Quantitative structure-property relationship model leading to virtual screening of fullerene derivatives: Exploring structural attributes critical for photoconversion efficiency of polymer solar cell acceptors

Nano Energy

Volumn 26, Issue , 2016, Pages 677-691

  Kar, Supratik ^a   Sizochenko, Natalia ^a   Ahmed, Lucky ^b   Batista, Victor S ^b   Leszczynski, Jerzy ^a  

^a JACKSON STATE UNIVERSITY   (United States)

^b YALE UNIVERSITY   (United States)

Author keywords

Fullerene; Photovoltaics; Polymer solar cells; Power conversion efficiency; QSPR; Virtual screening

Indexed keywords

CONVERSION EFFICIENCY; EFFICIENCY; FULLERENES; POLYMERS; SOLAR CELLS; SOLAR POWER GENERATION; TOXIC MATERIALS;

FULLERENE DERIVATIVE; PHOTOCONVERSION EFFICIENCY; PHOTOVOLTAICS; POLY-3-HEXYLTHIOPHENE; POWER CONVERSION EFFICIENCIES; QSPR; QUANTITATIVE STRUCTURE-PROPERTY RELATIONSHIP MODELS; VIRTUAL SCREENING;

POLYMER SOLAR CELLS;

EID: 84978399751     PISSN: 22112855     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.nanoen.2016.06.011     Document Type: Article

References (38)

1. European Commission, Regulation (EC) (No. 1907/2006) of the European Parliament and of the Council of 18 December 2006 Concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), Establishing a European Chemicals Agency, 2006.
2. Hoppe H., Sariciftci N.S. Photoresponsive Polymers II. Advances in Polymer Science 2008, 214:1-86. Springer-Verlag, Berlin.
3. Thompson B.C., Frechet J.M.J. Angew. Chem. Int. Ed. 2008, 47:58-77.
4. Li G., Shrotriya V., Huang J., Yao Y., Moriarty T., Emery K., Yang Y. Nat. Mater. 2005, 4:864-868.
5. National Renewable Energy Lab and U.S. Department of Energy, Sunshot Vision Study, Feb. 2012, pp. 4-5 〈〉. http://energy.gov/eere/sunshot/sunshot-vision-study.
6. Dennler G., Scharber M.C., Brabec C.J. Adv. Mater. 2009, 21:1323-1338.
7. Delgado J.L., Bouit P.A., Filippone S., Herranzand M.A., Martin N. Chem. Commun. 2010, 46:4853-4865.
8. He Y., Li Y. Phys. Chem. Chem. Phys. 2011, 13:1970-1983.
9. Cheng Y.-J., Yang S.-H., Hsu C.-S. Chem. Rev. 2009, 109:5868-5923.
10. Yu G., Gao J., Hummelen J.C., Wudl F., Heeger A.J. Science 1995, 270:1789-1791.
11. Seri M., Rossi E., Carofiglio T., Antonello S., Ruani G., Magginiand M., Muccini M. J. Mater. Chem. 2011, 21:18308-18316.
12. Ma W., Yang C., Gong X., Lee K., Heeger A.J. Adv. Funct. Mater. 2005, 15:1617.
13. Roy K., Kar S., Das R.N. Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment 2015, 1-478. Academic Press, USA.
14. Pourbasheer E., Banaei A., Aalizadeh R., Ganjali M.R., Norouzi P., Shadmanesh J., Methenitis C. J. Ind. Eng. Chem. 2015, 21:1058-1067.
15. OECD Document, Guidance Document on the Validation of (Quantitative)1226 Structure-Activity Relationships [(Q)SARs] Models, ENV/JM/MONO(2007)2, 2007.
16. Troshin P.A., Hoppe H., Renz J., Egginger M., Mayorova J.Y., Goryachev A.E., Peregudov A.S., Lyubovskaya R.N., Gobsch G., Sariciftci N.S., Razumov V.F. Adv. Funct. Mater. 2009, 19:779-788.
17. Liu J., Guo X., Qin Y., Liang S., Guo Z.-X., Li Y. J. Mater. Chem. 2012, 22:1758-1761.
18. HyperChem(TM), 1115 NW 4th Street, Gainesville, Florida 32601, USA.
19. Zhao Y., Truhlar D. Theor. Chem. Acc. 2008, 120:215-241.
20. Rassolov V.A., Ratner M.A., Pople J.A., Redfern P.C., Curtiss L.A. J. Comput. Chem. 2001, 22:976-984.
21. Gaussian 09: M.J. Frisch, G.W. Trucks, H.B. Schlegel, G.E. Scuseria, M.A. Robb, J.R. Cheeseman, G. Scalmani, V. Barone, B. Mennucci, G.A. Petersson, H. Nakatsuji, M. Caricato, X. Li, H.P. Hratchian, A.F. Izmaylov, J. Bloino, G. Zheng, J.L. Sonnenberg, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, J.A. Montgomery, Jr., J.E. Peralta, F. Ogliaro, M. Bearpark, J.J. Heyd, E. Brothers, K.N. Kudin, V.N. Staroverov, R. Kobayashi, J. Normand, K. Raghavachari, A. Rendell, J.C. Burant, S.S. Iyengar, J. Tomasi, M. Cossi, N. Rega, J.M. Millam, M. Klene, J.E. Knox, J.B. Cross, V. Bakken, C. Adamo, J. Jaramillo, R. Gomperts, R.E. Stratmann, O. Yazyev, A.J. Austin, R. Cammi, C. Pomelli, J.W. Ochterski, R.L. Martin, K. Morokuma, V.G. Zakrzewski, G.A. Voth, P. Salvador, J.J. Dannenberg, S. Dapprich, A.D. Daniels, Ö. Farkas, J.B. Foresman, J.V. Ortiz, J. Cioslowski, D.J. Fox, Gaussian Inc., Wallingford CT, 2009.
22. Li X., Frisch M.J. J. Chem. Theor. Comput. 2006, 2:835-839.
23. Ahmed L., Rasulev B., Turabekova M., Leszczynska D., Leszczynski J. Org. Biomol. Chem. 2013, 11:5798-5808.
24. Maestro, version 10.2, Schrödinger, LLC, New York, NY, 2015.
25. 〈〉. https://www.chemaxon.com.
26. 〈〉. http://qsar4u.com/pages/sirms.php.
27. Artemenko A.G., Muratov E.N., Kuz'min V.E., Muratov N.N., Varlamova E.V., Kuz'mina A.V., Gorb L.G., Golius A., Hill F.C., Leszczynski J. SAR QSAR Environ. Res. 2011, 22:575-601.
28. 〈〉. http://teqip.jdvu.ac.in/QSAR_Tools/.
29. Waller C.L., Bradley M.P. J. Chem. Inf. Comput. Sci. 1999, 39:345-355.
30. Roy K., Mitra I., Kar S., Ojha P., Das R.N., Kabir H. J. Chem. Inf. Model. 2012, 52:396-408.
31. Schüürmann G., Ebert R.-U., Chen J., Wang B., Kühne R. J. Chem. Inf. Model. 2008, 48:2140-2145.
32. Golbraikh A., Tropsha A. J. Mol. Graph. Model. 2002, 20:269-276.
33. Kar S., Gajewicz A., Puzyn T., Roy K. Toxicol. In Vitro 2014, 28:600-606.
34. Gramatica P. QSAR Comb. Sci. 2007, 26:694-701.
35. EUCLIDEAN (a program written in Java) is developed and validated on known data sets by PravinAmbure (Email: ambure.pharmait@gmail.com) of Drug Theoretics and Cheminformatics Laboratory, Jadavpur University, 2013.
36. Few S., Frostab J.M., Nelson J. Phys. Chem. Chem. Phys. 2015, 17:2311-2325.
37. van der Hofstad T.G.J., Di Nuzzo D., van den Berg M., Janssen R.A.J., Meskers S.C.J. Adv. Energy Mater. 2012, 2:1095-1099.
38. Kanai Y., Grossman J.C. Nano Lett. 2007, 7:1967-1972.