Nano composite fiber process optimization for polypropylene with antibacterial and far-infrared ray emission properties

Abstract

This research aims to develop multifunctional polypropylene (PP) fiber with a far-infrared ray emission property and microorganism resistance. The processing parameters, including powder proportion, twin-screw mixing and melt spinning, were planned using the Taguchi method, gray relational analysis and the technique for order preference by similarity to ideal solution. The emission test results showed that the far-infrared ray emission value of composite fiber was 85%, which is 2.3 times that of pure PP. According to the far-infrared ray emission temperature rise test, the composite fiber temperature increases by 8.6℃, which is 43% higher than the 6℃ temperature rise of pure PP. The antibacterial test showed that the composite fiber has an antibacterial effect on staphylococcus aureus and pneumobacillus. Moreover, the composite fiber of PP with nano silicon dioxide and zinc oxide met the far-infrared ray emission property FTTS-FA-010 and qualitative antibacterial JIS L-1902 standards.

Keywords

polypropylene nanoscale silicon dioxide nanoscale zinc oxide far-infrared ray emission property antibacterial twin-screw mixing melt spinning Taguchi method gray relational analysis technique for order preference by similarity to ideal solution

Polypropylene (PP) is characterized by good wear resistance, light weight, high heat retaining ability and being shrink-proof. However, it has drawbacks of a low softening point and elasticity due to its poor thermal and mechanical properties, thus limiting its applicability. The current solution is to add nano-powder to increase its crystallization temperature and degree of crystallinity, thereby elevating its softening point. As the added powder has high strength, it can enhance the mechanical property of PP and improve the elasticity. In order to improve the thermal and mechanical properties of PP composite fiber, Srisawat et al.¹ used single-screw mixing to mix PP with 0.25–1 wt% nano-silica uniformly, and produced composite fiber by melt spinning. The results showed that the silica particles dispersed over the fiber surface, and increased the tensile strength. The thermal property increased with the proportion of silica, so that the composite fiber has a higher degree of crystallinity. In order to improve the degree of crystallinity and mechanical property of PP, Bikiaris et al.² used 1–15 wt% nano-SiO₂ and maleic anhydride grafted PP as compatibility agents for injection molding after twin-screw mixing. The powder dispersion effect was improved. The Young's modulus increased with the content of nano-powder, as high as 1.6 times that of PP. This proved that the addition of nano-powder could enhance the mechanical property of material.

Fiber products are commonly used in fabrics, thus exposing human skin to a high content of staphylococcus aureus and colibacillus, which are harmful. In order to solve this problem, antibacterial fibers have been developed to prevent the hazards of the bacteria from fiber products. The two common methods are coating and blending. Shateri-Khalilabad and Yazdanshenas³ coated nano-ZnO on cotton fabric surface, and observed the surface through scanning electron microscopy (SEM). The observation showed that nano-ZnO dispersed spherically and uniformly, rendering the fabrics to resist to staphylococcus aureus and ultraviolet (UV) light. Hwang et al.⁴ blended polymethyl methacrylate (PMMA) with ZnO/TiO₂ nano-powder using the electrospinning technique, and examined the effectiveness on resisting colibacillus and staphylococcus aureus. The experimental results showed that the ZnO/PMMA is antibacterial in the dark, and the TiO₂/PMMA has good bacteria repellency in UV irradiation. The composite powder ZnO/TiO₂/PMMA has good bacteria repellency in the dark, and it is more antibacterial under UV light.

The far-infrared textiles are effective in retaining heat, as the energy absorbed by the far-infrared material contained in the fiber is converted into and emitted as far-infrared rays. Lin et al.⁵ used a twin-screw mixer to blend different proportions of nano-bamboo charcoal material in PVA, and used a melt spinning machine to make fiber. After examining the far-infrared ray emission property of fiber, it was found that the far-infrared ray emission value of composite fiber increased with the powder proportion. Moreover, the measurement of far-infrared ray temperature rise revealed that the temperature rise of composite fiber was 0.7℃ higher than pure polyvinyl alcohol (PVA), meeting the standard on fabrics with far-infrared ray characteristics. Bahng and Lee⁶ combined microorganisms with ceramic powder and polyester to produce fiber, and discussed the far-infrared ray temperature rise characteristic of fiber. They found that the temperature difference to the pure polyester was 2–3℃, and the functionality did not decline after multiple times of rinsing.

The Taguchi method has been widely used to design the experimental process, so as to reduce the number of experiments and the interference factors. However, the method is limited to single quality optimization, and is inapplicable to multi-quality problems. Mehata and Kamaruddinb⁷ used the Taguchi method to determine the optimum parameters of injection molding, and discussed the mechanical performance of recovered PP. Based on the Taguchi experiment design, the controllable factors for the experiment included melt temperature, packing pressure, injection time and packing time. Each factor had three levels. The results showed that the mechanical properties of the products made of recovered PP could be improved by using the parametric optimization method. Kuo et al.⁸ combined the Taguchi method with gray relational analysis (GRA) and the technique for order preference by similarity to ideal solution (TOPSIS) to determine the membrane material for preparing SiO _x N _y , as well as the correlation between processing parameters and the coating uniformity, coating thickness and water vapor permeability. The TOPSIS was used to determine the optimal combination of various quality characteristics, and build the prediction system. Finally, the multi-decision problem analysis process was established using the Taguchi method and confidence interval to solve the problem of no objective weight in the TOPSIS.

This study mixed PP with nano-silica and nano-ZnO, and used twin-screw mixing to disperse the powder uniformly. Then, melt spinning was used to produce fiber. As it is difficult to consider multiple levels of factor in the experimental process, the optimum parametric solution of various quality characteristics cannot be considered. Therefore, this study applied the Taguchi method to design the experiment, and the optimum parameters of the single quality characteristic were determined. The objective weight of each quality was obtained by GRA. Finally, the TOPSIS was used to determine the multi-quality optimum parametric solution to develop the parameter optimized fiber. Besides increasing the fiber processing efficiency and improving the thermal and mechanical properties of PP, far-infrared ray emission and antibacterial properties were added, and the applicability was expanded.

Experimental process

This study used PP as the substrate, and used nano-powder to enhance the thermal and mechanical properties. The effect on fiber processing parameters was discussed. The Taguchi orthogonal array L₁₈ was used to plan the addition level of dispersant, nano-silica and nano-ZnO and the twin-screw speed. The material was dispersed uniformly by twin-screw mixing to evaluate the material workability and thermal property. The appropriate die temperature, spinneret speed and drafting rotation speed were put in the orthogonal array to make fiber using the melt spinning machine. The tensile strength, elongation and denier of fiber were tested. The experimental data were used in the GRA to determine the multi-quality weight, and combined with the TOPSIS to obtain the multi-quality parameter solution. The optimum fiber was made, and its far-infrared ray emission property and bacteria repellency were discussed. The experimental process is shown in Figure 1.

Figure 1.

Experimental process.

Experimental instruments

Twin-screw mixer extruder had a mixing temperature of 230℃, and different screw speeds were designed according to the Taguchi experiment. Different proportions of PP, nano-silica, nano-ZnO and dispersant were mixed uniformly. For the melt spinning machine, the screw extrusion mixing temperature was set at 200℃, while different die temperatures, spinneret speeds and drafting rotation speeds were designed by the Taguchi experiment to produce fiber. The spinneret had 20 orifices, and each orifice had a diameter of 0.5 mm. For the melt indexer, the flowability of composite material was measured as per ASTM D1238 specifications. For the thermogravimetry analyzer, the heating rate was 10℃/min, and the temperature increased from 25℃ to 500℃. The pyrolysis temperature of composite material was observed. For the differential thermal analyzer, the heating/cooling rate was 10℃/min, and the standard heating-retention-cooling-retention-heating cycle was used to observe the melting temperature and crystallization temperature. For the SEM, a HITACHI TM3000 was used for observing the fiber morphology, structure and powder agglomeration. For the winding machine, the denier of fiber was measured as per ASTM D1577 specifications. For the universal tester, the tensile strength and elongation of fiber were measured as per CNS8306 specifications. The far-infrared ray emission property adopted in this study was above 80% of Taiwan’s standard far-infrared ray emission. The difference between far-infrared ray emission temperature rise and control group was above +0.5℃. The bacteria repellency was measured as per JIS L-1902 specifications.

Research methods

Far-infrared ray characteristics

Emission

When the SiO₂ powder in the fiber is heated or irradiated, the energy generates electronic excitation, the electrons are excited from Orbit K to Orbit I, and then the electrons become steady, transferring from Orbit I to Orbit K. In this period, the far-infrared ray transmits the heat energy in electromagnetic wave by radiation, as shown in Figure 2. This electromagnetic wave is likely to be absorbed by the human body for warming.

Temperature rise

In the state with external energy supply and human body heat retention

The energy is emitted in an electromagnetic wave to the far-infrared object, absorbed by the fabric and further emitted in an electromagnetic wave to the human body. The human body absorbs this electromagnetic wave, and the water molecules resonate, promoting blood circulation, so as to keep the human body warm.

In the state without external energy supply and human body heat retention

The human body emits energy, which is absorbed by the fabric and released in a far-infrared ray back to the human body. The procedure repeats, thus keeping the human body warm.

Figure 2.

Electrons transferring and releasing.

Antibacterial property

The inorganic powder antibacterial agent on the fiber is mainly ZnO nano-powder, and the antibacterial mechanism is a free radical reaction. When it is irradiated by the sun, photons with certain energy are injected into the powder. The electrons are excited from the VB to the CB, thereby leaving a hole. The excited CB electrons are combined with the hole to eliminate heat and energy. The hole changes the ambient hydroxy electrons into free radicals as a strong oxidizer during excitation. When it meets bacteria, it damages the cell walls and cell membranes of bacteria, thus disintegrating the bacteria for sterilization, as shown in Figure 3.

Figure 3.

Mechanism of disintegrating the bacteria.

The antibacterial mechanism reaction:

ZnO + hv \to e - + h +

h + + H_{2} O \to OH - + H +

e - + O_{2} \to O_{2}^{-}

O_{2}^{-} + 2 H + \to H_{2} O

The powder dispersion mechanism:

dispersant: bis-(triethoxy silpropyl);

chemical formula: (C2H5O)3SiCH2CH2CH2-CH2CH2CH2Si(OC2H5)3;

state: white oily liquid.

With regard to the dispersion mechanism, as the silica and ZnO are nanoscale powder, there is hydroxyl formed on the surface. The functional group of dispersant and the nano-powder generate hydrogen bonding, thus dispersing the agglomerated powder for uniform mixing.

This study combined PP with nano-silica and nano-ZnO to develop composite fiber. The material was dispersed uniformly after the dual-screw mixing process. The thermal property and flowability of material were analyzed, and the fiber was made by the melt spinning machine. The experiment was designed by the Taguchi method to discuss the influence of processing parameters (e.g., material mixing ratio, twin-screw speed, melt spinning die temperature, spinneret speed, drafting speed) on the tensile strength, elongation and denier of fiber. The GRA and TOPSIS were used to create quality analysis, make the optimal fiber and discuss the functional properties of yarn.

Results and discussion

Material thermal analysis

Three main powder proportions were selected for experimental analysis, which are PP/SiO₂(0.5 wt% content)/ZnO(0.5 wt% content), PP/SiO₂(1 wt% content)/ZnO(1 wt% content) and PP/SiO₂(1.5 wt% content)/ZnO(1.5 wt% content).

Thermogravimetric analysis

Firstly, 7.5 mg of specimens were placed in the platinum tray, at a fixed air flow of 20 ml/min, at a heating rate of 10℃/min, from 20℃ to 500℃. The experimental process is shown in Figure 4. The pyrolysis temperature of pure PP was 328℃. It was found that the nano-powder dispersed in the PP polymer chain, thus forming steric hindrance to the polymer chain and obstructing the activity of molecules. As a result, the pyrolysis temperature increased to 360℃. In terms of the composite material with 0.5 wt% (SiO₂ and ZnO), too much powder resulted in agglomeration. For the composite material with 1.0 and 1.5 wt% (SiO₂ and ZnO), respectively, the pyrolysis temperature was unstable, so that the pyrolysis temperature decreased to 344℃ and 338℃, respectively. When less than 3 wt% of power was added, the pyrolysis temperature increased.

Figure 4.

Thermogravimetric analysis diagram.

Differential thermal analysis

The differential thermal analyzer simulated the properties before and after material processing, in terms of the operating conditions. The first stage was heating from 25℃ to 200℃, the second stage was cooling from 200℃ to 30℃ and the third stage was heating from 25℃ to 200℃. The heating/cooling rate was 10℃/min, and the first stage heating eliminated heat history for the differential thermal analysis in the second and third stages. The experimental results showed that the crystallization temperature and degree of crystallinity increased obviously after the PP was mixed with nano-powder. This suggests that the PP was mixed with powder as a nucleating agent. The material could crystallize at a high temperature to improve the heat resistance. The average melting temperature of composite fiber was 166℃, and the crystalline melting point was 130℃.

Fiber process parameter optimization

The main purpose of the fiber process is to determine the influence and relationship of the twin-screw processing parameters and the processing parameters of the melt spinning machine to the quality characteristics. This study used the Taguchi orthogonal array L₁₈(2¹× 3⁶) to plan the relationship of quality characteristics. The experimental factors of different weight percent concentrations of dispersant, nano-silica powder and nano-ZnO powder, and the screw speed of the twin-screw machine tool, die temperature, spinneret rotation speed, and drafting speed of melt spinning machine were influential in the fiber process. Thus, it was required to use the appropriate level to make the optimum fiber. The controllable factors are shown in Table 1.

Table 1.

The controllable factors and their levels

		Level
Controllable factor		1	2	3
A	Dispersant (wt%)	0.1	0.05
B	Nano-silica powder (wt%)	0.5	1	1.5
C	Nano-ZnO powder (wt%)	0.5	1	1.5
D	Screw speed of twin screw (rpm)	200	300	400
E	Die temperature (℃)	210	220	230
F	Spinneret rotation speed (Hz)	10	12	14
G	Drafting speed (Hz)	4.5	5.5	6.5

The above controllable factors were substituted in the L₁₈(2¹× 3⁶) orthogonal array.^9–14 The fiber quality characteristics selected in this study included tensile strength, elongation and denier. There were 18 groups of parameters for experiments, and each group was tested five times. The gray relation data were derived from the measured data, and the gray relational grade of quality characteristics was calculated.¹⁵ The gray relational grade was converted into the weight of quality characteristics. The TOPSIS¹⁶ was used to determine the multi-quality optimal factor.

GRA

The tensile strength was the larger-the-better. Aiming at the maximum value measured in the experiment, the ultimate tensile strength was 3.686 N. The elongation was also the larger-the-better. Aiming at the minimum value of elongation in the experiment, the maximum elongation was 8.497%. The denier was the smaller-the -better. The minimum denier was 23.55 g. The three target values were used as reference sequence X₀, while the other 18 orders were used as comparison sequences, as shown in Table 2.

Gray relation sequence generation

Table 2.

Experimental data

No.	Strength (N)	Elongation (%)	Denier (g)
X ₀	3.686	8.497	2.494
1	2.656	7.324	3.455
2	3.162	4.849	4.151
3	2.433	3.704	3.685
4	2.299	6.862	3.550
5	2.308	7.379	4.980
6	1.528	2.442	2.961
7	2.051	4.632	4.561
8	1.378	6.704	2.729
9	0.950	2.744	5.403
10	3.686	6.874	3.849
11	3.596	6.723	3.427
12	1.219	2.765	3.475
13	1.303	5.437	2.494
14	2.372	8.497	4.219
15	1.805	2.892	4.267
16	3.275	5.206	4.393
17	0.806	3.447	2.861
18	1.738	5.616	3.428
Max	3.686	8.497	5.403
Min	0.806	2.442	2.494

The gray relation sequence is established according to Equations (1) and (2). The quality characteristics tensile strength and elongation are substituted into the larger-the-better equation. The denier is substituted into the smaller-the-better equation, and the gray relation sequence is calculated. The data must be 0–1, subtracted from max(x_i). The results are shown in Table 3. Equation (3) is used, and the recognition coefficient ζ is 0.5. The results are shown in Table 4.

x_{i} (k) = \frac{min x_{i} (k) - x_{i} (k)}{max x_{i} (k) - min x_{i} (k)}

(1)

x_{i} (k) = \frac{x_{i} (k) - min x_{i} (k)}{max x_{i} (k) - min x_{i} (k)}

(2)

ξ_{i} (k) = \frac{Δ min + ξ Δ max}{Δ oi (k) + ξ Δ max}

(3)

Normalized decision matrix

Table 3.

The gray relation sequence

No.	Strength	Elongation	Denier
1	0.358	0.194	0.330
2	0.182	0.602	0.569
3	0.435	0.792	0.409
4	0.482	0.270	0.363
5	0.478	0.185	0.855
6	0.749	1.000	0.160
7	0.568	0.638	0.711
8	0.801	0.296	0.081
9	0.950	0.950	1.000
10	0.000	0.268	0.466
11	0.031	0.293	0.321
12	0.857	0.947	0.337
13	0.827	0.505	0.000
14	0.456	0.000	0.593
15	0.653	0.926	0.610
16	0.143	0.543	0.653
17	1.000	0.834	0.126
18	0.676	0.476	0.321

Table 4.

The gray relation coefficient

No.	Strength	Elongation	Denier
1	0.583	0.721	0.602
2	0.733	0.454	0.468
3	0.535	0.387	0.550
4	0.509	0.649	0.579
5	0.511	0.730	0.369
6	0.400	0.333	0.757
7	0.468	0.439	0.413
8	0.384	0.628	0.861
9	0.345	0.345	0.333
10	1.000	0.651	0.518
11	0.941	0.630	0.609
12	0.369	0.346	0.597
13	0.377	0.497	1.000
14	0.523	1.000	0.458
15	0.434	0.351	0.451
16	0.778	0.479	0.434
17	0.333	0.375	0.799
18	0.425	0.512	0.609

The normalized decision matrix is established by using Equation (4), and the weighting matrix is established according to Equation (5) by using the gray correlation coefficient, as shown in Table 5.

r_{ij} = x_{ij} \div \sqrt{\sum_{i = 1}^{m} x_{ij}^{2}}

(4)

v = w \cdot r = [v_{11} \dots v_{1 n} : : v_{m 1} \dots v_{mn}] = [w_{1} r_{11} \dots w_{n} r_{1 n} : : w_{1} r_{m 1} \dots w_{n} r_{mn}]

(5)

Table 5.

The weighting matrix

No.	Strength	Elongation	Denier
1	0.089	0.101	0.074
2	0.106	0.067	0.089
3	0.081	0.051	0.079
4	0.077	0.094	0.076
5	0.077	0.101	0.107
6	0.051	0.034	0.064
7	0.068	0.064	0.098
8	0.046	0.092	0.059
9	0.032	0.038	0.116
10	0.123	0.094	0.083
11	0.120	0.092	0.074
12	0.041	0.038	0.075
13	0.044	0.075	0.054
14	0.079	0.117	0.091
15	0.060	0.040	0.092
16	0.109	0.072	0.094
17	0.027	0.047	0.061
18	0.058	0.077	0.074

The positive and negative ideal solutions are arranged by using Equations (6) and (7), respectively. The positive ideal solution $I_{i}^{*}$ and negative ideal solution $I_{i}^{-}$ are obtained, as shown in Table 6:

I * = {(max v_{ij} | j \in B), (min v_{ij} | j \in C) | i = 1, 2, 3 \dots, m} = {v_{1}^{*}, v_{1}^{*}, \dots, v_{n}^{*}}

(6)

I - = {(min v_{ij} | j \in B), (max v_{ij} | j \in C) | i = 1, 2, 3 \dots, m} = {v_{1}^{-}, v_{1}^{-}, \dots, v_{n}^{-}}

(7)

The separation measures

S_{i}^{*}

and

S_{i}^{-}

are calculated by Equations (8) and (9), and then the relative degree of approximation is calculated, as shown in Table 7.

Table 6.

The positive and negative ideal solutions

	Strength	Elongation	Denier
Positive ideal solution	0.123	0.117	0.116
Negative ideal solution	0.027	0.034	0.054

Table 7.

The relative degree of approximation

No.	$C_{i}^{+}$
1	0.733
2	0.707
3	0.344
4	0.613
5	0.804
6	0.044
7	0.429
8	0.280
9	0.212
10	0.896
11	0.839
12	0.043
13	0.141
14	0.811
15	0.199
16	0.785
17	0.015
18	0.301

The positive ideal solution distance is

S_{i}^{*} = \sqrt{\sum_{j = 1}^{n} (v_{ij} - v_{j}^{+}}) 2, i = 1, 2, \dots, m

(8)

The negative ideal solution distance is

S_{i}^{-} = \sqrt{\sum_{j = 1}^{n} (v_{ij} - v_{j}^{-}}) 2, i = 1, 2, \dots, m

(9)

Relative degree of approximation $C_{i}^{+}$ :

C_{i}^{+} = \frac{S_{i}^{-}}{S_{i}^{-} + S_{i}^{*}}

(10)

Use the Taguchi method to plot the response graph

Figure 5 shows the optimum factor levels of A1, B1, C1, D3, E1, F2 and G3. The dispersant proportion is 0.1 wt%, the SiO₂ proportion is 0.5 wt%, the ZnO proportion is 0.5 wt%, the screw speed is 400 rpm, the spinneret temperature is 210℃, the spinneret rotation speed is 12 Hz and the drafting rotation speed is 6.5 Hz.

The fiber made from the multi-quality optimum parameter was compared with the original PP performance. The comparison of the fiber process properties are shown in Table 8, and the comparison of the functional properties of fiber are shown in Table 9. The antibacterial test for fiber is shown in Figure 6. As can be seen, the staphylococcus aureus barrier strip is 1.8 mm; the pneumobacillus barrier strip is 1.6 mm.

Table 8.

The fiber process properties

	Strength (g)	Elongation (%)	Denier (g)
PP	204.61	632.62	29.7
Optimal fiber	243.58	728.24	33.5

PP: polypropylene.

Table 9.

The functional properties of fiber

	Far-infrared ray emission rate	Far-infrared ray emission temperature rise	Antibacterial
PP	42%	6℃	No
Optimal fiber	85%	8.6℃	Yes

PP: polypropylene.

Figure 5.

Response graph.

Figure 6.

The antibacterial test. (a) Staphylococcus aureus. (b) Pneumobacillus. (c) Polypropylene.

SEM observation

This study used SEM to observe the fiber dispersion after the addition of powder. Different powder proportions were shot.

On the fiber surface magnified 500 times, due to the high powder addition level, the projection and agglomeration were obvious, and the powder dispersed over the fiber surface. In terms of the fiber magnified 3000 times, with a high powder content, the fiber surface appeared to be more irregular, and the agglomerates increased obviously. However, the agglomerates were smaller than 60 µm, so the powder agglomeration did not cause stubble in melt spinning, and the addition of 0.1 wt% dispersant did not result in large agglomerates. Figure 7 shows the SEM graph of 1.5 wt% SiO₂ and 1.5 wt% ZnO. The dispersant of A and B was 0.1 wt% and 0.05 wt%, respectively. In SEM graphs of Figure 8, A is PP fiber; B is PP fiber including 0.5 wt% SiO₂ and ZnO; C is PP fiber including 1.0 wt% SiO₂ and ZnO; D is PP fiber including 1.5 wt% SiO₂ and ZnO. In Figure 9, A, B and C are 500 times scanning electron micrographs; A includes 0.5 wt% SiO₂ and 0.5 wt% ZnO; B includes 1 wt% SiO₂ and 1 wt% ZnO; C includes 1.5 wt% SiO₂ and 1.5 wt% ZnO. D, E and F are 3000 times scanning electron micrographs; D includes 0.5 wt% SiO₂ and 0.5 wt% ZnO; E includes 1 wt% SiO₂ and 1 wt% ZnO; F includes 1.5 wt% SiO₂ and 1.5 wt% ZnO.

Figure 7.

Scanning electron micrographs (2000×).

Figure 8.

Scanning electron micrograph cross-sections (4000×).

Figure 9.

Different powder ratios.

Conclusion

The results of the thermogravimetry analyzer showed that the pyrolysis temperature increased as the powder proportion increased, thus enhancing the heat resistance of material. The differential thermal analysis showed that the melting point of pure PP was 169℃ and the crystallization temperature was 117℃. Due to the addition of nano-powder, the degree of crystallinity of composite fiber increased to 130℃, and the PP crystallization temperature and the degree of crystallinity both increased obviously. The addition of nano-powder as a nucleating agent resulted in good crystallinity of composite fiber, thus increasing the crystallinity for resisting heat.

The fiber surface was observed through SEM. The composite fiber surface powder increased and coarsened as the powder proportion increased, but the powder agglomeration was not greater than 60 µm, and thus had no impact on fiber spinning processing.

The test for multi-quality optimization composite fiber found that the tensile strength of pure PP is 2.005 N, the composite fiber is 2.387 N, the property gain is 19%, the elongation is 6.32%, the composite fiber is 7.26% and the property gain is 14%.

According to the functionality of optimum fiber, after 10 min of far-infrared ray emission temperature rise standard testing, the temperature rise was 8.6℃, which is higher than that of PP by 1.43 times, higher than that of polyethylene terephthalate (PET) by 1.53 times, and higher than nylon by 1.75 times. The composite powder formulation had a better temperature rise than single powder. The temperature difference between the fiber of this study and standard sample was above 0.5℃, meeting the standard of the far-infrared ray temperature rise characteristic. The far-infrared ray emission rate of optimal fiber was 85%, increasing the PP emittance value by 2.02 times, which is above the 80% of emissivity in industrial practice. The antibacterial measurement showed that the fiber produced in this study is effective on resisting staphylococcus aureus and pneumobacillus.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Science Council of the Republic of China (grant no. 100-2221-E-011-046-MY3).

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. Gray relational analysis for recognizing fabric defects. Text Res J 2003; 73: 461–465.

16.

Lin

Twu

. Combination of a fuzzy analytic hierarchy process (FAHP) with the technique for order preference by similarity to ideal solution (TOPSIS) for fashion design scheme evaluation. Text Res J 2012; 82: 1065–1074.