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Mehrdad Khamforoush

Mehrdad Khamforoush

Academic rank: Associate Professor
ORCID:
Education: PhD.
ScopusId: 21742691800
HIndex:
Faculty: Faculty of Engineering
Address: Department of Chemical Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran.
Phone:

Research

Title
Optimization of rotating‑jet electrospinning process using response surface methodology
Type
JournalPaper
Keywords
Electrospinning, Rotating-jet method, ,Response surface methodology , Optimization
Year
2016
Journal IRANIAN POLYMER JOURNAL
DOI
Researchers Mehrdad Khamforoush ، Reza Agha-Moalapour

Abstract

Rotating-jet electrospinning method is one of the efficient techniques for producing aligned nanofibers. This paper reports an accurate investigation on the influence of collector diameter (CD), voltage, polymer concentration (PC), and insulator length (IL) of spinneret on the degree of fiber alignment (DFA), production rate of fiber, and fiber diameter. The polymer solution was a mixture of polyacrylonitrile and N,N-dimethylformamide. The ranges of independent variables were 20–50 cm for CD, 10–22 kV for voltage, 13–19 wt% for PC, and 0.5–3 cm for IL. To minimize the number of required experiments for a complete evaluation, response surface methodology (RSM) and central composite rotatable design were applied by means of Expert Design Software. After defining the upper and lower bounds of the above independent variables in the software, 30 unique experiments were delivered. The recommended operating conditions by the software were exactly applied in the laboratory and the corresponding values for the DFA, production rate, and fiber diameter were measured. The nanofiber morphology was examined by scanning electron microscopy (SEM). By applying the least-squares method in the DX7.0.0 software, well-fitting polynomial correlations to the experimental results were obtained, and using these correlations, the influence of independent variables and responses was comprehensively studied. Finally, the best values of independent variables for optimizing the responses were determined using RSM.