Published December 2015 | Version v1
Journal article

Optimization of nickel oxide nanoparticle synthesis through the sol–gel method using Box–Behnken design

  • 1. Department of Chemical Engineering, Faculty of Engineering and Petroleum, Hadhramout University of Science &Technology, Mukalla, Hadhramout (Yemen)
  • 2. Research Centre for Sustainable Process Technology, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor (Malaysia)
  • 3. Department of Chemical and Process Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor (Malaysia)
  • 4. Gas Processing Centre, Qatar University, P.O. Box 2713, Doha (Qatar)

Description

Highlights: • NiO nanoparticles (NiO NPs) were synthesized by simple sol–gel route. • Three main factors were optimized by Box–Behnken design for smaller size of NiO NPs. • The predicted size was found to be 13.74 nm which is in good agreement with experimental value as 14.31 nm. • The finding size showed a higher accurate of Box–Behnken design prediction. - Abstract: In this study, nickel oxide nanoparticles were prepared using the sol–gel method. The process parameters were optimized to produce smaller size of nanoparticles such as molar ratio, solution pH and calcination temperatures. The Box–Behnken method was selected as the statistical prediction method with the aim of reducing the number of experimental runs which will directly save time and chemicals and thereby reducing the overall cost. The size of the nickel oxide particles was selected as the response of the synthesis process and was determined using X-ray diffraction. The optimum predicted conditions were obtained at a molar ratio of 1:1.74, solution pH of 1.02 and calcination temperature of 400.08 °C. The particle size from the optimized experimental conditions was found to be 14.31 nm which was in good agreement with the predicted value of 13.74 nm. These results were justified by the relatively high correlation coefficients (R2 = 0.9859 and R2adj = 0.9677) of the statistical prediction.

Availability note (English)

Available from http://dx.doi.org/10.1016/j.matdes.2015.07.176

Additional details

Identifiers

DOI
10.1016/j.matdes.2015.07.176;
PII
S0264127515302483;

Publishing Information

Journal Title
Materials and Design
Journal Volume
86
Journal Page Range
p. 948-956
ISSN
0264-1275

Optional Information

Copyright
Copyright (c) 2017 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.