, and Xi are the data set, predicted value, and experimental value, respectively. Mathematical framework The general CAMD to design ILs for cellulose dissolution can be formulated as a mixed 7



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integer nonlinear programming (MINLP) problem where the objective function is optimized subject to constraints (structural constraints, properties constraints, etc.): fobj = max f(X,Y)

(Eq. 2)

Subject to: -

(i) Structural constraints

-

(ii) Operating constraints

The objective function (Eq. 2) in the present study maximizes cellulose solubility in ILs encoded in the predictive model, where X is a vector of integer variables representing the number of corresponding functional groups in ILs and Y is a vector of continuous variables related to the dissolution conditions (in this case, dissolution temperature). The structural constraints for ILs presented in this study include (i) charge rules (i.e. number of cations and anions and their charges), (ii) octet rule, which ensures that the molecule has zero valency, and (iii) constraints on lower and upper limits of numbers of groups of a particular type and total numbers of groups making up a molecule. In this study, structural constraints were defined following the method described by Karunanithi et al.12c. In addition, cellulose dissolution was evaluated at 100oC. Therefore, the variable corresponding to dissolution temperature was set to 100oC as an operating constraint. The solubility of cellulose, in general, is favorable in low viscous and low melting point ILs. In other words, there are intrinsic trade-off relationships between cellulose solubility and viscosity as well as melting point of ILs. Therefore, these two physical properties were not considered in the design framework. Genetic algorithm (GA) was employed to search for the optimal solution to the MINLP problem. GA is a heuristic solution-search technique used to solve optimization problems 8


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based on the natural selection process that mimics biological selection and evolution

15

.

Unlike conventional problem-solving techniques, GA converges upon the optimal solution from multiple directions. The general idea behind GA is the evolutionary creation of a new population of entities from an earlier generation through crossover and mutation processes as well as by passing of genes from the fittest offspring to the next generation 16. Although it is recognized that optimal solutions do exist for most engineering problems, it may be difficult to derive an optimal solution at the conceptual design stage. Therefore, a near-optimal solution is probably the best possible result that can be expected 16-17. Therefore, near-optimal solutions can be applied to solve problems that are not well suited for standard optimization algorithms, including problems in which the objective function is discontinuous, nondifferentiable, stochastic, or highly nonlinear

18

. In this study, GA was implemented in the

MATLAB (R2014a) with most parameters at default values, except population size (Table 2). The fitness function for selection of parents for the next generation was the objective function (Eq. 2). The upper bound of variables was set to the highest value of corresponding variables in the data set. The solution to the MINLP problem solved by GA was a vector representing the occurrence of functional groups in the cation, anion core, and their side chains, which corresponded to the optimal structure of ILs. Synthesis of ionic liquids Methoxymethyltrimethylphosphonium methoxyacetate: Trimethylphosphine (10.0 g, 0.13 moles) was dried in a round-bottom flask with a side arm adaptor and septum under high vacuum conditions. The flash was purged with N2 and subsequently immersed into an oil bath (90oC). Chloromethyl methyl ether (10.6 g, 0.13 moles) was added via a syringe, and the mixture was stirred at 90oC for 24 hrs. The mixture was cooled to room temperature before drying overnight at 50oC under high vacuum conditions. Methoxyacetic acid (10 mL, 0.13 9



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moles) was added, and the mixture was stirred at room temperature for 12 hrs before the product was washed with diethyl ether and dried overnight under vacuum conditions at 50oC. Halide content was less than 100 ppm. 1H-NMR (400 MHz, Methanol-d4): δ 1.89-1.93 (9H, d) 3.38 (3 H, s), 3.57 (3 H, s), 3.82 (2 H, s), 4.24-4.25 (2 H, s, s). Allyltrimethylphosphonium methoxyacetate: Trimethylphosphine (10.0 g, 0.13 moles) was dried in a round-bottom flask with a side arm adaptor and septum under high vacuum conditions. The flask was purged with N2 and subsequently immersed into an oil bath (50oC). Ally chloride (10.1 g, 0.13 moles) was added via syringe, and the mixture was stirred at 50oC for 4 days. Methoxyacetic acid (10 mL, 0.13 moles) was added, and the mixture was stirred at room temperature for 12 hrs before the product was washed with diethyl ether and dried overnight under vacuum conditions at 50oC. Halide content was less than 100 ppm. 1H-NMR (400 MHz, Methanol-d4): δ 1.85 (9 H, d) 3.09-3.10 (2 H, d, d), 3.36 (3 H, s), 3.79 (2 H, s), 5.48 (2 H, d), 5.49 (1H, q). RESULTS AND DISCUSSION QSAR model for cellulose dissolution in ionic liquids A QSAR model for cellulose solubility in ILs was developed by using the group contribution and ANN methods. The parameters of this model are provided in the supporting information (Table S2). Statistical analysis showed that this model exhibits an adequate correlation between ILs structure and cellulose solubility (R2 = 0.90 for the training data set) as well as suitable predictive ability (R2 = 0.91 for the test data set) (Table 3). The higher MAE value of the training set (2.96) compared to that of the test set (2.25) can be explained by the larger amount of data in this set (80 vs. 6). Nevertheless, the ANN-based QSAR model for cellulose solubility using functional groups of ILs exhibited good correlation between the 10


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experimental result and prediction (Fig. 3). The present results indicate that the structural functional groups of ILs can be efficiently used to predict their activities and, in particular, their cellulose solvating abilities. Computer-aided ionic liquid design Optimal IL for cellulose dissolution obtained from MINLP (IL-1, Fig. 4) was predicted to dissolve 175.8 g/mol of Avicel, which is at least two-fold higher than that of the best ILs (i.e. [DEME][MEPA], 70.4 g/mol, Entry 48, Table 4). This optimal IL-1 consisted of a phosphonium-based cation and methoxyacetate (MTA) anion. Itoh et al. showed that MTAbased ILs exhibit the ability to dissolve cellulose (entry 51, Table 1)

19

. In addition,

incorporation of a methoxy group into the alkyl chains of phosphonium-based ILs results in ILs with lower viscosities than counterpart ammonium-based ILs 20. The cellulose-solvating ability of ILs mainly depends on the nature of the anion, as less viscous ILs are favorable for dissolution of cellulose

21

. These results can be attributed to the high cellulose-dissolving

capacity of IL-1, which consisted of MTA anion and a methoxy group in the side chain of the posphosnium cation. However, experimental synthesis of IL-1, particularly its cation, was challenging due to the lack of precursor chemicals. Its derivatives (IL-2 and IL-3, Fig. 4) were synthesized and evaluated for dissolution of cellulose. Both IL-2 and IL-3 were watersoluble and liquid at room temperature. The physical properties of these ILs were characterized and presented in the Table 4. According to the ANN-based QSAR model, 174.9 and 170.7 g/mol of Avical were estimated to be dissolved in IL-2 and IL-3, respectively, which showed similar solubilities as optimal IL-1. However, only 75.7 and 63.9 g/mol of cellulose were experimentally dissolved in IL-2 and IL-3, respectively, due to the high viscosity of the resulting cellulose/IL mixture. In term of mass percent solubility, this accounts for 36.0 and 31.0 wt% for IL-2 and IL-3, respectively. A high viscosity of 11



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cellulose/ILs mixture is considered as a drawback for dissolution of large amounts of cellulose since thermodynamic equilibrium is not reached 21. However, in contrast to the well agreement between experimental and predicted cellulose solubility in ILs collected from literatures (Table 1, Fig. 3), the disagreement between experimental and predicted cellulose solubility in IL-2, IL-3 and [Emim][OAc] (Table 4) suggested that a consistent experimental approach for measuring the cellulose solubility in ILs should be addressed. In addition, the lack of comprehensive data regarding the physical properties (e.g. melting point, viscosity) and purity (e.g. water and impurities content) of reported ILs might attribute to the difference of experimental cellulose solubility in ILs. Nevertheless, the experimental solubility of cellulose in these ILs were about 1.2 to 1.4 folds higher than that of [DEME][MEPA] which was considered as the best ILs reported for cellulose dissolution. It is well acknowledged that the addition of a co-solvent such as dimethyl sulfoxide (DMSO) to ILs would enhance its solvating power by decreasing the viscosity and time needed for dissolution

5d, 22

. For instance, in this study, mixtures of IL-2/DMSO and IL-

3/DMSO (1:1 v/v) could dissolve up to 74.0 wt% and 100 wt% of Avicel at 100oC, respectively (Table 4). In addition, the dissolved cellulose could be easily regenerated from IL solution by addition of anti-solvent (e.g. acetone and water mixture, 1:1 v/v). This allows not only the separation of dissolved cellulose but also the recovery of ILs from other solvents simply by distillation due to the negligible vapor pressure of ILs 6b. Moreover, the degree of polymerization (DP) of regenerated cellulose (determined as methods described in Ref.

5c

)

from IL-2 and IL-3 were 232 and 248, respectively. It was similar to the experimental DP of the original cellulose (245). This indicated that, these designed ILs in this study were nonderivatizing solvents for cellulose dissolution. CONCLUSIONS 12


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Computer-aided methodology was used to design ILs for dissolution of cellulose. The proposed method employs GA to solve the MINLP problem in which the objective function is to maximize cellulose solubility in ILs encoded in the QSAR model. The QSAR model was developed by using the group contribution and ANN methods. The developed QSAR model exhibited relatively high accuracy and reliability (R2 > 0.9). Cellulose solubilities in the optimal ILs and its derivatives were at least two times higher than those of existing ILs. The experimental synthesis and characterization of these novel ILs for cellulose dissolution were also investigated. Although there was disagreement between the predicted and experimental values for cellulose solubilities in synthesized ILs due to high viscosity of the resulting IL/cellulose solution, the proposed method would enable the discovery of new ILs structures tailored for specific applications. To the best of our knowledge, the ILs developed in the present work can dissolve highest amount of Avicel cellulose reported so far. Moreover, the addition of new functional groups that can span the entire spectrum of ILs and reliable experimental data can enhance the effectiveness of this method. This will be focus of the future works. ACKNOWLEDGEMENTS This work was supported by an INHA UNIVERSITY Research Grant. This research was also a part of the project titled ‘Manpower training program for ocean energy’, funded by the Ministry of Oceans and Fisheries, Korea.

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FIGURE CAPTIONS Fig. 1 Schematic illustration of ionic liquids by group contribution Different colors and shapes represent different functional groups of ionic liquids. Fig. 2 Architecture of three-layered feed-forward artificial neural network Fig. 3 Experimental and predicted cellulose solubility in ionic liquids Fig. 4 Structure of optimal ionic liquids for cellulose dissolution

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Table 1. Avicel solubility in ionic liquids Entry

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

Ionic liquids

Temp. (oC)

[DEME][Ala] [DEME][Arg] [DEME][Asn] [DEME][Asp] [Admim[Br] [C2mim][Br] [C3mim][Br]* [C4mim][Br] [C5mim][Br] [C6mim][Br] [C7mim][Br] [C8mim][Br] [DEMB][Butyrate] [DEME][Butyrate] [C2mim][Cl] [C2mim][Cl] [C3mim][Cl]* [C4mim][Cl] [C5mim][Cl] [C6mim][Cl] [C7mim][Cl] [C8mim][Cl] [C9mim][Cl]* [C10mim][Cl]* [C4dmim][Cl] [H(OEt)2-Mim][Cl] [Me(OEt)2-Et-IM][Cl] [DEME][Cys] [C4mim][DCA] [C2mim][DEP] [DEME][DMA] [C1mim][DMP] [C2mim][F] [C4mim][Formate] [TBA][Formate] [TBP][Formate] [DEME][Gln] [DEME][Glu] [DEME][Gly] [DEME][His] [DEME][ILe] [DEME][Leu] [DEME][Lys]

100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 90 100 110 100 100 100 100 100 100 90 110 110 100 110 100 100 100 100 110 110 110 100 100 100 100 100 100 100

Avicel sol. (g/mol)

Ref.

Exp.

Pred.

28.1 3.2 13.9 2.8 26.1 1.9 2.1 4.4 2.3 2.5 2.6 2.8 0 7.0 20.5 17.6 0.8 17.5 1.9 12.2 10.8 9.2 4.9 1.3 17 2.1 4.7 2.7 2.1 37.0 13.2 22.2 2.6 14.7 4.3 18.3 8.7 0 8.8 3.0 19.3 13.8 32.1

25.7 5.2 14.9 3.0 21.1 3.6 3.0

23

2.6 2.3 2.1 1.9 1.7 0.7 6.6 15.0

25

7.2 11.1 7.2 2.2 6.3 5.6 4.9 4.18 3.04 17.5 2.6 4.7 3.1 2.6 35.5 11.3 19.6 2.3 16.0 17.7 17.7 10.9 0.8 12.1 3.7 16.6 16.6 22.0

24

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25 25 25 25 19 19 25

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23 44 [DEME][MDEPA] 100 60.7 80.2 23 45 [DEME][MeO(H)PO2] 100 16.9 17.8 19 100 38.7 37.8 46 [C2mim][MEPA] 19 47 [DEMB]MEPA] 100 61.2 61.3 19 48 [DEME][MEPA] 100 70.4 71.3 23 49 [DEME][Met] 100 14.7 16.9 19 50 [DEMB][MTA] 100 18.7 20.2 19 51 [DEME][MTA] 100 58.8 59.2 19 52 [DEME][MTEPA] 100 61.0 89.2 27 53 [(MeOEt)2NH2][OAc] 110 1.0 10.9 25 54 [C2mim][OAc] 100 13.6 14.1 25 55 [C4mim][OAc] 100 23.8 12.9 27 110 2.5 5.5 56 [C8mim][OAc] 23 57 [DEME][OAc] 100 14.4 16.1 27 58 [H(OEt)2-Me-IM][OAc] 110 11.5 9.8 27 59 [H(OEt)3-Me-IM][OAc] 110 5.5 7.0 27 60 [Me(MeOEt)2NH][OAc] 110 1.0 0.5 27 61 [Me(OEt)2-Et3N][OAc] 110 26.3 27.7 27 62 [Me(OEt)2-Et-Im][OAc] 110 31.0 32.6 * 27 63 [Me(OEt)3-Bu-Im][OAc] 110 1.7 7.0 27 64 [Me(OEt)3-Et3N][OAc] 110 30.7 9.5 27 65 [Me(OEt)3-Et-Im][OAc] 110 36.3 33.8 27 66 [Me(OEt)3-MeOEtOMe-Im][OAc] 110 1.8 1.5 27 67 [Me(OEt)4-Et-Im][OAc] 110 34.6 33.3 27 68 [Me(OEt)7-Et-Im][OAc] 110 14.4 15.2 27 69 [Me(OPr)3-Et-Im][OAc] 110 1.7 1.5 27 70 [MM(EtOH)NH][OAc] 110 0.8 0.8 27 71 [MM(MeOEt)NH][OAc] 110 0.8 1.4 23 72 [DEME][OH] 100 8.2 9.5 23 73 [DEME][Orn] 100 22.2 25.2 23 74 [DEME][Phe] 100 15.5 16.6 23 75 [DEME][Pro] 100 2.6 2.9 19 76 [DEME][Propionate] 100 13.2 14.5 23 77 [DEME][Ser] 100 10.0 11.8 23 78 [DEME][Thr] 100 18.5 18.5 23 79 [DEME][Trp] 100 17.5 18.4 23 80 [DEME][Tyr] 100 16.3 17.3 23 81 [DEME][Val] 100 13.1 15.3 25 82 [Amim][Cl[ 100 34.9 35.1 27 83 [C4mim][DCA] 110 2.1 2.6 19 84 [P66614][DCA] 110 2.8 1.3 28 25 8.25 10.4 85 [C2mim][(MEO)HPO2] * 28 86 [C2mim][(MEO)HPO2] 45 20.62 16.4 *: data used for test set. DEME (N,N-diethyl-2-methoxy-N-methylethanaminium); DEMB (N,N-diethyl-N-methylbutan-1-aminium); OAc (acetate); DMA (dimethylalanine); DMP (dimethylphosphate); DCA (dicyanamide); MTA (methoxyacetate); MEPA (methoxyethoxypropionate); MDEPA (methoxydiethoxypropionate); MTEPA (methoxytriethoxypropionate).

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Table 2. Genetic algorithm parameters Parameter Population size Crossover fraction Mutation probability

Value 500 0.8 0.2

Table 3. Statistical analysis of ANN predictive model for cellulose solubility in ionic liquids Statistical parameters

Training data

Test data

R2

0.90

0.91

MAE

2.96

2.25

Table 4. Physical properties and cellulose dissolving ability of ionic liquids Ionic liquids

IL-1 IL-2 IL-3 [Emim][OAc] [DEME][MEPA] [DEME][MTA] a

Viscositya (mPa S) 102 145 162 -

Water content (ppm) 30 19 23 -

Avicel solubility (g/mol)b Prediction Experiment IL:DMSO (1:1 v/v) 175.8 (70 wt%) 174.9 (69 wt%) 75.7 (36 wt%) 74 wt% 170.7 (72 wt%) 63.9 (31 wt%) 100 wt% 13.6 (15 wt%) 37.4 (22 wt%)c 28 wt% d 71.3 70.4 (25 wt%) 59.2 58.8 (24 wt%)d

Viscosity and water content of ILs were determined at 25oC. b Avicel was gradually added (2

wt% ~ 20 mg) into 5 mL glass vial containing 1 gam of solvents. The mixture was incubated at 100oC in a VARIOMAG reaction block (H+P Labortechnik AG, Germany) with magnetic stirring at 500 rpm under nitrogen atmosphere condition. c [Emim][OAc] was obtained from Sigma-Aldrich. d data from Ref 19.

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Fig. 1

Fig. 2 Hidden layer W Input 46

Output layer W

+

+

b

b

1 1

10

21


Output


Fig. 3

100 Training data Test data

80

Pred. solubility (g/mol)

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60

40

20

0 0

20

40

60

Exp. solubility (g/mol)

22


80

100

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Fig. 4

H3C

O

CH3 P+

O

H3C

O O-

H3C

IL -1 H3C

O

CH3 P+

O O

H3C

CH3

O-

H3C

IL -2 H3C

O P+

O

H3C CH3

H3C

IL -3

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O-


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Supporting information Ionic liquids functional groups, bias and weight of the artificial neural network, and NMR spectra of ionic liquids

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For Table of Contents Used Only

Computer-aided design of ionic liquids for high cellulose dissolution

Ngoc Lan Mai and Yoon-Mo Koo

CAMD was applied to optimal design of ionic liquids for cellulose dissolution. The ILs were synthesized and showed better cellulose solubility than that of existing one.

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Computer-Aided Design of Ionic Liquids for High Cellulose Dissolution

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