An integrated model for predicting the size of silver nanoparticles in montmorillonite/chitosan bionanocomposites: A hybrid of data envelopment analysis and genetic programming approach

Document Type : Article

Authors

Department of Materials Science and Engineering, Shahid Bahonar University of Kerman, Kerman, P.O. Box 76135-133, Iran

Abstract

Unique chemical and physical properties of silver nanoparticles (AgNPs) enhances its usages in various categories such as medical utilities. Due to the high dependency of AgNPs properties to the size, this study is an attempt to employ gene expression programing (GEP) for constructing a quantitative model for estimating the size of AgNPs in montmorillonite/chitosan bionanocomposites that prepared by chemical approach. Generalization capabilities, fault tolerance, noise tolerance, high parallelism, nonlinearity and significant information processing characteristics are the main advantages of GEP. Accordingly, the practical parameters including reaction temperature, AgNO3 concentration, weight of montmorillonite in aqueous AgNO3/chitosan solution (WMMT) and the percentage of chitosan are selected as input parameters through GEP modeling. The accuracy of proposed models are investigated by statistical indicators including mean absolute percentage error (MAPE), root relative squared error (RRSE), root mean square error (RMSE) and correlation coefficient (R2). Finally, the best model is selected by R2 = 0.987, RMSE = 0.100, RRSE = 0.146 and MAPE = 0.221. The sensitivity analysis confirmed that the percentage of chitosan, concentration of AgNO3, WMMT and reaction temperature are the most effecting parameters on the size of AgNPs, respectively.

Keywords


References:
 1. Shabanzadeh, P., Yusof, R. and Shameli, K. "Artificial neural network for modeling the size of silver nanoparticles’ prepared in montmorillonite/starch bionanocomposites", Journal of Industrial and Engineering Chemistry, 24, pp. 42-50 (2013).
2. Veisi, H., Azizi, S. and Mohammadi, P., "Green synthesis of the silver nanoparticles mediated by Thymbra spicata extract and its application as a heterogeneous and recyclable nanocatalyst for catalytic reduction of a variety of dyes in water", Journal of Cleaner Production, 170:, pp. 1536-1543 (2016). 
3. Rai, M., Yadav, A. and Gade, A., "Silver nanoparticles as a new generation of antimicrobials". Biotechnology advances, 27(1): pp. 76-83 (2009). 
4. Shameli, K., et al., "Green biosynthesis of silver nanoparticles using Callicarpa maingayi stem bark extraction", Molecules, 17(7), pp. 8506-8517 (2012). 
5. Shameli, K., et al., "Synthesis of silver/montmorillonite nanocomposites using γ-irradiation", International journal of nanomedicine, 5, pp. 1067 (2010). 
6. Makwana, D., et al., "Characterization of Agar-CMC/Ag-MMT nanocomposite and evaluation of antibacterial and mechanical properties for packaging applications", Arabian Journal of Chemistry, 13(1), pp. 3092-3099 (2018). 
7. Lavorgna, M., et al., "MMT-supported Ag nanoparticles for chitosan nanocomposites: structural properties and antibacterial activity", Carbohydrate polymers, 102: pp. 385-392 (2014). 
8. Usman, M.S., et al., "Copper nanoparticles mediated by chitosan: synthesis and characterization via chemical methods", Molecules, 17(12): pp. 14928-14936 (2012). 
9. Shameli, K., et al., "Synthesis and characterization of silver/montmorillonite/chitosan bionanocomposites by chemical reduction method and their antibacterial activity". International journal of nanomedicine, 6: pp. 271 (2011). 
10. Ahmad, M.B., et al., "Antibacterial effect of silver nanoparticles prepared in bipolymers at moderate temperature", Research on Chemical Intermediates, 40(2): pp. 817-832 (2014).
11. Shameli, K., et al., "Green synthesis of silver/montmorillonite/chitosan bionanocomposites using the UV irradiation method and evaluation of antibacterial activity", International journal of nanomedicine, 5, pp. 5:875 (2010). 
12. Jafari, M.M. and Khayati, G.R., "Prediction of hydroxyapatite crystallite size prepared by sol–gel route: gene expression programming approach", Journal of Sol-Gel Science and Technology, 86(1), pp. 112-125 (2018). Ebrahimzade, H., Khayati, G.R. and Schaffie, M., "A novel predictive model for estimation of cobalt leaching from waste Li-ion batteries: Application of genetic programming for design", Journal of Environmental Chemical Engineering, 6(4), pp. 3999-4007 (2018) 
13. Shabanzadeh, P., et al., "Prediction of silver nanoparticles’ diameter in montmorillonite/chitosan bionanocomposites by using artificial neural networks", Research on Chemical Intermediates, 41(5), pp. 3275-3287 (2015). 
14. Khayati, G.R., "Adaptive neuro-fuzzy inference system and neural network in predicting the size of monodisperse silica and process optimization via simulated annealing algorithm", Journal of Ultrafine Grained and Nanostructured Materials, 51(1): pp. 43-52 (2018). 
15. Faradonbeh, R.S. and Monjezi, M., "Prediction and minimization of blast-induced ground vibration using two robust meta-heuristic algorithms", Engineering with Computers, 33(4): pp. 835-851 (2017). 
16. Sun, M., "Prediction of viscosity of branched alkanes using gene expression programing."Petroleum Science and Technology, 36(23): pp. 2049-2056 (2018). 
17. Tiryaki, B., "Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees", Engineering Geology, 99(1-2): pp. 51-60 (2008). 
18. Sayadi, A.R., Khalesi, M.R. and Borji, M.K., "A parametric cost model for mineral grinding mills". Minerals Engineering, 55, pp. 96-102 (2014). 
19. Sayadi, A.R., Lashgari, A. and Paraszczak, J.J., "Hard-rock LHD cost estimation using single and multiple regressions based on principal component analysis", Tunnelling and underground space technology, 27(1), pp. 133-141 (2012). 
20. Kaiser, H.F., "An index of factorial simplicity", Psychometrika, 39(1), pp. 31-36 (1974). 
21. Alanni, R., et al., "New gene selection method using gene expression programing approach on microarray data sets." International Conference on Computer and Information Science. Springer, Cham, (2018). 
22. Dikmen, E., "Gene expression programming strategy for estimation performance of LiBr–H 2 O absorption cooling system." Neural Computing and Applications, 26(2), pp.409-415 (2015). 
23. Steeb, W.-H., "The Nonlinear Workbook: Chaos, Fractals, Cellular Automata, Neural Networks, Genetic Algorithms, Fuzzy Logic with C++, Java, Symbolicc++ and Reduce Programs". World Scientific, (2001).
24. Brownlee, J., "Clever algorithms: nature-inspired programming recipes", Jason Brownlee, 2011. 
25. Monjezi, M., et al., "Modification and prediction of blast-induced ground vibrations based on both empirical and computational techniques". Engineering with Computers, 32(4), pp. 717-728 (2016). 
26. Faradonbeh, R.S., et al., "Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction". International journal of environmental science and technology, 16(6), pp. 1453-1464 (2016). 
27. Khandelwal, M., et al., "A new model based on gene expression programming to estimate air flow in a single rock joint", Environmental Earth Sciences, 75(9), pp. 739 (2016). 
28. Aval, S. B., Ketabdari, H. and Gharebaghi, S. A., "Estimating shear strength of short rectangular reinforced concrete columns using nonlinear regression and gene expression programming", Structures, Elsevier, 12: (2017). 
29. Ferreira, C., "Gene expression programming: mathematical modeling by an artificial intelligence", Springer, 21, (2006). 30. Jafari, M. M., and Khayati, G. R., "Prediction of hydroxyapatite crystallite size prepared by sol–gel route: gene expression programming approach." Journal of Sol-Gel Science and Technology, 86(1), pp. 112-125 (2018). 
31. Zhong, J., Feng, L., and Ong, Y. S., "Gene expression programming: A survey." IEEE Computational Intelligence Magazine, 12(3), pp. 54-72 (2017). 
32. Faradonbeh, R.S., "Monjezi, M. and Armaghani, D.J., Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation", Engineering with Computers, 
32(1), pp. 123-133 (2016). 
33. Baykasoğlu, A., et al., "Prediction of compressive and tensile strength of limestone via genetic programming". Expert Systems with Applications, 35(1-2), pp. 111-123 (2008). 
34. Ferreira, C. and Gepsoft, U., "What is gene expression programming". (2008). 
35. Sarıdemir, M., "Effect of specimen size and shape on compressive strength of concrete containing fly ash: Application of genetic programming for design", Materials & Design (1980-2015), 56, pp. 297-304 (2014). 
36. Jafari, S., and Mahini, S. S., "Lightweight concrete design using gene expression programing", Construction and Building Materials 139, pp. 93-100 (2017). 
37. Fallahpour, A., et al., "An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach", Neural Computing and Applications, 27(3), pp. 707-725 (2016). 
38. Khozani, Z. S., Bonakdari, S., and Ebtehaj, I., "An analysis of shear stress distribution in circular channels with sediment deposition based on Gene Expression Programming." International Journal of Sediment Research, 32(4), pp. 575-584 (2017).
39. Hoseinian, F.S., et al., "Semi-autogenous mill power model development using gene expression programming", Powder Technology, 308, pp. 61-69 (2017).