1
58
HABIBI-YANGJEH, JAFARI-TARZANAG, AND BANAEI
trained neural network could fairly represent the depen-
dence of the reaction rate constant on solvatochromic
parameters. Then the optimized neural network could
simulate the complicated nonlinear relationship be-
tween the log kA values and the properties of media.
The MPD of 5.023 for the prediction set by the MLR
model should be compared with the value 0.343 by
the ANN model. It can be seen from Table IV that,
although the parameters appearing in the MLR model
are used as inputs for the generated ANN, the statistics
show a large improvement. Because the improvement
of the results obtained using nonlinear model (ANN)
is considerable, it can be concluded that the nonlinear
characteristics of solvent effects on the rate constant
are serious.
9. Jacob, D. S.; Bitton, L.; Grinblat, J.; Felner, I.; Koltypin,
Y.; Gedanken, A. Chem Mater 2006, 18, 3162–3168.
0. Meciarova, M.; Toma, S. Chem Eur J 2007, 13, 1268–
1
1
1
1
1
1
272.
1. Zhai, Y.; Gao, Y.; Liu, F.; Zhang, Q.; Gao, G. Mater Lett
007, 61, 5056–5058.
2
2. Yu, N.; Gong, L.; Song, H.; Liu, Y.; Yin, D. J Solid State
Chem 2007, 180, 799–803.
3. Farag, H. K.; Endres, F. J Mater Chem 2008, 18, 442–
449.
4. Mumalo-Djokic, D.; Stern, W. B.; Taubert, A. Cryst
Growth Des 2008, 8, 330–335.
15. Lancaster, N. L.; Welton, T.; Young, G. B. J Chem Soc
Perkin Trans 2, 2001, 2267–2270.
1
1
1
1
6. Kim, D. W.; Song, C. E.; Chin, D. Y. J Org Chem 2003,
8, 4281–4288.
7. Chiappe, C.; Pieraccini, D. J Org Chem 2004, 69, 6059–
064.
8. Laali, K. K.; Sarca, V. D.; Okazaki, T.; Brock, A.; Der,
P. Org Biomol Chem 2005, 3, 1034–1042.
6
6
CONCLUSIONS
9. D’Anna, F.; Frenna, V.; Noto, R.; Pace, V.; Spinelli, D.
J Org Chem 2006, 71, 9637–9642.
The solvatochromic parameters of the media are not
individually the main factor in determining solvent
effects on rate constant of the reaction between 1-
chloro-2,4-dinitrobenzene and aniline in mixtures of
an RTIL with methanol, chloroform, and dimethylsul-
foxide. MLR demonstrates that the rate constant of the
20. Landini, D.; Maia, A. Tetrahedron Lett 2005, 46, 3961–
3963.
21. P Pool, S. J.; Klingshirn, M. A.; Rogers, R. D.; Shaugh-
nessy, K. H. J Organomet Chem 2005, 690, 3522–3528.
2
2
2
2
2. D Anna, F.; Frenna, V.; Noto, R.; Pace, V.; Spinelli, D.
J Org Chem 2006, 71, 5144–5150.
3. D Anna, F.; Frenna, V.; Pace, V.; Noto, R. Tetrahedron
∗
reaction increases with π and β parameters and de-
creases with α. The results clearly demonstrate that,
in this reaction at least, there is no special “ionic liq-
uid effect” and that all significant interactions between
the ionic liquid and the compounds in the course of
the reaction are adequately described by an appropri-
ate combination of solvatochromic parameters. It was
found that properly selected and trained artificial neu-
ral network could fairly represent dependence of the
rate constant on solvatochromic parameters relative to
the MLR model. These improvements are due to the
fact that rate constant of the reaction shows nonlinear
correlations with the solvatochromic parameters.
2006, 62, 1690–1698.
4. Crowhurst, L.; Falcone, R.; Lancaster, N. L.; Llopis-
Mestre, V.; Welton, T. J Org Chem 2006, 71, 8847–8853.
5. Betti, C.; Landini, D.; Maia, A. Tetrahedron 2008, 64,
1689–1695.
26. Chiappe, C.; Pieraccini, D. J Phys Org Chem 2005, 18,
275–297.
27. Harifi-Mood, A. R.; Habibi-Yangjeh, A.; Gholami,
M. R. Int J Chem Kinet 2007, 39, 681–687.
2
2
3
3
8. Mancini, P. M.; Fortunato, G. G.; Adam, C. G.; Vottero,
L. R. J Phys Org Chem 2008, 21, 87–95.
9. Despagne, F.; Massart, D. L. Analyst 1998, 123, 157–
168.
0. Zupan, J.; Gasteiger, J. Neural Networks in Chemistry
and Drug Design; Wiley-VCH: Germany 1999.
1. Habibi-Yangjeh, A.; Nooshyar, M. Phys Chem Liq,
BIBLIOGRAPHY
2005, 43, 239–247.
1
2
. Wang, Y.; Yang, H. J Am Chem 2005, 127, 5316–5317.
. Wasserscheid, P.; Welton, T. Ionic Liquids in Synthesis;
Wiley-VCH: Weinheim, Germany 2003.
32. Habibi-Yangjeh, A.; Nooshyar, M. Bull Korean Chem
Soc 2005, 26, 139–145.
33. Habibi-Yangjeh, A.; Danandeh-Jenagharad, M.;
Nooshyar, M. Bull Korean Chem Soc 2005, 26,
2007–2016.
34. Habibi-Yangjeh, A.; Danandeh-Jenagharad, M.;
Nooshyar, M. J Mol Model 2006, 12, 338–347.
35. Habibi-Yangjeh, A. Bull Korean Chem Soc 2007, 28,
1472–1476.
3
4
. Welton, T. Coord Chem Rev 2004, 248, 2459–2477.
. Li, Z.; Liu, H.; Liu, Y.; He, P.; Li, J. J Phys Chem
B 2004, 108, 17512–17518.
5. Wilkes, J. S. J Mol Catal A 2004, 214, 11–17.
6. Zhang, Z. C. Adv Catal 2006, 49, 153–237.
7. Parvulescu, V. I.; Hardacre, C. Chem Rev 2007, 107,
2
615–2665.
36. Habibi-Yangjeh, A. Phys Chem Liq 2007, 45, 471–478.
37. Habibi-Yangjeh, A.; Esmailian, M. Bull Korean Chem
Soc 2007, 28, 1477–1484.
8
. Berthod, A.; Ruiz-Angel, M. J.; Carda-Broch, S. J Chro-
matogr A 2008, 1184, 6–18.
International Journal of Chemical Kinetics DOI 10.1002/kin