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Temporal Growth and Aging of ZnO Nanoparticles in Colloidal Solution: Phase Field Model

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Abstract

The temporal growth and aging of ZnO nanoparticles (NP's) in colloidal solution were investigated both experimentally and theoretically. UV–Vis spectroscopy revealed that the nucleation and growth of NP’s in solution occurs in less than 2 min. Transmission electron microscopy images depict the morphology of aggregated NP’s. In atomically balanced reaction (for sample S1), first growth takes place and then aging were observed. However, in the case of the atomically unbalanced reaction (for sample S2), decoupling of nucleation from growth was seen after 20 min. This result was confirmed by the slopes of dEg/dt (Eg = band gap) and dαmax/dt (αmax = absorption maximum) with time, which remains constant for sample S1 but shows abrupt decrease for sample S2 after 20 min. Thereafter, growth was found to be controlled by the diffusion and reaction parameters. The growth of NP’s was modelled using the phase-field model. The result from the current work reveals that the nucleation, growth and aging of NP’s occur in the atomically balanced reaction whereas decoupling of nucleation from growth happens in atomically unbalanced reaction.

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References

  1. J. Zhang, C. Li, Y. Zhang, M. Yang, D. Jia, G. Liu, Y. Hou, R. Li, N. Zhang, Q. Wu, and H. Cao (2018). J. Clean. Prod. 193, 236–248. https://doi.org/10.1016/j.jclepro.2018.05.009.

    Article  CAS  Google Scholar 

  2. M. Yang, C. Li, L. Luo, R. Li, and Y. Long (2021). Int. Commun. Heat Mass Transf. 125, 105317. https://doi.org/10.1016/j.icheatmasstransfer.2021.105317.

    Article  CAS  Google Scholar 

  3. M. Yang, C. Li, Y. Zhang, D. Jia, R. Li, Y. Hou, and H. Cao (2019). Int. J. Adv. Manuf. Technol. 102, 2617–2632. https://doi.org/10.1007/s00170-019-03367-0.

    Article  Google Scholar 

  4. M. Yang, C. Li, Y. Zhang, D. Jia, X. Zhang, Y. Hou, R. Li, and J. Wang (2017). Int. J. Mach. Tools Manuf. 122, 55–65. https://doi.org/10.1016/j.ijmachtools.2017.06.003.

    Article  Google Scholar 

  5. S. Guo, C. Li, Y. Zhang, Y. Wang, B. Li, M. Yang, X. Zhang, and G. Liu (2017). J. Clean. Prod. 140, 1060–1076. https://doi.org/10.1016/j.jclepro.2016.10.073.

    Article  CAS  Google Scholar 

  6. M. Yang, C. Li, Y. Zhang, D. Jia, R. Li, Y. Hou, H. Cao, and J. Wang (2019). Ceram. Int. 45, 14908–14920. https://doi.org/10.1016/j.ceramint.2019.04.226.

    Article  CAS  Google Scholar 

  7. B. Li, C. Li, Y. Zhang, Y. Wang, D. Jia, and M. Yang (2016). Chin. J. Aeronaut. 29, 1084–1095. https://doi.org/10.1016/j.cja.2015.10.012.

    Article  Google Scholar 

  8. K. Kaviyarasu, N. Geetha, K. Kanimozhi, C. Maria Magdalane, S. Sivaranjani, A. Ayeshamariam, J. Kennedy, and M. Maaza (2017). Mater. Sci. Eng. C. 74, 325–333. https://doi.org/10.1016/j.msec.2016.12.024.

    Article  CAS  Google Scholar 

  9. K. Kaviyarasu, C. Maria Magdalane, K. Kanimozhi, J. Kennedy, B. Siddhardha, E. Subba Reddy, N. K. Rotte, C. S. Sharma, F. T. Thema, D. Letsholathebe, G. T. Mola, and M. Maaza (2017). J. Photochem. Photobiol. B Biol. 173, 466–475. https://doi.org/10.1016/j.jphotobiol.2017.06.026.

    Article  CAS  Google Scholar 

  10. J. Kennedy, A. Markwitz, Z. Li, W. Gao, C. Kendrick, S. M. Durbin, and R. Reeves (2006). Curr. Appl. Phys. 6, 495–498. https://doi.org/10.1016/j.cap.2005.11.046.

    Article  Google Scholar 

  11. J. Kennedy, P. P. Murmu, E. Manikandan, and S. Y. Lee (2014). J. Alloys Compd. 616, 614–617. https://doi.org/10.1016/j.jallcom.2014.07.179.

    Article  CAS  Google Scholar 

  12. J. Kennedy, P. P. Murmu, J. Leveneur, A. Markwitz, and J. Futter (2016). Appl. Surf. Sci. 367, 52–58. https://doi.org/10.1016/j.apsusc.2016.01.160.

    Article  CAS  Google Scholar 

  13. K. Davis, R. Yarbrough, M. Froeschle, J. White, and H. Rathnayake (2019). RSC Adv. 9, 14638–14648. https://doi.org/10.1039/c9ra02091h.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. T. E. P. Alves, C. Kolodziej, C. Burda, and A. Franco (2018). Mater. Des. 146, 125–133. https://doi.org/10.1016/j.matdes.2018.03.013.

    Article  CAS  Google Scholar 

  15. T. Udayabhaskararao, M. Kazes, L. Houben, H. Lin, and D. Oron (2017). Chem. Mater. 29, 1302–1308. https://doi.org/10.1021/acs.chemmater.6b04841.

    Article  CAS  Google Scholar 

  16. M. Herbst and E. Hofmann (2019). ACS 35, 11702–11709. https://doi.org/10.1021/acs.langmuir.9b01149.

    Article  CAS  Google Scholar 

  17. P. Montero De Hijes, J. R. Espinosa, E. Sanz, and C. Vega (2019). J. Chem. Phys. 151, 144501. https://doi.org/10.1063/1.5121026.

    Article  CAS  PubMed  Google Scholar 

  18. L. Qu, W. W. Yu, and X. Peng (2004). Nano Lett. 4, 465–469. https://doi.org/10.1021/nl035211r.

    Article  CAS  Google Scholar 

  19. E. C. Vreeland, J. Watt, G. B. Schober, B. G. Hance, M. J. Austin, A. D. Price, B. D. Fellows, T. C. Monson, N. S. Hudak, L. Maldonado-Camargo, A. C. Bohorquez, C. Rinaldi, and D. L. Huber (2015). Chem. Mater. 27, 6059–6066. https://doi.org/10.1021/acs.chemmater.5b02510.

    Article  CAS  Google Scholar 

  20. G. S. Redner, C. G. Wagner, A. Baskaran, and M. F. Hagan (2016). Phys. Rev. Lett. 117, 1–7. https://doi.org/10.1103/PhysRevLett.117.148002.

    Article  CAS  Google Scholar 

  21. D. V. Alexandrov and I. V. Alexandrova (2020). Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 378, 20190247. https://doi.org/10.1098/rsta.2019.0247.

    Article  Google Scholar 

  22. M. Yang and L. Wang (2021). Npj Comput Mater. https://doi.org/10.1038/s41524-021-00524-6.

    Article  Google Scholar 

  23. L. Gránásy, F. Podmaniczky, G. I. Tóth, G. Tegze, and T. Puszta (2014). Chem. Soc. Rev. 43, 2159–2173. https://doi.org/10.1039/C3CS60225G.

    Article  PubMed  Google Scholar 

  24. K. Chockalingam, V. G. Kouznetsova, O. Van Der Sluis, and M. G. D. Geers (2016). Comput. Methods Appl. Mech. Engrg. 312, 492–508. https://doi.org/10.1016/j.cma.2016.07.002.

    Article  Google Scholar 

  25. T. Q. Ansari (2021). Npj Comput Mater. https://doi.org/10.1038/s41524-021-00612-7.

    Article  Google Scholar 

  26. A. G. Vega-Poot, G. Rodríguez-Gattorno, O. E. Soberanis-Domínguez, R. T. Patiño-Díaz, M. Espinosa-Pesqueira, and G. Oskam (2010). Nanoscale. 2, 2710–2717. https://doi.org/10.1039/c0nr00439a.

    Article  CAS  PubMed  Google Scholar 

  27. C. Lizandara-Pueyo, M. W. E. Van Den Berg, A. De Toni, T. Goes, and S. Polarz (2008). J. Am. Chem. Soc. 130, 16601–16610. https://doi.org/10.1021/ja804071h.

    Article  CAS  PubMed  Google Scholar 

  28. S. Repp and E. Erdem (2016). Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 152, 637–644. https://doi.org/10.1016/j.saa.2015.01.110.

    Article  CAS  Google Scholar 

  29. D. Valiente, S. F. de Avila, F. Rodríguez-Mas, J. C. Ferrer, and J. L. Alonso (2020). Cryst. MDPI. 10, 226. https://doi.org/10.3390/cryst10030226.

    Article  CAS  Google Scholar 

  30. A. V. Shapovalov and V. V. Obukhov (2018). Symmetry (Basel). 10, 53. https://doi.org/10.3390/SYM10030053.

    Article  Google Scholar 

  31. C. Valla (2017). Nonlinear Anal. Real World Appl. 36, 249–266. https://doi.org/10.1016/j.nonrwa.2017.01.013.

    Article  Google Scholar 

  32. S. Meng, A. Zhang, Z. Guo, and Q. Wang (2020). Comput. Mater. Sci. 184, 109784. https://doi.org/10.1016/j.commatsci.2020.109784.

    Article  CAS  Google Scholar 

  33. S. Sakane, T. Takaki, R. Rojas, M. Ohno, Y. Shibuta, T. Shimokawabe, and T. Aoki (2017). J. Cryst. Growth. 474, 154–159. https://doi.org/10.1016/j.jcrysgro.2016.11.103.

    Article  CAS  Google Scholar 

  34. X. Yang and J. Zhao (2019). Comput. Phys. Commun. 235, 234–245. https://doi.org/10.1016/j.cpc.2018.08.012.

    Article  CAS  Google Scholar 

  35. S. B. Biner, Programming Phase-Field Modeling. (Springer International Publishing, Cham, 2017), pp. 156–168. https://doi.org/10.1007/978-3-319-41196-5. .

    Book  Google Scholar 

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Acknowledgements

The authors would like to thank the Jaypee University of Information Technology, Waknaghat, Solan, Himachal Pradesh, India for financial and infrastructural support for current work.

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Correspondence to Priyanka Sharma.

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Sharma, P., Tiwari, S.K. & Barman, P.B. Temporal Growth and Aging of ZnO Nanoparticles in Colloidal Solution: Phase Field Model. J Clust Sci 34, 1381–1389 (2023). https://doi.org/10.1007/s10876-022-02309-3

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