1,439 research outputs found

    Electrospinning predictions using artificial neural networks

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    Electrospinning is a relatively simple method of producing nanofibres. Currently there is no method to predict the characteristics of electrospun fibres produced from 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 > 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 before scoping exercises

    Usage Statistics: The Perks, Perils and Pitfalls

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    Effect of Adding Nanofibers into Sunflower (Helianthus annuus) Oil on Oil Viscosity

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    The significant effect of the addition of very small proportions of nanofibres and nano-particulates on the physical and mechanical properties of solid materials has been long observed and described. The effect of addition of electrospun fibres on the physical properties of liquid has not been so widely examined. In this paper, the effect of the addition of polyvinyl alcohol (PVOH), and zein nanofibers in sunflower (Helianthus annuus) oil on its viscosity was observed and quantified. For a given amount of material, a trend of increasing effectiveness was found as fibre diameter reduced. The addition of 0.01% (by mass) of fibre increased the kinematic viscosity of oil samples by 15%. The presence of fibre in oil was confirmed by light microscopy whilst the size of the fibres was measured by the analysis of scanning electron microscope images (SEM). This phenomenon of a low concentration of nanofibers significantly increasing viscosity may find practical applications as a foodstuff thickener

    The history of the science and technology of electrospinning from 1600 to 1995

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    A significant challenge inThis paper outlines the story of the inventions and discoveries that directly relate to the genesis and development of electrostatic production and drawing of fibres: electrospinning. Current interest in the process is due to the ease with which nano-scale fibers can be produced in the laboratory. In 1600, the first record of the electrostatic attraction of a liquid was observed by William Gilbert. Christian Friedrich Schönbein produced highly nitrated cellulose in 1846. In 1887 Charles Vernon Boys described the process in a paper on nano-fiber manufacture. John Francis Cooley filed the first electrospinning patent in 1900. In 1914 John Zeleny published work on the behaviour of fluid droplets at the end of metal capillaries. His effort began the attempt to mathematically model the behavior of fluids under electrostatic forces. Between 1931 and 1944 Anton Formhals took out at least 22 patents on electrospinning. In 1938, N.D. Rozenblum and I.V. Petryanov-Sokolov generated electrospun fibers, which they developed into filter materials. Between 1964 and 1969 Sir Geoffrey Ingram Taylor produced the beginnings of a theoretical underpinning of electrospinning by mathematically modelling the shape of the (Taylor) cone formed by the fluid droplet under the effect of an electric field. In the early 1990s several research groups (notably that of Reneker who popularised the name electrospinning) demonstrated electrospun nano-fibers. Since 1995, the number of publications about electrospinning has been increasing exponentially every year

    Complementary characterization data in support of uniaxially aligned electrospun nanocomposite based on a model PVOH-epoxy system

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    This paper presents complementary data corresponding to char- acterization tests done for our research article entitled “Uniaxially aligned electrospun fibers for advanced nanocomposites based on a model PVOH-epoxysystem”. Poly(vinyl alcohol) and epoxy resin were selected as a model system and the effect of electrospun fiber loading on polymer properties was examined in conjunction with two manufacturing methods. A novel electrospinning technology for production of uniaxially aligned nanofiber arrays was used. A conventional wet lay-up fabrication method is compared against a novel, hybrid electro- spinning–electrospraying approach.The structure and thermo- mechanical properties of resulting composite materials were examined using scanning electron microscopy, dynamic mechanical thermal analysis, and Fourier transform infra-red spectroscopy

    Electrohydrodynamic atomization assisted encapsulation of bioactive compounds

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    Electrohydrodynamics atomization (Electrospraying) is a versatile technique to produce microspheres or beads from various materials by means of electrical forces. Electrospraying has numerous advantages over traditional methods in encapsulation: there is high encapsulation efficiency without unfavorable denaturation of bioactive molecules and an enhanced control over the size distribution of particles. This technique could be used for encapsulating bioactive molecules such as proteins, enzymes, vitamins, polyphenols, drugs and sensitive ingredients, for lots of purposes such as masking the bitter test of compounds, sustained releasing, the stability of compounds during process or shelf life of foods, etc. Living cells and spores could be encapsulated by this method for bioenvironmental purposes. Beads can be made by a wide range of food grade and natural biodegradable polymers including alginate, starch, carrageenan, chitosan zein, etc., which makes their use suitable in the development of new foods with enhanced properties and characteristics. Encapsulation by this method also achieves the ability of sustained and controlled release of bioactive compounds by foods, and increasing the effectiveness of such compounds along the time
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