Chinese Journal of Polymer Science

, Volume 37, Issue 2, pp 157–163 | Cite as

Dynamic Monte Carlo Simulation on Polymerization of Encapsulant

  • Jin ChenEmail author
  • Jiong-Hua Xiang


Based on the preparative experiments of the light-emitting diode (LED) encapsulant, three types of monomer models with different functional groups are carried out to study the polymerization process by dynamic Monte Carlo (DMC) simulation and bond fluctuation model (BFM). We calculate the degree of polymerization, the radius of gyration and the frequency of void spheres to discuss the polymerization process, the molecular size and the spatial distribution at different volume fractions and proportions. Our results are in agreement with Grest’s decay rate and Flory’s scale law. Simulations show that the polymerization process depends on the appropriate volume fraction and proportion exceedingly, and the volume contraction in the polymerization process can also be observed in this study. These investigations could provide some insights into the understanding of the polymerization process of the encapsulant and help us to adjust the parameters in later experiments.


Dynamic Monte Carlo (DMC) simulation Bond fluctuation model (BFM) LED encapsulant 


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  1. 1.
    Patel, P. Solid-state lighting: The future looks bright. MRS Bull. 2011, 36, 678–680.CrossRefGoogle Scholar
  2. 2.
    Tsami, A.; Yang, X. H.; Galbrecht, F.; Farrell, T.; Li, H.; Adamczyk, S.; Heiderhoff, R.; Balk, L. J.; Neher, D.; Holder, E. Random fluorene copolymers with on-chain quinoxaline acceptor units. J. Polym. Sci., Part A: Polym. Chem. 2007, 45, 4773–4785.CrossRefGoogle Scholar
  3. 3.
    Kanelidis, I.; Ren, Y.; Lesnyak, V.; Gasse, J. C.; Frahm, R.; Eychmüller, A.; Holder, E. Arylamino-functionalized fluoreneand carbazole-based copolymers: color-tuning their CdTe nanocrystal composites from red to white. J. Polym. Sci., Part A: Polym. Chem. 2011, 49, 392–402.CrossRefGoogle Scholar
  4. 4.
    Tao, P.; Li, Y.; Siegel, R. W.; Schadler, L. S. Transparent dispensible high-refractive index ZrÜ2/epoxy nanocomposites for LED encapsulation. J. Appl. Polym. Sci. 2013, 130, 3785–3793.CrossRefGoogle Scholar
  5. 5.
    Narendran, N.; Gu, Y.; Freyssinier, J. P.; Yu, H.; Deng, L. Solid-state lighting: failure analysis of white LEDs. J. Cryst. Growth 2004, 268, 449–456.CrossRefGoogle Scholar
  6. 6.
    Narendran, N.; Gu, Y. Life of LED-based white light sources. J. Disp. Technol. 2005, 1, 167–171.CrossRefGoogle Scholar
  7. 7.
    Chhajed, S.; Lee, W.; Cho, J.; Schubert, E. F.; Kim, J. K. Strong light extraction enhancement in GaInN light-emitting diodes by using self-organized nanoscale patterning of p-type GaN. Appl. Phys. Lett. 2011, 98, 071102.CrossRefGoogle Scholar
  8. 8.
    Hsu, C. W.; Ma, C. C. M.; Tan, C. S.; Li, H. T.; Huang, S. C.; Lee, T. M.; Tai, H. Effect of thermal aging on the optical, dynamic mechanical, and morphological properties of phenylmethylsiloxane-modified epoxy for use as an LED encapsulant. Mater. Chem. Phys. 2012, 134, 789–796.CrossRefGoogle Scholar
  9. 9.
    Huang, W.; Zhang, Y.; Yu, Y.; Yuan, Y. Studies on UV-stable silicone-epoxy resins. J. Appl. Polym. Sci. 2007, 104, 3954–3959.CrossRefGoogle Scholar
  10. 10.
    Bourget, L.; Corriu, R. J. P.; Leclercq, D.; Mutin, P. H.; Vioux, A. Non-hydrolytic sol-gel routes to silica. J. Non-Cryst. Solids 1998, 242, 81–91.CrossRefGoogle Scholar
  11. 11.
    Chung, P. T.; Yang, C. T.; Wang, S. H.; Chen, C. W.; Chiang, A. S. T.; Liu, C. Y. ZrO2/epoxy nanocomposite for LED encapsulation. Mater. Chem. Phys. 2012, 136, 868–876.CrossRefGoogle Scholar
  12. 12.
    Katayama, S.; Yamada, N.; Shibata, Y.; Noda, K. Fabrication and properties of PDMDPS-based inorganic/organic hybrid sheets. J. Ceram. Soc. Japan 2003, 111, 391.CrossRefGoogle Scholar
  13. 13.
    Mosley, D. W.; Khanarian, G.; Conner, D. M.; Thorsen, D. L.; Zhang, T.; Wills, M. High refractive index thermally stable phenoxyphenyl and phenylthiophenyl silicones for light-emitting diode applications. J. Appl. Polym. Sci. 2014, 131, 39824.CrossRefGoogle Scholar
  14. 14.
    Kim, J. S.; Yang, S. C.; Kwak, S. Y.; Choi, Y.; Paik, K. W.; Bae, B. S. High performance encapsulant for light-emitting diodes (LEDs) by a sol-gel derived hydrogen siloxane hybrid. J. Mater. Chem. 2012, 22, 7954–7960.CrossRefGoogle Scholar
  15. 15.
    Kim, Y. H.; Bae, J. Y.; Jin, H.; Bae, B. S. Sol-gel derived transparent zirconium-phenyl siloxane hybrid for robust high refractive index LED encapsulant. ACS Appl. Mater. Interfaces 2014, 6, 3115–3121.CrossRefGoogle Scholar
  16. 16.
    Leach, A. R. in Molecular modelling: principles and applications. London, Longman, 2001.Google Scholar
  17. 17.
    Landau, D. P.; Binder, K. in A guide to Monte Carlo simulations in statistical physics. Cambridge: Cambridge University, 2000.Google Scholar
  18. 18.
    Newman, M. E. J.; Barkema, G. T. in Monte Carlo methods in statistical physics. Oxford: Clarendon Press, 2000.Google Scholar
  19. 19.
    Bortz, A. B.; Kalos, M. H.; Lebowitz, J. L. A new algorithm for Monte Carlo simulation of Ising spin systems. J. Comput. Phys. 1975, 17, 10–18.CrossRefGoogle Scholar
  20. 20.
    Gillespie, D. T. A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J. Comput. Phys. 1976, 22, 403–434.CrossRefGoogle Scholar
  21. 21.
    Fichthorn, K. A.; Weinberg, W. H. Theoretical foundations of dynamical Monte Carlo simulations. J. Chem. Phys. 1991, 95, 1090–1093.CrossRefGoogle Scholar
  22. 22.
    Carmesin, I.; Kremer, K. The bond fluctuation method: a new effective algorithm for the dynamics of polymers in all spatial dimensions. Macromolecules 1988, 21, 2819–2823.CrossRefGoogle Scholar
  23. 23.
    Deutsch, H. P.; Binder, K. Interdiffusion and selfdiffusion in polymer mixtures: a Monte Carlo study. J. Chem. Phys. 1991, 94, 2294–2304.CrossRefGoogle Scholar
  24. 24.
    Paul, W.; Binder, K.; Heermann, D. W.; Kremer, K. Crossover scaling in semidilute polymer solutions: a Monte Carlo test. J. Phys. II 1991, 1, 37–60.Google Scholar
  25. 25.
    Wittkop, M.; Kreitmeier, S.; Goritz, D. The collapse transition of a single polymer chain in two and three dimensions: A Monte Carlo study. J. Chem. Phys. 1996, 104, 3373–3385.CrossRefGoogle Scholar
  26. 26.
    Wilding, N. B.; Muller, M.; Binder, K. Chain length dependence of the polymer-solvent critical point parameters. J. Chem. Phys. 1996, 105, 802–809.CrossRefGoogle Scholar
  27. 27.
    Shu, R. F.; Zha, L. Y.; Eman, A. A.; Hu, W. B. Fibril crystal growth in diblock copolymer solutions studied by dynamic Monte Carlo simulations. J. Phys. Chem. B 2015, 119, 5926–5932.CrossRefGoogle Scholar
  28. 28.
    Dasmahapatra, A. K.. Effect of composition asymmetry on the phase separation and crystallization in double crystalline binary polymer blends: A dynamic Monte Carlo simulation study. J. Phys. Chem. B 2017, 121, 5853–5866.Google Scholar
  29. 29.
    Chen, C. M.; Higgs, P. G. Monte-Carlo simulations of polymer crystallization in dilute solution. J. Chem. Phys. 1998, 108, 4305–4314.CrossRefGoogle Scholar
  30. 30.
    Grest, G. S.; Kremer, K.; Duering, E. R. Kinetics of end crosslinking in dense polymer melts. Europhys. Lett. 1992, 19, 195.CrossRefGoogle Scholar
  31. 31.
    Binder, K.; Heermann, D. W. in Monte Carlo simulation in statistical physics: An introduction. 3rd ed.; Springer-Verlag: Berlin, 1997.CrossRefGoogle Scholar
  32. 32.
    De Gennes, P. G. in Scaling concept in polymer physics. Cornell University Press, 1979.Google Scholar

Copyright information

© Chinese Chemical Society, Institute of Chemistry, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Physics, Research Institute for Soft Matter and BiomineticsXiamen UniversityXiamenChina
  2. 2.Department of Chemical EngineeringShanghai Jiao Tong UniversityShanghaiChina

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