Study on Porosity of Electrospun Nanofiber Membrane by ... · Electrospinning refers to a spinning...

Applied Mathematical Sciences, Vol. 12, 2018, no. 22, 1059 - 1074

HIKARI Ltd, www.m-hikari.com

https://doi.org/10.12988/ams.2018.8582

Study on Porosity of Electrospun Nanofiber

Membrane by Neural Network

Ting Wang 1, Wenxia Dong1, Ying Chen2, 3, 4,

Tiandi Pan2 and Rudong Chen1,*

1 Department of Mathematics, Tianjian Polytechnic University

No. 399, Binshui Street, Xiqin District, Tianjian, 300387, China *Corresponding author

2 Department of Textile, Tianjian Polytechnic University

No. 399, Binshui Street, Xiqin District, Tianjian, 300387, China

3 The Higher Occupation Education Department

Tianjin University of Technology and Education, Tianjin, 300222, China

4 Statistical Research Institute, Naikai University, Tianjin, 300071, China

Copyright © 2018 Ting Wang et al. This article is distributed under the Creative Commons

Attribution License, which permits unrestricted use, distribution, and reproduction in any medium,

provided the original work is properly cited.

Abstract

In this paper, using artificial neural network (ANN) method to find the effects

of electrospinning parameters including spinning distance (cm), applied voltage

(kV), and volume flow rate (mL/ h) on the porosity of electrospun nanofiber

membrane is mainly studied. The porosity of the nanofiber membrane was

obtained through the Matlab software to calculate the pixel value. The study

found that the applied voltage (kV) and the spinning distance (cm) on the

nanofiber membrane porosity have greater impact. The correlation coefficient

between the variables and ANN model (𝑅2=0.996) shows splendid fitting with

experimental data. The ANN model predicted the maximum porosity (59.88%)

of electrospinning nanofiber membrane at the conditions of 19 cm of spinning

distance, 26 kV of the applied voltage and 0.5 mL/h of volume flow rate.

1060 Ting Wang et al.

Keywords: artificial neural network; Matlab 2012b; electrospun nanofiber;

porosity

1 Introduction

Electrospinning refers to a spinning technique in which a polymer solution or

melt is spray-drawn under electrostatic action to obtain nanofibers.

Electrospinning is a special fiber-making process in which polymer solutions or

melts are spun in strong electric fields. Under the action of an electric field, the

droplet at the needle will change from spherical to conical (ie, "Taylor cone")

and extend from the tip of the cone to form a filament [1]. This method produces

nano-diameter polymer filaments. Electrospinning process parameters affecting

porosity can be divided into four categories: polymer properties, solute

properties, solution properties and experimental parameters [2-4]. This paper

focuses on the fourth category.

The porosity of a fibrous membrane, expressed as a percentage, refers to the

ratio of the pore volume of the fibrous membrane to the total volume of the

fibrous membrane [5]. The porosity of nanofiber membranes has great

application in filtration, tissue engineering and so on [5-7]. The measurement of

the porosity of nanofibrous membrane includes density method, solution

replacement method [8, 9]. Matlab and Photoshop are used to measure the

porosity of electrospun nanofiber membrane.

Artificial neural network is model that simulates the behavior characteristics

of animal neural network and carries out distributed parallel information

processing. The earliest proposed imitation of human brain function is the MP

model established by American scientists Pitts and McCulloch in 1934. Artificial

neural network is a multi-layer structure of the feedforward network, mainly by

the input layer, the hidden layer and the output layer of three parts. Each node in

the input layer corresponds to a predictor variable. The node of the output layer

corresponds to the target variable, there may be more than one. Between the

input layer and the output layer is hidden layer, hidden layer and the number of

nodes in each layer determines the complexity of the neural network. In recent

years, the use of neural networks on the porosity has attracted the interest of

many scholars [1, 10-12]. In this paper, the influence of electrospinning process

parameters on the porosity of nanofiber membranes is studied by means of

neural network. A new mathematical method is provided to study the porosity of

nanofiber membranes.

Study on porosity of electrospun nanofiber membrane 1061

2 Experimental

2.1 Materials

Polyvinyl alcohol (PVA) with number-average molecule weight (Mn) of

84000~89000, alcoholysis degree of 86 ~ 89 mol% and the average degree of

polymerization 1700 ~ 1800.was purchased from Changchun Petrochemical Co.,

Ltd.,Taiwan.

2.2 Preparation of electrospun nanofibers

Polyvinyl alcohol was dissolved in distilled water to prepare a polyvinyl

alcohol solution with a concentration of 12 wt %. The obtained mixed solution

was stirred in a water bath at 80 ° C for 1 h until a homogeneous solution was

formed, and then defibrated for standing.

2.3 Sample preparation

At room temperature, pour the prepared spinning solution into four 5-ml

syringes with a needle diameter of 1.2 mm. From the previous studies, the

process parameters of electrospun nanofiber nonwoven technology parameters

are five, namely: spinning time to take 90 minutes, solution concentration of 12

wt %; spinning distance (cm) take 11,13,15,17,19; applied voltage (kV) take

15,18,20,23,26; volume flow rate (ml/ h) take 0.5,0.7,1,1.2,1.5. In order to

experiment the generality, the mathematical method of orthogonal experiment

was used and five experiments was added on this basis as shown in TableⅠ and

Table Ⅱ to design experiments.

TABLE Ⅰ Orthogonal table

1 1 1 1 16 4 1 4

2 1 2 2 17 4 2 5

3 1 3 3 18 4 3 1

4 1 4 4 19 4 4 2

5 1 5 5 20 4 5 3

6 2 1 2 21 5 1 5

7 2 2 3 22 5 2 1

8 2 3 4 23 5 3 2

9 2 4 5 24 5 4 3

10 2 5 1 25 5 5 4


TABLE Ⅰ (Continued): Orthogonal table

11 3 1 3 26 1 2 1

12 3 2 4 27 2 5 3

13 3 3 5 28 3 3 2

14 3 4 1 29 4 4 5

15 3 5 2 30 5 1 3

TABLE Ⅱ Orthogonal experimental table

No

Spinning

distance

（cm）

Applied

voltage

（kV）

Volume

flow rate

（ml/h） No

Spinning

distance

（cm）

Applied

voltage

（kV）

Volume

flow rate

（ml/h）

1 11 15 0.5 16 15 20 1.5

2 13 15 0.7 17 17 20 0.5

3 15 15 1 18 19 20 0.7

4 17 15 1.2 19 11 23 1.2

5 19 15 1 20 13 23 1.5

6 19 15 1.5 21 15 23 0.5

7 11 18 0.5 22 17 23 0.7

8 11 18 0.7 23 17 23 1.5

9 13 18 1 24 19 23 1

10 15 18 1.2 25 11 26 1.5

11 17 18 1.5 26 13 26 0.5

12 19 18 0.5 27 13 26 1

13 11 20 1 28 15 26 0.7

14 13 20 1.2 29 17 26 1

15 15 20 0.7 30 19 26 1.2

2.4 Morphological characterization

The surface of polyvinyl alcohol nanofiber membrane with different

parameters under vacuum condition was sprayed with gold. The morphology

after treatment was observed by scanning electron microscope (SEM,TM-3030,

Japan) coating with magnification of 2000. Figure 1 shows the SEM micrograph

of electrospun nanofiber mat.


Figure 1 The SEM of electrospun nanofiber mat

2.5 Threshold determination

In Photoshop, the threshold command turns a gray scale or color image into a

high-contrast black-and-white image. We can specify a color scale as a threshold,

all pixels that are brighter than the threshold to white, and all pixels that are

darker than the threshold to black. In this paper, the scanning electron

micrographs of the nanofiber membranes are imported into Photoshop. The

SEM is transformed into gray scale images, the thresholds of the images are

adjusted, and a suitable threshold is found by observing changes of the images.

2.6Porosity measurement

The pixel values corresponding to each position in the scanning electron

micrograph (SME) are calculated by Matlab software. Then, using the threshold

values obtained above, the part larger than the threshold value is a white pixel

value, that is, a non-porous part. Matlab procedures are as follows:

filename='1.jpg';

ddata=imread(num2str(filename));

figure;imshow(ddata);

gdata=rgb2gray(ddata);

figure;imshow(gdata);

xlswritecopy('1.xls',gdata)

Thus the porosity can be calculated as follows:


1 0 0( % )

N

nNp

(1)

Where n is the pixel value of the white portion, N is the pixel value of the entire image, and N is the porosity of the nanofiber membrane.

2.7 Experimental design

In this paper, only three process parameters (spinning distance, applied

voltage, volume flow rate) and one output parameter (porosity) are studied, so a

three-input neuron and one neural network model can be established, as shown

in Figure 2. For Hidden layer neurons, according to "hidden neurons try to

minimize the number of convergence as fast as possible, approaching the error

as small as possible" principle, we have been trained to select 12 hidden neurons.

Transfer function selection as 'transig', 'transig' such a transfer function, select

'trainlm' as a training function.

Figure 2 Architecture of a three-layer neural network with one hidden layer

3 Results and discussion

3.1 Porosity measurement results

First of all, the threshold was determined using Photoshop software. Secondly,

pixel values for the SEM image at various positions were obtained by Matlab

software. Then the porosity of the nanofiber membrane can be calculated, the

results of 30 groups of nanofiber membrane porosity experiments are shown in

Table Ⅲ.

TABLE Ⅲ Electrospun nanofiber membrane porosity results

No Threshold Magnification Porosity/% No Threshold Magnification Porosity/%

1 110 2000 59.24267 16 112 2000 55.96664

2 128 2000 53.70591 17 110 2000 51.35727

3 120 2000 45.94035 18 121 2000 72.71339

4 111 2000 58.07089 19 109 2000 54.14104


TABLE Ⅲ (Continued): Electrospun nanofiber membrane porosity results

5 118 2000 45.01372 20 132 2000 46.39544

6 111 2000 63.24721 21 107 2000 52.36049

7 112 2000 56.3153 22 113 2000 47.15806

8 109 2000 42.86643 23 127 2000 47.39277

9 115 2000 59.88159 24 109 2000 52.29995

10 128 2000 47.68498 25 126 2000 51.89886

11 126 2000 47.76246 26 116 2000 47.50889

12 108 2000 58.53733 27 128 2000 43.06313

13 119 2000 53.96498 28 122 2000 48.3516

14 130 2000 41.87643 29 97 2000 50.31139

15 128 2000 46.69801 30 128 2000 46.55773

3.2 Artificial neural networks results

After observing the sample images, we can see that the 6 # and 18 # samples

have problems.

Figure 3 Samples of 6# and 18#

As we can see, 6 #, 18 # samples are almost no fiber, so two samples are

removed. Neural network model is trained and simulated using Matlab

2012a.The training of the ANN was stopped after 23 because the targeted MSE

value was reached, as shown in Figure 4. Then the functional relationship

between each parameter and porosity was obtained:

f(x1,x2,x3)= 36.01/(exp(19.59/(exp(1.283*x1 - 1.3*x2 - 4.014*x3)+ 13.19) +

1.0) - 9.905/(exp(1.252*x1 - 0.04274*x2)- 7.355*x3 - 13.0) + 1.0) -

16.97/(exp(33.47 - 0.4775*x2 - 11.38*x3 - 1.29*x1) + 1.0) -

10.51/(exp(4.591*x3 - 1.041*x2 - 0.1329*x1 + 23.68) + 1.0) - 7.821/(exp(35.85

- 0.4357*x2 - 9.607*x3 - 0.9344*x1) + 1.0) - 18.47/(exp(0.9808*x1 - 1.046*x2


+ 4.191*x3 - 0.5388) + 1.0) - 6.316/(exp(0.2119*x1 + 0.8518*x2 - 10.02*x3 -

9.322) + 1.0) + 3.715/(exp(0.6171*x2 - 0.3942*x1 - 10.72*x3 - 0.6515) + 1.0) +

8.283/(exp(0.901*x1 - 0.08951*x2 - 11.57*x3 - 7.567) + 1.0) +

14.43/(exp(35.13 - 0.6639*x2 - 2.003*x3 - 1.563*x1) + 1.0) +

16.73/(exp(1.585*x1 - 0.9456*x2 - 2.026*x3 - 9.525) + 1.0) +

6.664/(exp(1.149*x2 - 0.6706*x1 - 0.5825*x3 - 16.25) + 1.0) - 8.961) + 1.0) +

23.87

where x1, x2, x3, respectively, that spinning distance, applied voltage and

volume flow rate.

Figure 4 The graphic of error variation depending on iteration of ANN

TABLE Ⅳ The Matlab and ANN predicted electrospun nanofiber porosity

No

porosity

error/% No

porosity

error/% Matlab/% ANN/% Matlab/% ANN/%

1 59.24267 59.23637 0.010648 16 55.96664 56.03572 0.123442

2 53.70591 53.52115 0.344023 17 51.35727 51.25041 0.208068

3 45.94035 45.96707 0.058161 19 54.14104 54.11683 0.044721

4 58.07089 58.06394 0.011968 20 46.39544 46.35697 0.082908

5 45.01372 45.01528 0.003476 21 52.36049 52.27466 0.16392

7 56.3153 56.34125 0.046077 22 47.15806 47.00127 0.332483

8 42.86643 42.95941 0.216902 23 47.39277 47.50672 0.240449

9 59.88159 59.88 0.002669 24 52.29995 52.17518 0.238568


TABLE Ⅳ (Continued): The Matlab and ANN predicted electrospun nanofiber

porosity

10 47.68498 47.60867 0.160022 25 51.89886 51.78743 0.21472

11 47.76246 47.77188 0.019708 26 47.50889 47.27931 0.48324

12 58.53733 58.52467 0.021628 27 43.06313 42.81528 0.575552

13 53.96498 53.89592 0.127965 28 48.3516 48.21858 0.275114

14 41.87643 42.12327 0.589448 29 50.31139 50.13514 0.350313

15 46.69801 46.6223 0.162132 30 46.55773 46.49209 0.140969

𝑅2=0.996

Mean absolute error (%)=0.187

Mean square error (%)=1.358

3.3 Result analysis

According to the function relation f(x1, x2, x3), the effects of all the single

and dual factors on the porosity of the nanofiber membrane were studied. The

effect plots, which describe the effects of single factor and dual factors on the

porosity of the nanofiber membrane, are shown Figure 5 and Figure 6

respectively.

When x1 = 15, x2 = 20, the porosity changes with x3, as shown in Figure 5 (a);

When x1 = 15, x3 = 1, the porosity changes with x2, as shown in Figure 5 (b);

When x2 = 20, x3 = 1, the porosity changes with x1, as shown in Figure 5 (c).

When x1 = 16, the porosity changes with x2, x3, as shown in Figure 6(a);

When x2 = 21, the porosity changes with x1, x3, as shown in Figure 6 (b);

When x3 = 16, the porosity changes with x1, x2, as shown in Figure 6 (c).


Figure 5 Fixed two variables, the

changes of porosity with the other

variables

Po

rosity

(%)

Volume flow rate (mL/h)

Po

rosity

(%)

Spinning distance (cm)

Po

rosity

(%)

Applied voltage (kV)


Figure 6 Fixed one variable, the changes of porosity with the remaining two

variables

We can also use the ANN weight matrix to evaluate the relative importance

(RI) of different input parameters on the porosity of electrospinning nanofiber

membrane to the output parameters, and one based on the weight of the

connection weight is proposed[13,14]：

i h i

ih

N

k

N

mmn

N

kmkmk

mn

N

kmk

N

mmj

j

LII

LIWI

1 1 1

11

}))/(({

))/((

RI

(2)

Where jRI is the relative importance of the output parameters of different input

parameters； hi N，N ,respectively, the number of input neurons and hidden neurons; I, L, respectively, for the input layer to the hidden layer of the weight

matrix and the hidden layer to the output layer weight matrix ; Subscript n is the

output parameter. In the paper, j = 1, 2, 3, Ni = 3, Nh = 12, n = 1, I,L are given in

Table Ⅴ.


TABLE Ⅴ Weights and bias in training

Layer Weight Bias

Hidden layer 𝐼11 𝐼12 𝐼13

-3.6024 0.4923 5.7834

𝑏11

2.3906

𝐼21 𝐼22 𝐼23

-6.3398 5.2006 1.0128

𝑏21

3.6433

𝐼31 𝐼32 𝐼33

-3.9231 5.7539 -2.0955

𝑏31

1.6736

𝐼41 𝐼42 𝐼43

3.7378 2.3961 4.8035

𝑏41

-7.1161

𝐼51 𝐼52 𝐼53

-5.1307 7.1504 2.0068

𝑏51

-2.8951

𝐼61 𝐼62 𝐼63

0.5316 5.7272 -2.2954

𝑏61

-4.4450

𝐼71 𝐼72 𝐼73

-5.0083 0.2351 3.6776

𝑏71

1.7753

𝐼81 𝐼82 𝐼83

2.6825 -6.3206 0.2912

𝑏81

3.3406

𝐼91 𝐼92 𝐼93

-0.8477 -4.6847 5.0121

𝑏91

-0.3869

𝐼10,1 𝐼10,2 𝐼10,3

5.1619 2.6263 5.6903

𝑏10,1

-3.2108

𝐼11,1 𝐼11,2 𝐼11,3

6.2532 3.6513 1.0014

𝑏11,1

-3.4877

𝐼12,1 𝐼12,2 𝐼12,3

1.5769 -3.3939 5.3587

𝑏12,1

0.5454

Output layer

𝐿1 𝐿2 𝐿3 𝐿4 𝐿5 𝐿6

-2.0709 -4.1819 4.6175 1.9551 -4.8978 2.6281

𝐿7 𝐿8 𝐿9 𝐿10 𝐿11 𝐿12

2.4761 -1.6660 1.5791 4.2414 -3.6078 -0.9288

b

4.6248

The relative importance of the nanofiber membrane porosity is calculated as

shown in Figure 7. The three process parameters have a strong influence on the

porosity of the nanofiber membrane. In the research, the influence of these three

parameters on the porosity of the nanofiber membrane should not be neglected.


However, the influence of voltage and distance on the porosity of nanofiber

membranes is even more significant.

Figure7 The relative importance of the parameters of the porosity of the

nanofibrous membrane

3.4 Optimization

In this paper, the goal is to find the maximum porosity of nanofiber membrane. Optimization finds a set of conditions that meet the maximum

porosity. In Figure 5, the optimum conditions in the given range for maximum

porosity of electrospinning nanofiber membrane were 19 cm of spinning

distance, 26 kV of the applied voltage and 0.5 mL/h of volume flow rate. In

order to insure the predictive ability of the ANN model, more electrospinning

experiments were carried out. The result and experiment conditions was shown

in Table Ⅵ.

rate

36.8 37.2

26.0

distance voltage relativ

e

imp

ortan

ce

（%

）


TABLE Ⅵ Validation of ANN using different levels of applied voltage, spinning

distance and volume flow rate

No

Actual values of the variables

Nanofiber membrane

porosity

Spinning distance

(cm)

Applied voltage

(kV)

Volume flow rate

(mL/h) Threshold Matlab ANN

A

B

C

13

15.51

14.69

26

15.55

16.69

0.6

0.5

1.2

125

90

98

58.05091

23.51724

33.77956

58.1788

23.8707

33.7871

D 19 16 1.5 126 58.58745 59.8799

4 Conclusions

In this study the impact of three electrospinning parameters, namely applied

voltage (kV), spinning distance (cm), and volume flow rate (mL/h), on the

porosity of electrospun nanofiber mats was determined by ANN. The porosity of

the nanofiber membrane measured by the Matlab software is close to the

porosity of the nanofiber membrane simulated by the ANN, indicating that the

performance of ANN model for predicting was good. The results also showed

that the applied voltage and spinning were the two most critical parameters

affecting the porosity of the nanofiber mats. Increasing the spinning distance

resulted in less nanofiber mat porosity whereas increasing the applied voltage

caused an increase in nanofiber mat porosity.

Acknowledgements. This research was supported by The Science and

Technology Plans of Tianjin (No. 15PTSYJC00230), NSFC Grant No.

11071279.

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Received: March 31, 2018; Published: August 30, 2018
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