Skip to main content
Log in

Hydrogen-bonded networks in alcohol-acetone binary mixtures: molecular dynamics study

  • Original Paper
  • Published:
Journal of Molecular Modeling Aims and scope Submit manuscript

Abstract

Our previous studies on various hydrogen-bonded binary systems have shown anomalous physico-chemical properties at lower (10–30%) volume concentrations of either one or both of the components. In order to have a better understanding of this phenomenon, a systematic molecular dynamics study of binary mixtures of acetone with eight primary alcohols (R-OH, with R = 1 to 8) was undertaken. The structure of the binary systems is studied using radial distribution function, hydrogen bond statistics, and graph theoretical approach. Two distinct features are observed. Firstly, the bunching of R = (1, 2), R = (3, 4, 6), and R = (5, 7, 8)-acetone mixture in their hydrogen bond characteristics. Secondly, the number of alcohol-acetone hydrogen bonds is more for R = (3, 4, 6) and the alcohol-alcohol hydrogen bonds for the rest, indicating a preferential bonding of R = (3, 4, 6) alcohols with acetone when compared to the rest. With an increase in acetone concentration, the average degree of association decreases for all systems, showing an overall decrease in hydrogen bond multimer structures. The hydrogen bond networks are visualized using graph theory.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Demontis P, Masia M, Suffritti GB (2014) Peculiar structure of water in slightly superhydrated vermiculite clay studied by Car-Parrinello molecular dynamics simulations. J Phys Chem C 118(15):7923–7931. https://doi.org/10.1021/jp409723x

    Article  CAS  Google Scholar 

  2. Daniel Głowacki E, Irimia-Vladu M, Bauer S, SerdarSariciftci N (2013) Hydrogen-bonds in molecular solids – from biological systems to organic electronics. J Mater Chem B 1(31):3742–3753. https://doi.org/10.1039/C3TB20193G

    Article  Google Scholar 

  3. Pardo L, Campillo M, Bosch D, Pastor N, Weinstein H (2000) Binding mechanisms of TATA box-binding proteins: DNA kinking is stabilized by specific hydrogen bonds. Biophys J 78(4):1988–1996. https://doi.org/10.1016/S0006-3495(00)76746-4

    Article  CAS  Google Scholar 

  4. Kim B-S, Park SW, Hammond PT (2008) Hydrogen-bonding layer-by-layer-assembled biodegradable polymeric micelles as drug delivery vehicles from surfaces. ACS Nano 2(2):386–392. https://doi.org/10.1021/nn700408z

    Article  CAS  Google Scholar 

  5. Heinzelmann G, Figueiredo W, Girardi M (2012) Micellar dynamics and water–water hydrogen-bonding from temperature-jump Monte Carlo simulations. Chem Phys Lett 550:83–87. https://doi.org/10.1016/j.cplett.2012.09.011

    Article  CAS  Google Scholar 

  6. Joly L, Tocci G, Merabia S, Michaelides A (2016) Strong coupling between nanofluidic transport and interfacial chemistry: how defect reactivity controls liquid–solid friction through hydrogen bonding. J Phys Chem Lett 7(7):1381–1386. https://doi.org/10.1021/acs.jpclett.6b00280

    Article  CAS  Google Scholar 

  7. Nilavarasi K, Madhurima V (2018) Controlling breath figure patterns on PDMS by concentration variation of ethanol-methanol binary vapors. Eur Phys J E 41(7):82. https://doi.org/10.1140/epje/i2018-11691-x

    Article  CAS  Google Scholar 

  8. Hao M-H (2006) Theoretical calculation of hydrogen-bonding strength for drug molecules. J Chem Theory Comput 2(3):863–872. https://doi.org/10.1021/ct0600262

    Article  CAS  Google Scholar 

  9. Zhang X, Liu C, Wang Z (2008) Force spectroscopy of polymers: studying on intramolecular and intermolecular interactions in single molecular level. Polymer 49(16):3353–3361. https://doi.org/10.1016/j.polymer.2008.04.056

    Article  CAS  Google Scholar 

  10. Rojas-Ruiz FA, Vargas-Méndez LY, Kouznetsov VV (2011) Challenges and perspectives of chemical biology, a successful multidisciplinary field of natural sciences. Molecules 16(3) Art. no. 3, https://doi.org/10.3390/molecules16032672

  11. Benedetti AV, Cilense M, Vollet DR, Montone RC (1983) Thermodynamic properties of liquid mixtures. III. Acetone—water. Thermochim Acta 66(1):219–223. https://doi.org/10.1016/0040-6031(93)85032-5

    Article  CAS  Google Scholar 

  12. Modla G, Lang P (2010) Separation of an acetone−methanol mixture by pressure-swing batch distillation in a double-column system with and without thermal integration. Ind Eng Chem Res 49(8):3785–3793. https://doi.org/10.1021/ie9019352

    Article  CAS  Google Scholar 

  13. Essafri I, Ghoufi A (2019) Microstructure of nonideal methanol binary liquid mixtures. Phys Rev E 99(6):062607. https://doi.org/10.1103/PhysRevE.99.062607

    Article  CAS  Google Scholar 

  14. Bakó I, Megyes T, Bálint S, Grósz T, Chihaia V (2008) Water–methanol mixtures: topology of hydrogen bonded network. Phys Chem Chem Phys 10(32):5004–5011. https://doi.org/10.1039/B808326F

    Article  Google Scholar 

  15. Zhang X et al (2021) Using acetone/water binary solvent to enhance the stability and bioavailability of spray dried enzalutamide/HPMC-AS solid dispersions. J Pharm Sci 110(3):1160–1171. https://doi.org/10.1016/j.xphs.2020.10.010

    Article  CAS  Google Scholar 

  16. Koch K, Górak A (2014) Pervaporation of binary and ternary mixtures of acetone, isopropyl alcohol and water using polymeric membranes: experimental characterisation and modelling. Chem Eng Sci 115:95–114. https://doi.org/10.1016/j.ces.2014.02.009

    Article  CAS  Google Scholar 

  17. Perera A, Zoranić L, Sokolić F, Mazighi R (2011) A comparative molecular dynamics study of water–methanol and acetone–methanol mixtures. J Mol Liq 159(1):52–59. https://doi.org/10.1016/j.molliq.2010.05.006

    Article  CAS  Google Scholar 

  18. Požar M, Zoranić L (2020) The structuring in mixtures with acetone as the common solvent. Phys Chem Liq 58(2):184–201. https://doi.org/10.1080/00319104.2018.1564305

    Article  CAS  Google Scholar 

  19. Hagberg AA, Schult DA, Swart PJ (2022) Exploring network structure, dynamics, and function using networkX, in proceedings of the 7th python in science conference, Pasadena, CA USA, 2008, pp. 11–15. Accessed: Jan. 17, 2022. [Online]. Available: http://conference.scipy.org/proceedings/SciPy2008/paper_2/

  20. Jorgensen WL, Maxwell DS, Tirado-Rives J (1996) Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J Am Chem Soc 118(45):11225–11236. https://doi.org/10.1021/ja9621760

    Article  CAS  Google Scholar 

  21. Galicia-Andrés E, Dominguez H, Pusztai L, Pizio O (2015) Composition dependence of thermodynamic, dynamic and dielectric properties of water–methanol model mixtures. Molecular dynamics simulation results with the OPLS-AA model for methanol. J Mol Liq 212:70–78. https://doi.org/10.1016/j.molliq.2015.08.061

    Article  CAS  Google Scholar 

  22. Abraham MJ et al (2015) GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2:19–25. https://doi.org/10.1016/j.softx.2015.06.001

    Article  Google Scholar 

  23. Berendsen HJC, van der Spoel D, van Drunen R (1995) GROMACS: a message-passing parallel molecular dynamics implementation. Comput Phys Commun 91(1–3):43–56. https://doi.org/10.1016/0010-4655(95)00042-E

    Article  CAS  Google Scholar 

  24. Hess B, Kutzner C, van der Spoel D, Lindahl E (2008) GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J Chem Theory Comput 4(3):435–447. https://doi.org/10.1021/ct700301q

    Article  CAS  Google Scholar 

  25. Kutzner C, Páll S, Fechner M, Esztermann A, de Groot BL, Grubmüller H (2015) Best bang for your buck: GPU nodes for GROMACS biomolecular simulations. J Comput Chem 36(26):1990–2008. https://doi.org/10.1002/jcc.24030

    Article  CAS  Google Scholar 

  26. Kutzner C, Páll S, Fechner M, Esztermann A, Groot BL, Grubmüller H (2019) More bang for your buck: improved use of GPU nodes for GROMACS 2018. J Comput Chem 40(27):2418–2431. https://doi.org/10.1002/jcc.26011

    Article  CAS  Google Scholar 

  27. Lindahl E, Hess B, van der Spoel D (2001) GROMACS 3.0: a package for molecular simulation and trajectory analysis. J Mol Model 7(8):306–317. https://doi.org/10.1007/s008940100045

    Article  CAS  Google Scholar 

  28. Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC (2005) GROMACS: fast, flexible, and free. J Comput Chem 26(16):1701–1718. https://doi.org/10.1002/jcc.20291

    Article  CAS  Google Scholar 

  29. Martínez L, Andrade EGBR, Martínez JM (2009) Packmol: a package for building initial configurations for molecular dynamics simulations. J Comput Chem 30(13):2157–2164

    Article  Google Scholar 

  30. Abraham MJ, Lindahl E, Hess B (n.d.) GROMACS, and development team, “GROMACS user manual version 5.0.6.” [Online]. Available: www.gromacs.org

  31. Berendsen HJC, Postma JPM, van Gunsteren WF, DiNola A, Haak JR (n.d.) Molecular dynamics with coupling to an external bath. J Chem Phys 81(8) https://doi.org/10.1063/1.448118

  32. Bussi G, Donadio D, Parrinello M (2007) Canonical sampling through velocity rescaling. J Chem Phys 126(1):014101. https://doi.org/10.1063/1.2408420

  33. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol Graph 14(1):33–38. https://doi.org/10.1016/0263-7855(96)00018-5

  34. Dodda LS, Cabeza de Vaca I, Tirado-Rives J, Jorgensen WL (2017) LigParGen web server: an automatic OPLS-AA parameter generator for organic ligands. Nucleic Acids Res 45(W1):W331–W336. https://doi.org/10.1093/nar/gkx312

    Article  CAS  Google Scholar 

  35. Lide D (2010) CRC handbook of chemistry and physics, 90th ed.

  36. Luzar A, Chandler D (n.d.) Effect of environment on hydrogen bond dynamics in liquid water. Am Phys Soc 76(6):928–931. https://doi.org/10.1103/PhysRevLett.76.928

  37. Gao J, Bosco DA, Powers ET, Kelly JW (2009) Localized thermodynamic coupling between hydrogen bonding and microenvironment polarity substantially stabilizes proteins. Nat Struct Mol Biol 16(7):684–690. https://doi.org/10.1038/nsmb.1610

  38. Stumpe MC, Grubmüller H (2007) Aqueous urea solutions: structure, energetics, and urea aggregation. J Phys Chem B 111(22):6220–6228. https://doi.org/10.1021/jp066474n

    Article  CAS  Google Scholar 

  39. Gerlt JA, Kreevoy MM, Cleland WW, Frey PA (1997) Understanding enzymic catalysis: the importance of short, strong hydrogen bonds. Chem Biol 4(4):259–267. https://doi.org/10.1016/S1074-5521(97)90069-7

  40. Herschlag D, Pinney MM (2018) Hydrogen bonds: simple after All? Biochemistry 57(24):3338–3352. https://doi.org/10.1021/acs.biochem.8b00217

    Article  CAS  Google Scholar 

  41. (2020) Chapter 1. Hydrogen bond – definitions, criteria of existence and various types,” in Theoretical and Computational Chemistry Series, Cambridge: Royal Society of Chemistry, pp. 1–40. https://doi.org/10.1039/9781839160400-00001

  42. Svishchev IM, Kusalik PG (1994) Structure in liquid methanol from spatial distribution functions. J Chem Phys 100(7):5165–5171. https://doi.org/10.1063/1.467181

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors acknowledge the use of the Inter-University Center for Astronomy and Astrophysics (IUCAA) server to run the MD simulations. AU acknowledges the Central University of Tamil Nadu, India for providing university fellowship.

Author information

Authors and Affiliations

Authors

Contributions

AU and TRK performed all the simulations, V M concieved the orginal idea.  All the authors were involved in the analysis of the results and contributed equally to the consolidation of results, writing, and editing of the manuscript.

Corresponding author

Correspondence to Abdulkareem U.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 64458 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

U, A., Kartha, T.R. & Madhurima, V. Hydrogen-bonded networks in alcohol-acetone binary mixtures: molecular dynamics study. J Mol Model 28, 382 (2022). https://doi.org/10.1007/s00894-022-05369-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00894-022-05369-4

Keywords

Navigation