Modeling the relationship between electrospinning process parameters and ferrofluid/polyvinyl alcohol magnetic nanofiber diameter by artificial neural networks

Maurya, A.K.; Narayana, P.L.; Bhavani, A. Geetha; Hong, Jae‐Keun; Yeom, Jong‐Taek; Reddy, N.S.

doi:10.1016/j.elstat.2020.103425

Cited by 31 publications

(24 citation statements)

References 24 publications

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“…It can be seen that the increase in the distance slightly increases the fiber diameter. In general, the fiber diameter increases with distance up to a threshold value, and then it decreases, 14,29 yet the other process variables strongly influenced the effect. An increase in the applied voltage tends to increase the fiber diameter (Figure 4(d)) due to more vital driving forces that provide jet formation and elongation, reducing the fiber diameter.…”

Section: Resultsmentioning

confidence: 95%

Correlating the 3D melt electrospun polycaprolactone fiber diameter and process parameters using neural networks

Narayana

Wang

Yeom

et al. 2021

J of Applied Polymer Sci

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In the present work, we developed an artificial neural networks (ANN) model to predict and analyze the polycaprolactone fiber diameter as a function of 3D melt electrospinning process parameters. A total of 35 datasets having various combinations of electrospinning writing process variables (collector speed, tip to nozzle distance, applied pressure, and voltage) and resultant fiber diameter were considered for model development. The designed stand‐alone ANN software extracts relationships between the process variables and fiber diameter in a 3D melt electrospinning system. The developed model could predict the fiber diameter with reasonable accuracy for both train (28) and test (7) datasets. The relative index of importance revealed the significance of process variables on the fiber diameter. Virtual melt spinning system with the mean values of the process variables identifies the quantitative relationship between the fiber diameter and process variables.

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Section: Resultsmentioning

confidence: 95%

Correlating the 3D melt electrospun polycaprolactone fiber diameter and process parameters using neural networks

Narayana

Wang

Yeom

et al. 2021

J of Applied Polymer Sci

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“…This was due to the fact that the voltage and acceptance distance of A1 during the preparation process were larger than other groups. According to Maurya’s research results, the increase in voltage or the distance needle to collector within a certain range can refine the fiber diameter [ 38 ]. The spinning condition of group A2 was similar to that of group A3, and the average fiber diameter was 334 ± 69 nm.…”

Section: Resultsmentioning

confidence: 99%

Coaxial Electrospun PLLA Fibers Modified with Water-Soluble Materials for Oligodendrocyte Myelination

et al. 2021

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Myelin sheaths are essential in maintaining the integrity of axons. Development of the platform for in vitro myelination would be especially useful for demyelinating disease modeling and drug screening. In this study, a fiber scaffold with a core–shell structure was prepared in one step by the coaxial electrospinning method. A high-molecular-weight polymer poly-L-lactic acid (PLLA) was used as the core, while the shell was a natural polymer material such as hyaluronic acid (HA), sodium alginate (SA), or chitosan (CS). The morphology, differential scanning calorimetry (DSC), Fourier transform infrared spectra (FTIR), contact angle, viability assay, and in vitro myelination by oligodendrocytes were characterized. The results showed that such fibers are bead-free and continuous, with an average size from 294 ± 53 to 390 ± 54 nm. The DSC and FTIR curves indicated no changes in the phase state of coaxial brackets. Hyaluronic acid/PLLA coaxial fibers had the minimum contact angle (53.1° ± 0.24°). Myelin sheaths were wrapped around a coaxial electrospun scaffold modified with water-soluble materials after a 14-day incubation. All results suggest that such a scaffold prepared by coaxial electrospinning potentially provides a novel platform for oligodendrocyte myelination.

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“…Randomly generated initial weights were between −0.5 and +0.5, and the final model weights transformed as −9.08 to +7.68. The magnitude and nature of these weights distribution will be capable of mapping the complex relationship between the process parameters and fiber diameter 27,28 …”

Section: Methodsmentioning

confidence: 99%

“…The magnitude and nature of these weights distribution will be capable of mapping the complex relationship between the process parameters and fiber diameter. 27,28 F I G U R E 1 The scatter matrix of each input variable data from the polysaccharide-based biopolymer (Hylon VII starch) nanofiber [Color figure can be viewed at wileyonlinelibrary.com]…”

Section: Transformation Of Coefficients Of the Ann Modelmentioning

confidence: 99%

“…20,21 The polysaccharide is a biocompatible biopolymer, possesses excellent mechanical properties. 22 There have been few attempts to model the fiber diameter and electrospinning process parameters [23][24][25][26][27][28] ; however, there is hardly any report to model the relationship between electrospinning process variables with fiber diameter and also the quality (poor, good, and fair) of polysaccharide. The fiber diameter and the quality depends on the several features, such as (i) the solution (e.g., conductivity, solvent volatility, viscosity, and surface tension); and (ii) electrospinning process parameters (e.g., collector rotating speed, flow rate, the distance between tip and collector, and applied voltage), and the temperature.…”

Section: Introductionmentioning

confidence: 99%

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Using artificial neural networks to model and interpret electrospun polysaccharide (Hylon VII starch) nanofiber diameter

Premasudha

Reddy

Lee

et al. 2020

J of Applied Polymer Sci

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Present work was aimed to develop an artificial neural networks (ANN) model to predict the polysaccharide-based biopolymer (Hylon VII starch) nanofiber diameter and classification of its quality (good, fair, and poor) as a function of polymer concentration, spinning distance, feed rate, and applied voltage during the electrospinning process. The relationship between diameter and its quality with process parameters is complex and nonlinear. The backpropagation algorithm was used to train the ANN model and achieved the classification accuracy, precision, and recall of 93.9%, 95.2%, and 95.2%, respectively. The average errors of the predicted fiber diameter for training and unseen testing data were found to be 0.05% and 2.6%, respectively. A stand-alone ANN software was designed to extract information on the electrospinning system from a small experimental database. It was successful in establishing the relationship between electrospinning process parameters and fiber quality and diameter. The yield of smaller diameter with good quality was favored by lower feed rate, lower polymer solution concentration, and higher applied voltage.

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Modeling the relationship between electrospinning process parameters and ferrofluid/polyvinyl alcohol magnetic nanofiber diameter by artificial neural networks

Cited by 31 publications

References 24 publications

Correlating the 3D melt electrospun polycaprolactone fiber diameter and process parameters using neural networks

Correlating the 3D melt electrospun polycaprolactone fiber diameter and process parameters using neural networks

Coaxial Electrospun PLLA Fibers Modified with Water-Soluble Materials for Oligodendrocyte Myelination

Using artificial neural networks to model and interpret electrospun polysaccharide (Hylon VII starch) nanofiber diameter

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