Experimental investigation of the governing parameters in the electrospinning of polymer solutions

Theron, S. A.; Zussman, Eyal; Yarin, Alexander L.

doi:10.1016/j.polymer.2004.01.024

Cited by 890 publications

(593 citation statements)

References 37 publications

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“…The surface tension of the solvent is considered a critical parameter in many theoretical models, however experimentally it has been found to have a weak influence on fibre diameter [30,32], while it may impact highly on fibre morphology [44]. For this reason the ANN will be trained with solvent surface tension values verify their impact.…”

Section: Solvent Propertiesmentioning

confidence: 99%

“…The most important solution property is concentration, as together with molecular weight it determines whether or not there is a critical level of molecular entanglement for electrospinning to occur [28,30,33,35,51]. The concentration also follows power law relationships with fibre diameter and can easily be varied prior to electrospinning.…”

Section: Solution Propertiesmentioning

confidence: 99%

“…Some report the flow rate is a parameter that can be used to control the final fibre diameter whilst others report it has no statistical significance [30,39,46,49,62]. In the case of stable electrospinning where the meniscus at the spinning tip maintains constant shape, the flow rate into the Taylor cone must balance the rate of drawing from the jet.…”

Section: Experimental Parametersmentioning

confidence: 99%

“…A nonlinear inverse relationship was found between the dielectric constant of the solvent and the fibre diameter [29]. Theron [30][31][32][33]. While it is difficult to directly compare findings, certain trends seem to be universal i.e.…”

Section: Introductionmentioning

confidence: 98%

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Electrospinning predictions using artificial neural networks

Brooks

Tucker

2015

Polymer

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Abstract:Electrospinning is a relatively simple method of producing nanofibres. Currently there is no method to predict the characteristics of electrospun fibres for a wide range of polymer/solvent combinations and concentrations without first measuring a number of solution properties. This paper shows how artificial neural networks can be trained to make electrospinning predictions using only commonly available prior knowledge of the polymer and solvent. Firstly, a probabilistic neural network was trained to predict the classification of three possibilities; no fibres (electrospraying), beaded fibres and smooth fibres with over 80% correct predictions. Secondly, a generalised neural network was trained to predict fibre diameter with an average absolute percentage error of 22.3% for the validation data. These predictive tools can be used to reduce the parameter space prior to scoping exercises. Graphical Abstract:Predicted versus measured electrospun-fibre diameter from an artificial neural network trained using data from 13 different polymer/solvent combinations from over 20 independent studies.

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Section: Solvent Propertiesmentioning

confidence: 99%

Section: Solution Propertiesmentioning

confidence: 99%

Section: Experimental Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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Electrospinning predictions using artificial neural networks

Brooks

Tucker

2015

Polymer

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“…[20][21][22] In a typical procedure, a probe of a high voltage generator is immersed into a polymer solution. A high voltage on the order of several kilovolts is applied to the polymer solution that is filled into a narrow tube.…”

Section: Introductionmentioning

confidence: 99%

Glycidyl‐Methacrylate‐Based Electrospun Mats and Catalytic Silver Nanoparticles

Demir

Uǧur

Gülgün

et al. 2008

Macro Chemistry & Physics

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P(AN‐GMA) and PGMA fibers coated with monodisperse silver nanoparticles have been prepared by a combination of electrospinning and electroless plating. The morphology of the electrospun fibers remains unchanged after surface hydrazination. Oxidation of hydrazine in an ammoniacal solution of AgNO3 reduces and deposits silver atoms along the fiber surface, which then coalesce to Ag particles. The size of the silver nanoparticles is varied between 20–60 nm. Since the density of the active sites for silver reduction is lower in P(AN‐GMA), a smaller particle size could be obtained. The catalytic activity of the silver nanoparticles has been confirmed.magnified image

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References

2007

Science and Technology of Polymer Nanofibers

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Experimental investigation of the governing parameters in the electrospinning of polymer solutions

Cited by 890 publications

References 37 publications

Electrospinning predictions using artificial neural networks

Electrospinning predictions using artificial neural networks

Glycidyl‐Methacrylate‐Based Electrospun Mats and Catalytic Silver Nanoparticles

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