Selective separation of methanol-water mixture using functionalized boron nitride nanosheet membrane: a computer simulation study

  • Jafar AzamatEmail author
Original Research


The separation of alcohol-water mixture from each other is one of the significant subjects for scientists in the pharmacy and engineering fields owing to economic savings. In this research, the separation of methanol-water mixture was investigated using molecular dynamics (MD) simulations method. The MD results explain the mechanisms of solvent separation from each other in the atomic-scale perspective. As a separator membrane for separation of methanol from water, boron nitride nanosheets (BNNS) with two various functionalized pores was applied. In these systems, in the normal conditions, solvation separation phenomenon did not occur. Therefore, external pressure was applied to the simulation box. Each of methanol and water molecules passed through a specific functionalized pore of BNNS, so that these pores acted as a selective membrane to separate them from each other. Results were confirmed with the calculation of potential of mean force for each solvent in both pores. The separation of the methanol-water mixture using functionalized BNNS was dependent on the amount of applied pressure and the pore size and chemical group on the edge pores.


Boron nitride nanosheet Methanol Separation MD simulation PMF 



The author thanks the Farhangian University for their support.

Compliance with ethical standards

Conflict of interest

The author declares that there are no competing interests.


  1. 1.
    Lively RP, Dose ME, Thompson JA, McCool BA, Chance RR, Koros WJ (2011) Ethanol and water adsorption in methanol-derived ZIF-71. Chem Commun 47(30):8667–8669. Google Scholar
  2. 2.
    Besinis A, van Noort R, Martin N (2016) The use of acetone to enhance the infiltration of HA nanoparticles into a demineralized dentin collagen matrix. Dent Mater 32(3):385–393. Google Scholar
  3. 3.
    Elisia I, Nakamura H, Lam V, Hofs E, Cederberg R, Cait J, Hughes MR, Lee L, Jia W, Adomat HH (2016) DMSO represses inflammatory cytokine production from human blood cells and reduces autoimmune arthritis. PLoS One 11(3):e0152538Google Scholar
  4. 4.
    Stephan DW (2013) Catalysis: a step closer to a methanol economy. Nature 495(7439):54–55Google Scholar
  5. 5.
    Nielsen M, Alberico E, Baumann W, Drexler H-J, Junge H, Gladiali S, Beller M (2013) Low-temperature aqueous-phase methanol dehydrogenation to hydrogen and carbon dioxide. Nature 495(7439):85–89Google Scholar
  6. 6.
    Yanju W, Shenghua L, Hongsong L, Rui Y, Jie L, Ying W (2008) Effects of methanol/gasoline blends on a spark ignition engine performance and emissions. Energy Fuel 22(2):1254–1259Google Scholar
  7. 7.
    Liu S, Clemente ERC, Hu T, Wei Y (2007) Study of spark ignition engine fueled with methanol/gasoline fuel blends. Appl Therm Eng 27(11):1904–1910Google Scholar
  8. 8.
    Boysen DA, Uda T, Chisholm CR, Haile SM (2004) High-performance solid acid fuel cells through humidity stabilization. Science 303(5654):68–70Google Scholar
  9. 9.
    Ren X, Zelenay P, Thomas S, Davey J, Gottesfeld S (2000) Recent advances in direct methanol fuel cells at Los Alamos National Laboratory. J Power Sources 86(1):111–116Google Scholar
  10. 10.
    Schrader J, Schilling M, Holtmann D, Sell D, Villela Filho M, Marx A, Vorholt JA (2009) Methanol-based industrial biotechnology: current status and future perspectives of methylotrophic bacteria. Trends Biotechnol 27(2):107–115Google Scholar
  11. 11.
    Maity JP, Bundschuh J, Chen C-Y, Bhattacharya P (2014) Microalgae for third generation biofuel production, mitigation of greenhouse gas emissions and wastewater treatment: present and future perspectives—a mini review. Energy 78:104–113Google Scholar
  12. 12.
    Vispute TP, Zhang H, Sanna A, Xiao R, Huber GW (2010) Renewable chemical commodity feedstocks from integrated catalytic processing of pyrolysis oils. Science 330(6008):1222–1227Google Scholar
  13. 13.
    Liang K, Li W, Luo H, Xia M, Xu C (2014) Energy-efficient extractive distillation process by combining preconcentration column and entrainer recovery column. Ind Eng Chem Res 53(17):7121–7131Google Scholar
  14. 14.
    Liu F, Liu L, Feng X (2005) Separation of acetone-butanol-ethanol (ABE) from dilute aqueous solutions by pervaporation. Sep Purif Technol 42(3):273–282. Google Scholar
  15. 15.
    Li W, Xu X (1998) Separation of acetone/water mixtures by a modified γ-alumina membrane via a new method. J Membr Sci 149(1):21–27. Google Scholar
  16. 16.
    Zang J, Konduri S, Nair S, Sholl DS (2009) Self-diffusion of water and simple alcohols in single-walled aluminosilicate nanotubes. ACS Nano 3(6):1548–1556Google Scholar
  17. 17.
    Azamat J, Sardroodi JJ, Mansouri K, Poursoltani L (2016) Molecular dynamics simulation of transport of water/DMSO and water/acetone mixtures through boron nitride nanotube. Fluid Phase Equilib 425:230–236. Google Scholar
  18. 18.
    Remy T, Cousin Saint Remi J, Singh R, Webley PA, Baron GV, Denayer JF (2011) Adsorption and separation of C1− C8 alcohols on SAPO-34. J Phys Chem C 115(16):8117–8125Google Scholar
  19. 19.
    Hu K, Nie J, Liu J, Zheng J (2013) Separation of methanol from methanol/water mixtures with pervaporation hybrid membranes. J Appl Polym Sci 128(3):1469–1475Google Scholar
  20. 20.
    Chapeaux A, Simoni LD, Ronan TS, Stadtherr MA, Brennecke JF (2008) Extraction of alcohols from water with 1-hexyl-3-methylimidazolium bis (trifluoromethylsulfonyl) imide. Green Chem 10(12):1301–1306Google Scholar
  21. 21.
    Zhao W-H, Shang B, Du S-P, Yuan L-F, Yang J, Cheng Zeng X (2012) Highly selective adsorption of methanol in carbon nanotubes immersed in methanol-water solution. J Chem Phys 137(3):034501Google Scholar
  22. 22.
    Zheng J, Lennon EM, Tsao H-K, Sheng Y-J, Jiang S (2005) Transport of a liquid water and methanol mixture through carbon nanotubes under a chemical potential gradient. J Chem Phys 122(21):214702Google Scholar
  23. 23.
    Soetens J-C, Bopp PA (2015) Water–methanol mixtures: simulations of mixing properties over the entire range of mole fractions. J Phys Chem B 119(27):8593–8599. Google Scholar
  24. 24.
    Morrone JA, Haslinger KE, Tuckerman ME (2006) Ab initio molecular dynamics simulation of the structure and proton transport dynamics of methanol−water solutions. J Phys Chem B 110(8):3712–3720. Google Scholar
  25. 25.
    Liu Y, Consta S, Goddard WA (2010) Nanoimmiscibility: selective absorption of liquid methanol-water mixtures in carbon nanotubes. J Nanosci Nanotechnol 10(6):3834–3843. Google Scholar
  26. 26.
    Xu M, Liang T, Shi M, Chen H (2013) Graphene-like two-dimensional materials. Chem Rev 113(5):3766–3798. Google Scholar
  27. 27.
    Mortazavi B, Rémond Y (2012) Investigation of tensile response and thermal conductivity of boron-nitride nanosheets using molecular dynamics simulations. Phys E 44(9):1846–1852. Google Scholar
  28. 28.
    Lei W, Zhang H, Wu Y, Zhang B, Liu D, Qin S, Liu Z, Liu L, Ma Y, Chen Y (2014) Oxygen-doped boron nitride nanosheets with excellent performance in hydrogen storage. Nano Energy 6(0):219–224. Google Scholar
  29. 29.
    Azamat J, Khataee A, Sadikoglu F (2016) Separation of carbon dioxide and nitrogen gases through modified boron nitride nanosheets as a membrane: insights from molecular dynamics simulations. RSC Adv 6(97):94911–94920. Google Scholar
  30. 30.
    Sajjad M, Feng P (2014) Study the gas sensing properties of boron nitride nanosheets. Mater Res Bull 49(0):35–38. Google Scholar
  31. 31.
    Xuebin W, Chunyi Z, Qunhong W, Yoshio B, Dmitri G (2013) Boron nitride nanosheets: novel syntheses and applications in polymeric composites. J Phys Conf Ser 471(1):012003Google Scholar
  32. 32.
    Azamat J, Khataee A (2018) MoS2 nanosheet as a promising nanostructure membrane for gas separation. J Ind Eng Chem 66:269–278. Google Scholar
  33. 33.
    Zhu H, Li Y, Fang Z, Xu J, Cao F, Wan J, Preston C, Yang B, Hu L (2014) Highly thermally conductive papers with percolative layered boron nitride nanosheets. ACS Nano 8(4):3606–3613. Google Scholar
  34. 34.
    Schmidt MW, Baldridge KK, Boatz JA, Elbert ST, Gordon MS, Jensen JH, Koseki S, Matsunaga N, Nguyen KA, Su S (1993) General atomic and molecular electronic structure system. J Comput Chem 14(11):1347–1363Google Scholar
  35. 35.
    Won CY, Aluru NR (2007) Water permeation through a subnanometer boron nitride nanotube. J Am Chem Soc 129(10):2748–2749. Google Scholar
  36. 36.
    Phillips JC, Braun R, Wang W, Gumbart J, Tajkhorshid E, Villa E, Chipot C, Skeel RD, Kale L, Schulten K (2005) Scalable molecular dynamics with NAMD. J Comput Chem 26(16):1781–1802Google Scholar
  37. 37.
    Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14(1):33–38. Google Scholar
  38. 38.
    Izaguirre JA, Catarello DP, Wozniak JM, Skeel RD (2001) Langevin stabilization of molecular dynamics. J Chem Phys 114(5):2090–2098. Google Scholar
  39. 39.
    Ciccotti G, Frenkel D, McDonald IR (1987) Simulation of liquids and solids: molecular dynamics and Monte Carlo methods in statistical mechanics. North Holland, New YorkGoogle Scholar
  40. 40.
    Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79(2):926–935. Google Scholar
  41. 41.
    Zhu F, Tajkhorshid E, Schulten K (2004) Theory and simulation of water permeation in aquaporin-1. Biophys J 86(1):50–57. Google Scholar
  42. 42.
    Azamat J, Khataee A, Joo SW, Yin B (2015) Removal of trihalomethanes from aqueous solution through armchair carbon nanotubes: a molecular dynamics study. J Mol Graphics Modell 57:70–75. Google Scholar
  43. 43.
    Fang C, Wu H, Lee S-Y, Mahajan RL, Qiao R (2018) The ionized graphene oxide membranes for water-ethanol separation. Carbon 136:262–269. Google Scholar
  44. 44.
    Wang Y, He Z, Gupta KM, Shi Q, Lu R (2017) Molecular dynamics study on water desalination through functionalized nanoporous graphene. Carbon 116:120–127. Google Scholar
  45. 45.
    Jafarzadeh R, Azamat J, Erfan-Niya H (2018) Fluorine-functionalized nanoporous graphene as an effective membrane for water desalination. Struct Chem 29(6):1845–1852. Google Scholar
  46. 46.
    Azamat J, Khataee A, Joo SW (2016) Molecular dynamics simulations of trihalomethanes removal from water using boron nitride nanosheets. J Mol Model 22(4):82. Google Scholar
  47. 47.
    Kjellander R, Greberg H (1998) Mechanisms behind concentration profiles illustrated by charge and concentration distributions around ions in double layers. J Electroanal Chem 450(2):233–251. Google Scholar
  48. 48.
    Torrie GM, Valleau JP (1977) Nonphysical sampling distributions in Monte Carlo free-energy estimation: umbrella sampling. J Comput Phys 23(2):187–199. Google Scholar
  49. 49.
    Roux B (1995) The calculation of the potential of mean force using computer simulations. Comput Phys Commun 91(1–3):275–282. Google Scholar
  50. 50.
    Richards LA, Schäfer AI, Richards BS, Corry B (2012) The importance of dehydration in determining ion transport in narrow pores. Small 8(11):1701–1709. Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Basic SciencesFarhangian UniversityTehranIran

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