Literature DB >> 34201513

Optimization of the Electrospun Niobium-Tungsten Oxide Nanofibers Diameter Using Response Surface Methodology.

Babajide Oluwagbenga Fatile1, Martin Pugh1, Mamoun Medraj1.   

Abstract

The present research aimed to investigate the effect of working parameters on the electrospinning of niobium-tungsten oxide nanofibers and optimize the process using central composite design (CCD) based on the response surface methodology (RSM). An experiment was designed to assess the effects of five variables including the applied voltage (V), spinning distance (D), polymer concentration (P), flow rate (F), and addition of NaCl (N) on the resulting diameter of the nanofibers. Meanwhile, a second-order prediction model of nanofibers diameter was fitted and verified using analysis of variance (ANOVA). The results show that the diameter of the nanofibers was significantly influenced by all the variables except the flow rate. Some second-order and cross factor interactions such as VD, DP, PF, PN, and P2 also have significant effects on the diameter of the nanofibers. The results of the ANOVA yielded R2 and adjusted R2 values of 0.96 and 0.93 respectively, this affirmed that the predictive model fitted well with the experimental data. Furthermore, the process parameters were optimized using the CCD method and a maximum desirability function of 226 nm was achieved for the diameter of the nanofibers. This is very close to the 233 nm diameter obtained from a confirmatory experiment using the optimum conditions. Therefore, the model is representative of the process, and it could be used for future studies for the reduction of the diameter of electrospun nanofibers.

Entities:  

Keywords:  electrospinning; nanofibers; niobium–tungsten oxide; optimization; response surface methodology

Year:  2021        PMID: 34201513     DOI: 10.3390/nano11071644

Source DB:  PubMed          Journal:  Nanomaterials (Basel)        ISSN: 2079-4991            Impact factor:   5.076


  5 in total

1.  Controlling the fiber diameter during electrospinning.

Authors:  Sergey V Fridrikh; Jian H Yu; Michael P Brenner; Gregory C Rutledge
Journal:  Phys Rev Lett       Date:  2003-04-08       Impact factor: 9.161

Review 2.  Statistical designs and response surface techniques for the optimization of chromatographic systems.

Authors:  Sergio Luis Costa Ferreira; Roy Edward Bruns; Erik Galvão Paranhos da Silva; Walter Nei Lopes Dos Santos; Cristina Maria Quintella; Jorge Mauricio David; Jailson Bittencourt de Andrade; Marcia Cristina Breitkreitz; Isabel Cristina Sales Fontes Jardim; Benicio Barros Neto
Journal:  J Chromatogr A       Date:  2007-03-18       Impact factor: 4.759

3.  How To Optimize Materials and Devices via Design of Experiments and Machine Learning: Demonstration Using Organic Photovoltaics.

Authors:  Bing Cao; Lawrence A Adutwum; Anton O Oliynyk; Erik J Luber; Brian C Olsen; Arthur Mar; Jillian M Buriak
Journal:  ACS Nano       Date:  2018-07-20       Impact factor: 15.881

Review 4.  Electrospinning: a fascinating fiber fabrication technique.

Authors:  Nandana Bhardwaj; Subhas C Kundu
Journal:  Biotechnol Adv       Date:  2010-01-25       Impact factor: 14.227

5.  Electrospinning Optimization of Eudragit E PO with and without Chlorpheniramine Maleate Using a Design of Experiment Approach.

Authors:  Hend E Abdelhakim; Alastair Coupe; Catherine Tuleu; Mohan Edirisinghe; Duncan Q M Craig
Journal:  Mol Pharm       Date:  2019-05-07       Impact factor: 4.939

  5 in total

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