Synthesis of oxides’ nanoparticles to produce aqueous solutions for antimicrobial applications


  • Mustafa Hashium Education college, Mustansiriya University
  • Reem Shadi Khaleel Physics Department, Education College, Mustansiriya University, Baghdad, Iraq
  • Dalal Mseer Naser Physics Department, Education College, Mustansiriya University, Baghdad, Iraq


A novel method was used to transform pure metals to antimicrobial solutions by production of ZnO, Cu2O, MgO and TiO2 nanoparticles using rapid breakdown anodization (RBA) technique. The oxides’ nanoparticles were converted to their acetates by chemical reaction with acetic acid. After synthesize the acetates’ crystals they were dissolved in water to yield aqueous solutions. To evaluate the antibacterial activity of these solutions against pathogenic bacteria their inhibition zones were measured.  X ray diffraction (XRD) technique and scanning electron microscope (SEM) were utilized to characterize these oxides. Before transforming to their acetates all mixed synthesized nanoparticles with deionized water did not have antibacterial activities but after transforming process Copper and Zinc acetates’ solutions had inhibition zones. Against S. aureus , S. epidermidis, Escherichia coli, Klebsiella pneumoniae  and Candida albicans the inhibition zones for Copper acetate solution  were 21, 19, 22, 22 and 30 mm respectively. For ZnO acetate solution these zones were 26, 25, 0, 0 and 14 mm respectively. There were no antibacterial activities recorded for both Titanium and Magnesium acetates’ solutions.  

Author Biographies

Reem Shadi Khaleel, Physics Department, Education College, Mustansiriya University, Baghdad, Iraq

Physics Department, Education College, Mustansiriya University, Baghdad, Iraq

Dalal Mseer Naser, Physics Department, Education College, Mustansiriya University, Baghdad, Iraq

Physics Department, Education College, Mustansiriya University, Baghdad, Iraq


. "Mungbean crop pulse in Pakistan," Ed., Pakistan Agriculture Research Council(PARC), 2015.

. S. Kara and F. Dirgenali, "A System to Diagnose Atherosclerosis via Wavelet Transforms, Principal Component Analysis and Artificial Neural Networks " Expert Systems with Applications, vol. 32, pp. 632-640, 2007.

. "Mung Beans Nutrition & Its Big Benefits," in Dr.Axe (FOOD IS MEDICINE), Ed., 2016.

. E. S. Oplinger, L. L. Hardman, A. R. Kaminski, S. M. Combs and J. D. Doll, "Mungbean," in Corn Agronomy Ed., University of Wisconsin-, Madison, May,1990.

. Mahajan, Shveta, Das, Amitava, Sardana and H. Kumar, "Image acquisition techniques for assessment of legume quality," Trends in Food Science & Technology, vol. 42, no. 2, pp. 116-133, 2015.

. M. A. S. a. S. J. Symons, "A machine vision system for grading lentils," Canadian Grain Commission vol. 43, pp. 7.7-7.14, 2001.

. B. S. a. D. G. S. Anami, "Improved Method for identification and classification of foreign boundries mixed food grain image sample," ICGST- International Journal on Artificial Intelligence and Machine Learning, vol. 9, no. 1, pp. 1-8, 2009.

. F. Kurtulmus, I. Alibas and I. Kavdir, "Classification of pepper seeds using machine vision based on neural network " Int. J. Agric. and Biol. Eng. , vol. 9, no. 1, pp. 51-62, 2016.

. D. Li, Y. Liu and L. Goa, "Research of maize seeds classification recognition based on the image processing," International Journal of Signal Processing, Image Processing and Pattern Recognition vol. 9, no. 11, pp. 181-190, 2016.

. K. Sabanci, A. Kayabasi and A. Toktas, "Computer vision based-method for classification of wheat grains using artificial neural networks " Journal of Science Food Agric., 2016.

. P. Zapotocnzy, "Discrimination of wheat grain varieties using image analysis and multidimensional analysis texture of grain mass " International Journal of food properties, vol. 17, pp. 139-151, 2014.

.M. Huang, J. Tang, B. Yang and Q. Zhu, "Classification of maize seeds of different years baased on hyperspectral imaging and model updating," Computers and Electronics in Agriculture vol. 122, pp. 139-145, 2016.

. X. Zhang, F. Liu, Y. He and X. Li, "Application of hyperspectral imaging and chemometric calibrations for variety discrimination of maize seeds " Sensors, vol. 12, pp. 17234-17246, 2012.

. A. A. Abdullah and M. A. Quteishat, "Wheat seeds classification using multi-layer perceptron artificial neural network " International Journal of Electronics Communication and Computer Engineering vol. 6, no. 2, pp. 307-309, 2015.

. N. Pandey, S. Krishna and S. Sharma, "Automatic seed classification by shape and color features by using machine vision technology," Internatoinal Journal of Computer Applications Technology and Research vol. 2, no. 2, pp. 208-213, 2013.

.Neelam and J. Gupta, "Identification and Classification of Rice varieties using Mahalanobis Distance by Computer Vsision " Journal of Scientific and Research Publications vol. 5, no. 5, pp. 1-5, 2016.

. R. Birla and P. A. Singh, "An efficient method for quality analysis of rice using machine vision system " Journal of Advance Information Technology vol. 6, no. 3, pp. 140-145, 2015.

. X. Chen, Y. Xun, W. Li and J. Zhang, "Combining discriminant analysis and neural networks for corn variety identification " Computers and Electronics in Agriculture, vol. 71, no. 1, pp. 548-553, 2010.

. S. Ghamari, "Classification of chickpea seeds using supervised and unsupervised neural networks," African Journal of Agricultural Research vol. 7, no. 21, pp. 3193-3201, 2012.

. H. K. Mebatsion, J. Paliwal and D. S. Jayas, "Automatic classification of non-touching cereal grains in digital images using limited morphological and color features," Computers and Electronics in Agriculture, vol. 90, pp. 99-105, 2013.

. N. S. Visen, J. Paliwal, J. D.S. and N. D. G. White, "Image analysis of bulk grain samples using neural networks," Canadian Biosystems Engineering, vol. 46, pp. 7.11-17.15, 2004.

. M. Shahid, M. S. Naweed, E. A. Rehmani and Mutiullah, "Varietal discrimination of wheat seeds by machine vision approach " Life Science Journal vol. 11, no. 6(s), pp. 245-252, 2014.

. P. Zapotocnzy, "Application of image texture analysis for varietal classification of barly " International Agrophysics, vol. 26, pp. 81-90, 2012.

. A. Pourreza, H. Pourreza, M. Abbaspur-Fard and H. Sadrina, "Identification of nine iranian wheat varieties by texture analysis," Computers and Electronics in Agriculture, vol. 83, pp. 102-108, 2012.

. R. M. Haralick, K. Shanmugam and I. Distein, "Texture Features for Image Classification," IEEE Transactions on System, Man and Cybernetics, vol. SMC-3, pp. 610-621, 1973.

. P. M. Szczypiński, M. Strzelecki, A. Materka and A. Klepaczko, "MaZda—A software package for image texture analysis," Computer Methods and Programs in Biomedicine, vol. 94, no. 1, pp. 66-76, 2009.

. T. Raykov and G. A. Marcoulides, An Introduction to Applied Multivariate Analysis, Routledge Taylor & Francis Group New York, 2008.

. J. F. Hair, W. C. Black, B. J. Babin, R. E. Anderson and R. L. Tatham, Multivariate Data Analysis, Prentice Hall, Upper Saddle River, New Jersey, 6th edition 2006.

. R. Duda, P. Hart and D. Stork, Pattern Classification, John Wiley, New York, 2001.

. S. Haykin, Neural Networks a ComprehensiveFoundation, Prentice Hall, Upper Saddle River, NJ, USA, 2 edition 1999.






Sciences (Physics)