Skip to main content
SpringerLink
Log in

Investigation and Modeling of the Solubility of Anthracene in Organic Phases

  • Published:
Journal of Solution Chemistry Aims and scope Submit manuscript

Abstract

Investigation of the solubility of anthracene can yield potential models to give insights into solvent–solute systems of polyaromaric hydrocarbons (PAHs). This is important in petroleum industries and also fluorescence studies of polyaromatics in organic phases. A five parametric linear QSPR model, based structural/theoretical descriptors of solvents, and a five parametric LSER model using empirical scales, are developed to predict the Ostwald solubility coefficient of anthracene in various organic solvents. Both QSPR and LSER models obtained good prediction quality with covering 84 and 88% of data variance, respectively. The validation process of these models was done using cross validation, y-randomization and external test set. The applicability domain of the proposed model was also calculated using both a Williams plot and the standardization approach. The first model shows the role of some structural features combined with mass, charge and electronegativity of solvents in prediction of anthracene’s Ostwald solubility coefficient in organic phases. In addition, the second, alternative model based on empirical scales reveals the contributions of solvent polarity, polarizability, dielectric constant and acidity parameter to the solubility of anthracene.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Abbreviations

PAH:

Polycyclic aromatic hydrocarbons

MLR:

Multiple linear regression analysis

VIF:

Variance inflation factor

AD:

Applicability domain

PCA:

Principle component analysis

QSPR:

Quantitative structure property relationship

LSER:

Linear solvation energy relationship

MAE:

Mean absolute error

References

  1. Means, J.C., Wood, S.G., Hassett, J.J., Banwart, W.L.: Sorption of polynuclear aromatic hydrocarbons by sediments and soils. Environ. Sci. Technol. 14, 1524–1528 (1980)

    Article  CAS  Google Scholar 

  2. Ali, S.H., Al-Rashed, O.A.: Solubility of pyrene in simple and mixed solvent systems. Fluid Phase Equilib. 281, 133–143 (2009)

    Article  CAS  Google Scholar 

  3. Powell, J.R., Voisinet, D., Salazar, A., Acree, W.E.: Solubility of pyrene in organic nonelectrolyte solvents. Comparison of observed versus predicted values based upon mobile order theory. Phys. Chem. Liq. 28, 269–276 (1994)

    Article  CAS  Google Scholar 

  4. McCargar, J.W., Acree, W.E.: Thermochemical investigations of associated solutions: 4. Calculation of carbazole–dibutyl ether association constants from measured solubility in binary solvent mixtures. J. Pharm. Sci. 76, 572–574 (1987)

    Article  CAS  Google Scholar 

  5. Acree, W.E., Tucker, S.A.: Solubility of anthracene in binary p-xylene + alkane and benzene + alkane solvent mixtures. Phys. Chem. Liq. 20, 31–38 (1989)

    Article  CAS  Google Scholar 

  6. Acree, W.E., Zvaigzne, A.I., Tucker, S.A.: Thermochemical investigations of hydrogen-bonded solutions: development of a predictive equation for the solubility of anthracene in binary hydrocarbon. Fluid Phase Equilib. 92, 233–253 (1994)

    Article  CAS  Google Scholar 

  7. Acree, W.E.: Polycyclic aromatic hydrocarbons: binary non-aqueous systems, Part 1: solutes A–E. In: Solubility Data Series, pp. 1–44. Oxford University Press, Oxford (1995)

  8. Acree Jr., W.E., Abraham, M.H.: Solubility predictions for crystalline nonelectrolyte solutes dissolved in organic solvents based upon the Abraham general solvation model. Can. J. Chem. 79, 1466–1476 (2001)

    Article  CAS  Google Scholar 

  9. Acree, W.E.: Thermochemical investigations of associated solutions. Part 14.—Calculation of anthracene–butyl acetate association parameters from measured solubility data. J. Chem. Soc., Faraday Trans. I 87, 461–464 (1991)

    Article  CAS  Google Scholar 

  10. Roy, L.E., Hernández, C.E., Acree, W.E.: Solubility of anthracene in organic nonelectrolyte solvents. Comparison of observed versus predicted values based upon mobile order theory. Polycycl. Aromat. Compd. 13, 105–116 (1999)

    Article  CAS  Google Scholar 

  11. Zhang, Q., Hu, Y., Shi, Y., Yang, Y., Cheng, L., Cao, C., Yang, W.: Thermodynamic models for determination of the solubility of dibenzothiophene in different solvents at temperatures from (278.15 to 328.15) K. J. Chem. Eng. Data 59, 2799–2804 (2014)

    Article  CAS  Google Scholar 

  12. Ruelle, P., Sarraf, E., Kesselring, U.W.: Prediction of carbazole solubility and its dependence upon the solvent nature. Int. J. Pharm. 104, 125–133 (1994)

    Article  CAS  Google Scholar 

  13. Monárrez, C.I., Stovall, D.M., Woo, J.H., Taylor, P., Acree, W.E.: Solubility of xanthene in organic nonelectrolyte solvents: comparison of observed versus predicted values based upon mobile order theory. Phys. Chem. Liq. 40, 703–714 (2002)

    Article  Google Scholar 

  14. Franck, H.-G., Stadelhofer, J.W.: Anthracene—production and uses. In: Industrial Aromatic Chemistry, pp. 343–361. Springer, Berlin (1988)

  15. Diaz, A.: Analytical applications of 1,10-anthraquinones: A review. Talanta 38, 571–588 (1991)

    Article  CAS  Google Scholar 

  16. He, Z.-H., He, M.-F., Ma, S.-C., But, P.P.-H.: Anti-angiogenic effects of rhubarb and its anthraquinone derivatives. J. Ethnopharmacol. 121, 313–317 (2009)

    Article  CAS  Google Scholar 

  17. Honarasa, F., Yousefinejad, S., Nasr, S., Nekoeina, M.: Structure–electrochemistry relationship in non-aqueous solutions: Predicting the reduction potential of anthraquinones derivatives in some organic solvents. J. Mol. Liq. 212, 52–57 (2015)

    Article  CAS  Google Scholar 

  18. Yousefinejad, S., Hemmateenejad, B.: Chemometrics tools in QSAR/QSPR studies: a historical perspective. Chemom. Intell. Lab. Syst. 149, 177–204 (2015)

    Article  CAS  Google Scholar 

  19. Dearden, J.C.: The history and development of quantitative structure-activity relationships (QSARs). Int. J. Quant. Struct. Relationships 1, 1–44 (2016)

    Article  Google Scholar 

  20. Roy, K., Kar, S., Das, R.N.: Understanding the Basics of QSAR for Applications in Pharmaceutical. Academic Press, Tokyo (2015)

    Google Scholar 

  21. Katritzky, A.R., Oliferenko, A.A., Oliferenko, P.V., Petrukhin, R., Tatham, D.B., Maran, U., Lomaka, A., Acree, W.E.: A general treatment of solubility. 1. The QSPR correlation of solvation free energies of single solutes in series of solvents. J. Chem. Inf. Comput. Sci. 43, 1794–1805 (2003)

    Article  CAS  Google Scholar 

  22. Yousefinjead, S., Honarasa, F., Chaabi, M.: New relationship models for solvent–pyrene solubility based on molecular structure and empirical properties. New J. Chem. 40, 10197–10207 (2016)

    Article  Google Scholar 

  23. Abraham, M.H., Grellier, P.L., McGill, R.A.: Determination of olive oil–gas and hexadecane–gas partition coefficients, and calculation of the corresponding olive oil–water and hexadecane–water partition coefficients. J. Chem. Soc., Perkin Trans. 2, 797–803 (1987)

    Article  Google Scholar 

  24. Mauri, A., Consonni, V., Pavan, M., Todeschini, R.: Dragon software: an easy approach to molecular descriptor calculations. MATCH Commun. Math. Comput. Chem. 56, 237–248 (2006)

    CAS  Google Scholar 

  25. Katritzky, A.R., Fara, D.C., Kuanar, M., Hur, E., Karelson, M.: The classification of solvents by combining classical QSPR methodology with principal component analysis. J. Phys. Chem. A 109, 10323–10341 (2005)

    Article  CAS  Google Scholar 

  26. Golbraikh, A., Tropsha, A.: Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. Mol. Divers. 5, 231–243 (2002)

    Article  Google Scholar 

  27. Yousefinejad, S., Honarasa, F., Solhjoo, A.: On the solubility of ferrocene in nonaqueous solvents. J. Chem. Eng. Data 61, 614–621 (2016)

    Article  CAS  Google Scholar 

  28. Gramatica, P.: On the development and validation of QSAR models. In: Reisfeld, B., Mayeno, A.N. (eds.) Computational Toxicology, pp. 499–526. Humana Press, New York (2013)

  29. Gramatica, P.: External evaluation of QSAR models, in addition to cross-validation: verification of predictive capability on totally new chemicals. Mol. Inform. 33, 311–314 (2014)

    Article  CAS  Google Scholar 

  30. Tetko, I.V., Livingstone, D.J., Luik, A.I.: Neural network studies. 1. Comparison of overfitting and overtraining. J. Chem. Inf. Model. 35, 826–833 (1995)

    Article  CAS  Google Scholar 

  31. Ojha, P.K., Mitra, I., Das, R.N., Roy, K.: Further exploring rm2 metrics for validation of QSPR models. Chemom. Intell. Lab. Syst. 107, 194–205 (2011)

    Article  CAS  Google Scholar 

  32. Roy, K., Mitra, I., Kar, S., Ojha, P.K., Das, R.N., Kabir, H.: Comparative studies on some metrics for external validation of QSPR models. J. Chem. Inf. Model. 52, 396–408 (2012)

    Article  CAS  Google Scholar 

  33. Roy, K., Chakraborty, P., Mitra, I., Ojha, P.K., Kar, S., Das, R.N.: Some case studies on application of “rm2” metrics for judging quality of quantitative structure–activity relationship predictions: Emphasis on scaling of response data. J. Comput. Chem. 34, 1071–1082 (2013)

    Article  CAS  Google Scholar 

  34. Roy, K., Das, R.N., Ambure, P., Aher, R.B.: Be aware of error measures. Further studies on validation of predictive QSAR models. Chemom. Intell. Lab. Syst. 152, 18–33 (2016)

    Article  CAS  Google Scholar 

  35. Rücker, C., Rücker, G., Meringer, M.: y-Randomization and its variants in QSPR/QSAR. J. Chem. Inf. Model. 47, 2345–2357 (2007)

    Article  Google Scholar 

  36. Eriksson, L., Jaworska, J., Worth, A.P., Cronin, M.T.D., McDowell, R.M., Gramatica, P.: Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. Environ. Health Perspect. 111, 1361–1375 (2003)

    Article  CAS  Google Scholar 

  37. Craney, T.A., Surles, J.G.: Model-dependent variance inflation factor cutoff values. Qual. Eng. 14, 391–403 (2002)

    Article  Google Scholar 

  38. Yousefinejad, S., Honarasa, F., Montaseri, H.: Linear solvent structure–polymer solubility and solvation energy relationships to study conductive polymer/carbon nanotube composite solutions. RSC Adv. 5, 42266–42275 (2015)

    Article  CAS  Google Scholar 

  39. Todeschini, R., Consonni, V., Gramatica, P.: Chemometrics in QSAR. In: Tauler, R., Walczak, B., Brown, S.D. (eds.) Comprehensive Chemometrics: Chemical and Biochemical Data Analysis. Elsevier B.V, Amsterdam (2009)

    Google Scholar 

  40. Yousefinejad, S., Honarasa, F., Abbasitabar, F., Arianezhad, Z.: New LSER model based on solvent empirical parameters for the prediction and description of the solubility of buckminsterfullerene in various solvents. J. Solution Chem. 42, 1620–1632 (2013)

    Article  CAS  Google Scholar 

  41. Yousefinejad, S., Hemmateenejad, B.: A chemometrics approach to predict the dispersibility of graphene in various liquid phases using theoretical descriptors and solvent empirical parameters. Colloids Surfaces, A 441, 766–775 (2014)

    Article  CAS  Google Scholar 

  42. Kamlet, M.J., Taft, R.W.: Linear solvation energy relationships. Part 1. Solvent polarity–polarizability effects on infrared spectra. J. Chem. Soc., Perkin Trans. 2, 337–341 (1979)

    Article  Google Scholar 

  43. Taft, R.W., Abboud, J.-L.M., Kamlet, M.J., Abraham, M.H.: Linear solvation energy relations. J. Solution Chem. 14, 153–186 (1985)

    Article  CAS  Google Scholar 

  44. Fawcett, W.R.: Acidity and basicity scales for polar solvents. J. Phys. Chem. 97, 9540–9546 (1993)

    Article  CAS  Google Scholar 

  45. Abe, T.: Improvements of the empirical π* solvent polarity scale. Bull. Chem. Soc. Jpn 63, 2328–2338 (1990)

    Article  CAS  Google Scholar 

  46. Pincock, R.E.: Effects of nonpolar solvents on an ionic reaction. The ionic decomposition of tert-butylperoxy formate. J. Am. Chem. Soc. 86, 1820–1826 (1964)

    Article  CAS  Google Scholar 

  47. Brooker, L.G.S., Craig, A.C., Heseltine, D.W., Jenkins, P.W., Lincoln, L.L.: Color and constitution. XIII. Merocyanines as solvent property indicators. J. Am. Chem. Soc. 87, 2443–2450 (1965)

    Article  CAS  Google Scholar 

  48. Todeschini, R., Consonni, V.: Molecular Descriptors for Chemoinformatics. WILEY-VCH, Weinheim (2009)

    Book  Google Scholar 

  49. Kamlet, M.J., Abboud, J.L.M., Taft, R.W.: An examination of linear solvation energy relationships. In: Taft, R.W. (ed.) Progress in Physical Organic Chemistry, vol. 13, pp. 485–630. Wiley, Hoboken (1981)

    Chapter  Google Scholar 

  50. Gemperline, P. (ed.): Practical Guide to Chemometrics. Taylor & Francis Group, Boca Raton (2006)

    Google Scholar 

  51. Netzeva, T.I., Worth, A.P., Aldenberg, T., Benigni, R., Cronin, M.D., Gramatica, P., Jaworska, J.S., Kahn, S., Klopman, G.A.C., Myatt, G., Nikolova-Jeliazkova, N., Patlewicz, G.Y., Perkins, R.: Current status of methods for defining the applicability domain of (quantitative) structure–activity relationships. Altern. Lab. Anim. 2, 1–19 (2005)

    Google Scholar 

  52. Roy, K., Kar, S., Ambure, P.: On a simple approach for determining applicability domain of QSAR models. Chemom. Intell. Lab. Syst. 145, 22–29 (2015)

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Occupational Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran

    Saeed Yousefinejad

  2. Department of Chemistry, Shiraz Branch, Islamic Azad University, Shiraz, Iran

    Fatemeh Honarasa & Fahime Zangene

  3. Department of Chemistry, Payame Noor University (PNU), P.O. BOX 19395-3697, Tehran, Iran

    Mohsen Nekoeinia

Authors
  1. Saeed Yousefinejad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fatemeh Honarasa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohsen Nekoeinia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fahime Zangene
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saeed Yousefinejad.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 121 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yousefinejad, S., Honarasa, F., Nekoeinia, M. et al. Investigation and Modeling of the Solubility of Anthracene in Organic Phases. J Solution Chem 46, 352–373 (2017). https://doi.org/10.1007/s10953-017-0568-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10953-017-0568-0

Keywords

Search

Navigation