Optimization of Graphene Oxide’s Characteristics with TOPSIS Using an Automated Decision-Making Process | Journal of Engineering Sciences

Optimization of Graphene Oxide’s Characteristics with TOPSIS Using an Automated Decision-Making Process

Author(s): Javanbakht T.

Affiliation(s): Department of Computer Science, University of Quebec in Montreal, 201 President Kennedy St., Montreal, H2X 3Y7 Quebec, Canada

*Corresponding Author’s Address: [email protected]

Issue: Volume 10, Issue 1 (2023)

Submitted: March 8, 2023
Received in revised form: May 6, 2023
Accepted for publication: May 15, 2023
Available online: May 18, 2023

Javanbakht T. (2023). Optimization of graphene oxide’s characteristics with TOPSIS using an automated decision-making process. Journal of Engineering Sciences, Vol. 10(1), pp. E1-E7, doi: 10.21272/jes.2023.10(1).e1

DOI: 10.21272/jes.2023.10(1).e1

Research Area:  MECHANICAL ENGINEERING: Computational Mechanics

Abstract. The present study focuses on a new application of TOPSIS to predict and optimize graphene oxide’s characteristics. Although this carbon-based material has been investigated previously, its optimization with this method using an automated decision-making process has not been performed yet. The major problem in the design and analysis of this nanomaterial is the lack of information on comparing its characteristics, which has led to the use of diverse methods that have not been appropriately compared. Moreover, their advantages and inconveniences could be investigated better once this investigation provides information on optimizing its candidates. In the current research work, a novel automated decision-making process was used with the TOPSIS algorithm using the Łukasiewicz disjunction, which helped detect the confusion of properties and determine its impact on the rank of candidates. Several characteristics of graphene oxide, such as its antibiofilm activity, hemocompatibility, activity with ferrous ions in hydrogen peroxide, rheological properties, and the cost of its preparation, have been considered in its analysis with TOPSIS. The results of this study revealed that the consideration of the criteria of this nanomaterial as profit or cost criteria would impact the distances of candidates from the alternatives. Moreover, the ranks of the candidates changed when the rheological properties were considered differently in the data analysis. This investigation can help improve the use of this nanomaterial in academic and industrial investigations.

Keywords: process innovation, energy optimization, prediction, TOPSIS, algorithm.


  1. Novoselov, K.S., Geim, A.K. et al. (2004). Electric field effect in automatically thin carbon films, Science, vol. 306(5696), pp. 666-669. https://doi. org/10.1126/science.1102896.
  2. Perrozzi, F. et al. (2015). Graphene oxide: from fundamentals to applications, J. Phys.: Condens. Matter, vol. 27, 013002. https://doi. Org/10.1088/0953-8984/27/1/013002.
  3. Su, C.-Y., Xu, Y., Zhang, W., Zhao, J., Tang, X., Tsai, C.-H., Li, L.-J. (2009). Electrical and spectroscopic characterizations of ultra-large reduced graphene oxide monolayers, Chem. Mater., vol. 21, pp. 5674-80. https://doi.org/10.1021/cm902182y.
  4. Piñas, J.A.V. et al. (2019). Production of reduced graphene oxide platelets from graphite flakes using the Fenton reaction as an alternative to harmful oxidizing agents, Journal of Nanomaterials, 736563.
  5. Shim, Y.H. et al. (2018). Tailored colloidal stability and rheological properties of graphene oxide liquid crystals with polymer-induced depletion attractions, ACS Nano, vol. 12(11), pp. 11399-11406.
  6. Javanbakht, T., Hadian, H., Wilkinson, K.J. (2020). Comparative study of physicochemical properties and antibiofilm activity of graphene oxide nanoribbons, Journal of Engineering Sciences, vol. 7(1), pp. C1-C8. https://doi. org/10.21272/jes.
  7. Kenry. (2018). Understanding the hemotoxicity of graphene nanomaterials through their interactions with blood proteins and cells, J. Mater. Res., vol. 33(1), pp. 44-57.
  8. Kenry et al. (2015). Molecular hemocompatibility of graphene oxide and its implication for antithrombotic applications, Small, vol. 11(38), pp. 5105-5117. https://europepmc.org/article/med/26237338.
  9. Zhang, X. et al. (2015). Large-area preparation of high-quality and uniform three- dimensional graphene networks through thermal degradation of graphene oxide−nitrocellulose composites, ACS Applied Materials and Interfaces, vol. 7, pp. 1057-1064.
  10. Stankovich, S., Dikin, D.A., Piner, R.D., Kohlhaas, K.A., Kleinhammes, A., Jia, Y., Wu, Y., Nguyen, S.T., Ruoff, R.S. (2007).  Synthesis of graphene-based nanosheets via chemical reduction of exfoliated graphite oxide, Carbon, vol. 45, pp. 1558-65. https://doi.org/10.1016/j.carbon.2007.02.034.
  11. Jung, I., Vaupel, M., Pelton, M., Piner, R., Dikin, D.A., Stankovich, S., An, J., Ruoff, R.S. (2008). Characterization of thermally reduced graphene oxide by imaging ellipsometry, J. Phys. Chem. C, vol. 112, pp. 8499-506. https://doi.org/10.1021/jp802173m.
  12. Gupta, A., Chen, G., Joshi, P., Tadigadapa, S. Eklund, P.C. (2006). Raman scattering from high-frequency phonons in supported n-graphene layer films, Nano lett., vol. 6, pp. 2667-73. https://doi.org/10.1021/nl061420a.
  13. Sałabun, W., Wątróbski, J., Shekhovtsov, A. (2020). Are MCDA methods benchmarkable? A comparative study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II methods. Symmetry, vol. 12(9), 1549. https://doi.org/10.3390/sym12091549.
  14. Hsu, L.-C. (2013). Investment decision making using a combined factor analysis and entropy-based TOPSIS model, Journal of Business Economics and Management, vol. 14(3), pp. 448-466. https://doi.org/10.3846/16111699.2011.633098.
  15. Bulgurcu, B. (2012). Application of TOPSIS technique for financial performance evaluation of technology firms in Istanbul stock exchange market. Procedia, vol. 62, pp. 1033-1040. https://doi.org/10.1016/j.sbspro.2012.09.176.
  16. Kochkina, M. V., Karamyshev, A. N., Isavnin, A. G. (2017). Modified multi-criteria decision making method development based on “AHP” and “TOPSIS” methods using probabilistic interval estimates. The Turkish Online Journal of Design, Art and Communication TOJDAC, pp. 1663-1674. https://doi.org/10.7456/1070DSE/144.
  17. Abidin, M. Z., Rusli, R., Shariff, A. M. (2016). Technique for order performance by similarity to ideal solution (TOPSIS)- entropy methodology for inherent safety design decision making tool, Procedia Engineering, vol. 148, pp. 1043-1050. https://doi.org/10.1016/j.proeng.2016.06.587.
  18. Azari, A. et al. (2022). Integrated fuzzy AHP-TOPSIS for selecting the best color removal process using carbon-based adsorbent materials: multi-criteria decision making vs. systematic review approaches and modeling of textile wastewater treatment in real conditions, International Journal of Environmental Analytical Chemistry, vol. 102(18), pp. 7329-7344.
  19. Şimşek, B. et al. (2018). Improvement of the graphene oxide dispersion properties with the use of TOPSIS based Taguchi application, Periodica Polytechnica Chemical Engineering, vol. 62(3), pp. 323-335. https://doi.org/10.3311/Ppch.11412.
  20. Awate, P.P., Barvem S.B. (2022). Graphene/Al6061 nanocomposite selection using TOPSIS and EXPROM2 multi-criteria decision-making methods, Materials Today: Proceedings, vol. 62(2). https://doi.org/10.1016/j.matpr.2022.04.069.
  21. Korucu, H. et al. (2018). A TOPSIS-based Taguchi design to investigate optimum mixture proportions of graphene oxide powder synthesized by Hummers method, Arabian Journal for Science and Engineering, vol. 43, pp. 6033-6055. https://doi.org/10.1007/s13369-018-3184-4.
  22. Korucu, H. (2022). Evaluation of the performance on reduced graphene oxide synthesized using ascorbic acid and sodium borohydride: Experimental designs‐based multi‐response optimization application, Journal of Molecular Structure, vol. 1268, 133715. https://doi.org/10.1016/j.molstruc.2022.133715.
  23. Kobryń, A., Prystrom, J. (2016). A data pre-processing model for TOPSIS method, Folia Oeconomica Stetinensia, vol. 16(2), pp. 219-235. https://doi.org/10.1515/foli-2016-0036.
  24. Shekhovtsov, A., Sałabun, W. (2020). A comparative case study of the VIKOR and TOPSIS rankings similarity, Procedia Computer Science, vol. 176, pp. 3730-3740. https://doi.org/10.1016/j.procs.2020.09.014.
  25. Djavanbakht T, Carrier V, André JM, Barchewitz R, Troussel P. (2000). Effets d’un chauffage thermique sur les performances de miroirs multicouches Mo/Si, Mo/C et Ni/C pour le rayonnement X mou, Journal de Physique IV, France, vol. 10, pp. 281-287. https://doi.org/10.1051/jp4:20001031.
  26. Čitaković, N.M. (2019). Physical properties of nanomaterials, Military Technical Courier, vol. 67(1), pp. 159-171. https://doi.org/10.5937/vojtehg67-18251.
  27. Bhawani, E. et al. (2020). Investigation on the synthesis and chemical properties of nanomaterials, International Research Journal on Advanced Science Hub, vol. 2(12), pp. 41-47. https://doi.org/10.47392/irjash.2020.246.
  28. Javanbakht, T., Laurent, S., Stanicki, D., Frenette, M. (2020). Correlation between physicochemical properties of superparamagnetic iron oxide nanoparticles and their reactivity with hydrogen peroxide, Canadian Journal of Chemistry, Vol. 98(10), pp. 601-608. https://doi.org/10.1139/cjc-2020-0087.
  29. Radu, N.N. et al. (2009). Biological properties of nanomaterials based on irridoidic compounds, Proceedings of the International Society for Optical Engineering, 7403. https://doi.org/10.1117/12.828875.
  30. Javanbakht, T., Ghane-Motlagh, B., Sawan, M. (2020). Comparative study of antibiofilm activity and physicochemical properties of microelectrode arrays, Microelectronic Engineering, vol. 229, 111305. https://doi.org/10.1016/j.mee.2020.111305.
  31. Mehrabian, M. et al. (2021). Simulating the thickness effect of the graphene oxide layer in CsPbBr3– based solar cells, Materials Research Express, 035509. http://doi.org/10.1088/2053-1591/abf080.
  32. Kwon, S. et al. (2018). The effect of thickness and chemical reduction of graphene oxide on nanoscale friction, J. Phys. Chem. B, vol. 122(2), pp. 543-547. https://doi.org/10.1021/acs.jpcb.7b04609.
  33. Gacka, E. (2021). Effect of graphene oxide flakes size and number of layers on photocatalytic hydrogen production, Scientific Reports, vol. 11, 15969. http://doi.org/10.1038/s41598-021-95464-y.

Full Text

© 2014-2024 Sumy State University
"Journal of Engineering Sciences"
ISSN 2312-2498 (Print), ISSN 2414-9381 (Online).
All rights are reserved by SumDU