Hydrophobic Metal-Organic Frameworks for Separation of Biofuel/Water Mixtures Introduction Methods

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Hydrophobic Metal-Organic Frameworks for Separation of Biofuel/Water Mixtures Hongda Zhang and Randall Q. Snurr Department of Chemical & Biological Engineering, Northwestern University Introduction Biofuels are an attractive, sustainable transportation fuel, either on their own or blended with conventional fuels. As representatives of the first and second generations of biofuels, ethanol and 1-butanol (hereafter referred to as butanol), respectively, are already widely studied and used all over the world. 1 From a fuel perspective, butanol benefits from its higher heat of combustion and its lower volatility and polarity compared to ethanol, and thus our work mainly focused on butanol. 2 In industry, butanol is mainly produced from the ABE (acetone-butanol-ethanol) fermentation process. 3 However, due to toxicity of butanol to the bacteria, the maximum achievable concentration of butanol in the fermentation broth is only ~20 g/l (equivalent to 0.5% mole fraction and 2% mass fraction) and the rest is mostly water. 4 The separation of butanol out of this dilute aqueous solution is estimated to account for 60 to 80% of biofuel production cost. 5 Accordingly, the development of a low-energy separation process is vital for the improvement of biofuel technology. For the energy cost of the separation process, a target value of less than 4 MJ/kg of recovered butanol, which is equivalent to 10% of the internal combustion energy of butanol (36.3 MJ/kg), has been established. 4 Adsorption using porous solids is one of the most promising separation technologies for alcohol/water separation due to its relatively mild operating conditions and low energy cost. Because of the high water concentration, a higher affinity to alcohol than water is required for the adsorbent materials. Accordingly, porous hydrophobic materials are desired for this application. Metal-organic frameworks (MOFs) are a new family of nanoporous crystalline materials, assembled from metal clusters and organic linkers. MOFs are remarkably tunable by the appropriate selection of building blocks and functionalization. In addition, many MOFs have already been shown to be hydrophobic and water stable, 6 which suggests that suitable MOFs should exist (or be synthesizable) for this application. Methods We used grand canonical Monte Carlo (GCMC) simulations to study the separation of alcohol/water mixtures on a molecular level with our in-house code RASPA. 7 GCMC simulations are commonly been carried out for gas-phase adsorption. In order to simulate liquid-phase adsorption in MOFs, we used a three-phase equilibrium approach. Given a known liquid solution with fixed temperature and composition, we calculate the partial pressure of each species in the gas phase which is in equilibrium with the solution using an equation of state. Then we run a GCMC simulation to calculate the equilibrium between the gas phase and the adsorbed (MOF) phase using the partial pressures calculated before as simulation inputs. In this way, we can link 1

the liquid phase and the MOF phase together through the gas phase. Accomplishments Task 1. Development of efficient strategies to simulate water adsorption: Water presents a particular challenge for GCMC simulations due to strong water-water interactions and the specific geometries imposed by hydrogen bonding. All Monte Carlo moves that change existing energetically favorable water-water interactions have extremely low acceptance ratios (~0.02%), so the simulations take a very long time to equilibrate. To enhance the simulation speed, we proposed and incorporated several advanced algorithms into our simulation code including energy-bias GCMC, orientation-bias GCMC, continuous fractional component (CFC) moves and molecular dynamics simulation combined with the Widom insertion method. We have tested these algorithms in some sample systems and obtained some preliminary results. Qualitatively speaking, in some cases, we can really improve the simulation speed with these methods. Currently, we are looking into more details of these algorithms to improve their performance for our particular application, and we will continue to test them in more MOF materials. Task 2. Calculation of selectivity and adsorption capacities in MOFs: To test our model for water and alcohol adsorption in MOFs, we selected two well-known hydrophobic MOFs ZIF-8 8 and FMOF-1 9 to calculate their single component and mixture isotherms. As shown in Figures 1 and 2, our simulation results match the experimental measurements quite well (even better than previous simulation data in the literature 10 ). Ethanol and butanol pure-component isotherms have a type I isotherm shape in both MOFs. ZIF-8 has much higher capacity than FMOF-1 due to its larger pore volume. For pure water adsorption isotherms (not shown here), ZIF-8 has a type V isotherm shape and has zero water uptake before 0.8 relative humidity. FMOF-1 does not adsorb water at all up to the vapor pressure of water. These simulations validated again that these two structures are highly hydrophobic. Interestingly, in the mixture isotherms (Figure 2), FMOF-1 starts to adsorb water, although the loading is quite low. Our hypothesis is that the adsorbed alcohol molecules will form hydrogen bonds with water molecules to generate alcohol-water complexes, which enhance the water loading. Based on the mixture simulation data, the selectivities of butanol over water in ZIF-8 and FMOF-1 at a butanol concentration of 0.5% are 174 and 1087, respectively, which exceed the selectivities in some good zeolites and activated carbon candidates reported in the literature. Task 3. High-thoughput computational screening of MOFs: To discover additional MOF candidates and establish structure/performance relationships, we also conducted a high-throughput screening of approximately 2000 synthesized MOFs extracted from the Computation-Ready Experimental (CoRE) MOF database, 11 by calculating the Henry s constant (KH) of alcohol and water in each material. The ratio of the KH values of the two 2

components was used to estimate the selectivity in each material. Many materials with high selectivity for alcohol/water were discovered in our screening and will be further studied in our future work. In addition, by looking for correlations between the selectivity and different MOF properties, we can discover some interesting insights. For example, Figure 3 shows the relationship between the selectivity and the ratio of the largest cavity diameter (LCD) to the pore limiting diameter (PLD) of the MOFs. Basically, the higher this ratio, the more the structure resembles a network of large cavities connected by narrow channels or windows. It is clear that for both butanol and ethanol, most of the good candidates with high selectivity locate in the region with LCD/PLD close to one. The best materials, thus, have pores resembling uniform tubes. Conclusion Thanks to the funding from ISEN, we were able to initiate this modeling project on the separation of biofuel/water mixtures with hydrophobic MOFs. We improved the efficiency of simulating water systems by introducing some advanced algorithms. We also calculated the capacities and selectivities in some hydrophobic MOFs and conducted a high throughput screening of 2000 synthesized MOFs to discover promising candidates and obtain valuable insights to guide our future research. Based on the results from this project, we are planning to prepare several manuscripts and collaborate with Prof. Joseph Hupp s team in the Chemistry Department to synthesize and test some of the candidate materials that we identified. Ultimately, this work may help solve real-world problems in the biofuel industry. Figure 1. (a) Simulated 10 and experimental 12 pure alcohol isotherms in ZIF-8 at 308 K. (b) Simulated pure alcohol isotherms in ZIF-8 and FMOF-1 at 298 K. 3

Figure 2. Ethanol and water mixture isotherms in ZIF-8 and FMOF-1 at 298 K and 1 bar. Figure 3. Correlations between the selectivity of alcohol/water mixtures (left: butanol; right: ethanol) and the ratio of largest cavity diameter (LCD) to pore limiting diameter (PLD). 4

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