Experimental characterization of electrospinning: the electrically forced jet and instabilities

Shin, Yu-Shik; Hohman, Moses M.; Brenner, Michael P.; Rutledge, Gregory C.

doi:10.1016/s0032-3861(01)00540-7

Cited by 976 publications

(528 citation statements)

References 24 publications

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“…The current also increases linearly with the voltage applied between the electrodes, except for very high voltages, for which the increase is faster than linear. In additional experiments with the same apparatus, Shin et al (2001) observed that the slope of the current/flow rate characteristic of their high-conductivity PEO/water solutions changes when the jet becomes unstable to asymmetric (whipping) perturbations.…”

Section: Introductionmentioning

confidence: 96%

Electric current of an electrified jet issuing from a long metallic tube

Higuera

2011

J. Fluid Mech.

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This paper presents an analysis of the transport of electric current in a jet of an electrically conducting liquid discharging from a metallic tube into a gas or a vacuum, and subject to an electric field due to a high voltage applied between the tube and a far electrode. The flow, the surface charge and the electric field are computed in the current transfer region of the jet, where conduction current in the liquid becomes surface current due to the convection of electric charge accumulated at its surface. The electric current computed as a function of the flow rate of the liquid injected through the tube increases first as the square root of this flow rate, levels to a nearly constant value when the flow rate is increased and finally sets to a linear increase when the flow rate is further increased. The current increases linearly with the applied voltage at small and moderate values of this variable, and faster than linearly at high voltages. The characteristic length and structure of the current transfer region are determined. Order-of-magnitude estimates for jets which are only weakly stretched by the electric stresses are worked out that qualitatively account for some of the numerical results.

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Section: Introductionmentioning

confidence: 96%

Electric current of an electrified jet issuing from a long metallic tube

Higuera

2011

J. Fluid Mech.

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“…In literature this is often reported as nominal electric field strength in kV/cm, the distance referring to the gap between the nozzle tip and collector [39,40,46].…”

Section: Experimental Parametersmentioning

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%

“…In addition to this many material properties such as the intrinsic viscosity and bulk/surface charge density need to be determined experimentally before being incorporated into the models [32,[36][37][38][39][40][41]. Notwithstanding these difficulties, important models have been validated that describe different portions of the jet for selected materials.…”

Section: Introductionmentioning

confidence: 99%

“…Reneker et al created a mathematical model to explain the whipping instability based on the rheological properties of the polymer solution and lateral perturbations [32,36,41,44]. Hohman et al developed mathematical models for three different instabilities and used the models to create operating diagrams for when electrospinning occurs [37][38][39]. Feng created a 1D model based on slender body theory to examine the role of nonlinear rheology on the electrically stretched jet [40].…”

Section: Introductionmentioning

confidence: 99%

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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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References

2007

Science and Technology of Polymer Nanofibers

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Experimental characterization of electrospinning: the electrically forced jet and instabilities

Cited by 976 publications

References 24 publications

Electric current of an electrified jet issuing from a long metallic tube

Electric current of an electrified jet issuing from a long metallic tube

Electrospinning predictions using artificial neural networks

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