Functional dyeable polypropylene fabric development and process parameter optimization. Part II: Development of graphene thermal insulation dyeable polypropylene fabric with process parameter optimization

Abstract

This series of studies developed a nanographene modified dyeable polypropylene yarn with far-infrared properties making it suitable for winter clothing fabrics. In this paper, part II, the dyeable polypropylene granules developed in part I are used as a base material and, with the addition of graphene nanopowder, melt-mixed and melt-spun to produce 75 d/24 f fully drawn yarn. The physical properties of the yarn, namely, yarn count, tensile strength, elongation at break, far-infrared emissivity, and far-infrared temperature rise, are investigated, and the impact of the melt-spinning process parameters, namely, graphene nanopowder content, mold temperature, melt temperature, gear pump speed, hot roller speed and take-up speed on the quality of the yarn, is determined. The Taguchi method, combined with gray relational analysis, is used to design experiments through which an optimal set of melt-spinning process parameters maximizing the multi-characteristic quality of the yarn is obtained. This optimized nanographene modified dyeable polypropylene yarn has a tensile strength of 3.5 g/d, elongation at break of 41.0%, yarn count of 75.3 d, far-infrared emissivity of 82%, far-infrared temperature rise of 21.0°C, washing fastness grade of 3–4 and surface resistance of 3 × 10⁸ Ω.

Keywords

Dyeable polypropylene fabric far-infrared temperature rise antistatic resistance Taguchi method gray relational analysis

The most common method by means of which clothing provides warmth is active heat-generating heat preservation. It is achieved by adding heat-generating devices or using heat-generating fiber materials to heat the human body actively to keep it warm. Among fiber materials, polypropylene fiber¹ has the lowest thermal conductivity coefficient of all fibers and has the advantages of light weight and warmth. Therefore, polypropylene fiber can effectively maintain human body temperature and make the wearer feel warm.

Polypropylene has a polyolefin structure and generally cannot be dyed with disperse dyes. Usually, dope dye is added to the raw polypropylene, which is kneaded and processed, before melt-spinning to impart color. In this study, dyeable polypropylene with disperse dyes were used to improve the functions of the usually nondyeable polypropylene. Due to the functional properties of dyeable polypropylene fabric not being sufficient to improve thermal insulation, the main aim of this research was to develop a nanographene modified dyeable polypropylene fiber that has far-infrared radiation, heat generation and antistatic properties suitable for the production of warm functional fabrics.

Polypropylene is a thermoplastic material, and it is used extensively due to its ease of processing, light weight, low density, low cost, and high recyclability properties. However, the applications of polypropylene are clearly limited due to some disadvantages, including high molding shrinkage, low stiffness, and poor impact toughness.^2,3 Thus, polypropylene is usually modified through blending with other resins and the introduction of inorganic fillers.⁴ Kotek et al.⁵ presented the effect of a specific β-nucleation on the morphology and mechanical behavior of isotactic polypropylene. The supermolecular structure of the specimens correlated with mechanical behavior. Kotek et al.⁶ demonstrated the tensile behavior of isotactic polypropylene modified by specific nucleation and active fillers. A commercial-grade polypropylene promotes crystallization predominantly in the β-phase. Kukakova et al.⁷ confirmed that the changes in the toughness of the microcomposites are closely related to the increased formation of the partially ordered trans-crystalline fraction of polypropylene chains. Policianova et al.⁸ sought to explore the origin of the toughness in β-phase isotactic polypropylene (i-PP). Samples of commercial-grade i-PP indicated larger restrictions in chain mobility in the amorphous phase of the α-polymorphic polypropylene system than in that of the β-crystalline phase. On the basis of these studies, it can be seen that the presence of inorganic nanofiller can induce modification of the polymorphic form of the crystalline phase of polypropylene.

One such filler, graphene, has attracted great attention in recent years because of its exceptional mechanical and physical properties, including excellent conductivity and high specific strength.⁹ One of the most favored applications of graphene is in polymer nanocomposites, which incorporate nano scale filler materials. The graphene/polymer nanocomposites show excellent mechanical, thermal, gas barrier, electrical, and flame-retardant properties compared with the neat polymer.^3,10,11 Imran et al.¹² applied graphene modified polypropylene polymer to improve its electrical and thermal conductivity. In the case of a coating application, the percolation threshold was found to be 0.5 wt% of graphene and electrical conductivity of polypropylene increased around 13 log cycles. Wang et al.¹³ described the incorporation of graphene platelets into polypropylene to produce polypropylene/graphene platelet self-reinforced polymer composites. The tensile strength, tensile modulus, and interfacial strength were increased, respectively. Huang et al.¹⁴ presented results using polypropylene and graphene nanosheets (GNs) to make polypropylene/GNs conductive composites. The GNs enable polypropylene to crystallize at a high temperature. The tensile modulus of polypropylene/GNs conductive composites remarkably increased as a result of the increasing content of conductive fillers.

To achieve the uniform dispersion of graphene in polypropylene, polypropylene grafted-maleic anhydride (PPg-MA) as a compatibilizer can be compounded with graphene to obtain a master batch, and then melt blend with polypropylene using a micro-compounder.¹⁵ Remarkable enhancements in mechanical, thermal and dielectric properties are acquired at low graphene loadings. Abuoudah et al.¹⁶ reported that the property enhancement was a function of the degree of exfoliation and dispersion of graphene as well as its compatibility with the base polymer. The PPg-MA compatibilized polypropylene/graphene nanocomposites showed reasonably improved melting and mechanical properties. Kalantari et al.¹⁷ introduced PPg-MA into polypropylene/graphene nanoplatelets (GnPs) nanocomposite fiber to increase storage modulus. Flow-induced crystallization could be occurring in the polypropylene/GnPs nanocomposite fiber. About a 20% increase in crystallinity was obtained for the compatibilized polypropylene/GnPs nanocomposite fiber as compared with that of the virgin polypropylene.

Based on the above results, it is seen that: (a) PPg-MA compatibilizer can improve the interface between graphene and polypropylene material, and enhance the mechanical properties of polypropylene; (b) adding graphene to polypropylene polymer can increase its thermal conductivity and thermal stability; (c) polypropylene and graphene mixing can improve the tensile strength and impact strength of composite materials and the crystallinity of polypropylene. In addition, the far-infrared emissivity of graphene reaches 0.97, which is close to the theoretical upper limit of 1.¹⁸ Because graphene has excellent far-infrared emission characteristics, Hu et al.¹⁹ presented multifunctional cotton fabrics with a graphene/polyurethane coating with far-infrared emission properties.

In this study, in the preparation of nanographene modified dyeable polypropylene fibers, nanographene powder was added to dyeable polypropylene particles. The powder was uniformly dispersed by twin-screw mixing, and the fibers were made by melt-spinning. From Taguchi methods, the optimum processing parameters for each single quality were obtained. Then it would combine with gray relational analysis (GRA) to quantify five qualities into one index to obtain the best combination of multiple quality traits. Finally, through the performance of confirmation experiments and the calculation of the confidence interval (CI), the prediction error rate appeared to be less than 5%.

Materials and material processing

Materials

Dyeable polypropylene

The dyeable polypropylene granule is developed in part I.²⁰ The melting point is 160.56°C, and the melting index is 37.88 g/10 min.

Polypropylene grafted-maleic anhydride

The PPg-MA is from Kunyu Industry Co. Ltd. Model: MAPP, material melting point: 166.4°C, melting index: 100 g/10 min.

Nanographene powder

Supplier: Taiwan Avient Co. Ltd. Particle size: less than 25 nm.

Material processing

Mixing process

Polypropylene was ground and melted with nanographene in a dispersant (PPg-MA) from JSW, a twin screw extruder²¹ to get the uniformity. The twin screw extruder process is shown in Figure 1.

Figure 1.

Twin screw extruder process for materials blending.

Melt-spinning process

The melt-spinning equipment was from TMT Spinning System, Osaka, Japan.²² It consists of a feeder, an extruder unit (extrusion zone and heating zone), gear pump, spinneret, cooling and passing through the fiber take-up system as shown in Figure 2.

Figure 2.

The melt-spinning process.

Methods

Taguchi method experiment design

The Taguchi method^23–28 used an orthogonal array to design experiments to study the influence of the control factors. The signal-to-noise (S/N) ratio is employed to analyze the experimental data to find the optimum parameter for each quality.

Orthogonal array

The orthogonal array used is L₁₈ (3⁶). There are 18 experiments and six three-level factors.

S/N ratio

The melt-spinning process parameters maximizing the multi-characteristic qualities of the yarn are the yarn count, tensile strength, elongation at break, far-infrared emissivity, and far-infrared temperature rise. They are expected to be obtained as the maximum. The quality characteristics are Larger-the-Better, and the corresponding S/N ratio is:

S / N ratio = - 10 \log (\frac{1}{n} \sum_{i = 1}^{n} \frac{1}{y_{i}^{2}})

(1)

where y_i is the measured value of quality i, and n = 3 is the total number of measurements in this study.

Main effects analysis

Main effects analysis (MEA)²⁴ was used to decide the effect of changes in specific factor levels on the observed values, and a response graph was applied to observe the corresponding factor values to determine the most influential variation factor of the entire experiment. The larger the main effect value of a factor is, the greater the influence that factor has on the system, in comparison with the other factors.

Analysis of variance

The analysis of variance (ANOVA)^27,28 is used to evaluate the experimental error and confirm the influence of each factor on the significant factors in the product process. The control factors of the melt-spinning process include nanographene content, mold temperature, metering pump speed, melting temperature, hot roll speed and take-up speed. The factor with a greater degree of significance means that changing the process parameters can effectively achieve process benefits. The ANOVA table in this study²⁷ for five control factors (A–F) is shown in Table 1.

Table 1.

ANOVA table

Factor	SS	DOF	MS	F ratio	SS′	$ρ$
A	SS_A	DOF_A	MS_A	F_A	SS^′_A	$ρ_{A}$
F	SS_F	DOF_F	MS_F	F_F	SS^′_E	$ρ_{E}$
Error	SS_Error	DOF _Error	MS _Error
Combined error	SS_CE	DOF_CE	MS_CE		SS^′_CE	$ρ_{C E}$
Total	SS_T	DOF_T			SS^′_T	100%

ANOVA: analysis of variance.SS is the sum of squares; dof is the degrees of freedom of a single factor equal to its level number minus one; MS is mean square; F ratio: the larger F ratio represents a more significant effect of the factor on the system; SS' is the partial sum of square; $ρ$ is the contribution (the percentage of contribution represents the proportion of each factor effect on the quality); Error is the experimental error of the assessment; and Combined error is the incorporation of insignificant factors in the analysis of variables into the error term, in which insignificant factors are factors with a lower contribution.

Confidence interval

The confirmation experiment is to determine the experiment results, and the predicted results conform to a certain probability within a certain CI.²⁹ It is used to verify that the mathematical model established for the orthogonal array experimental data is suitable. The formula is as follows:

C I = \sqrt{F_{α; 1, V_{2}} \times V_{e} \times (\frac{1}{n_{eff}} + \frac{1}{r})}

(2)

where

F_{α; 1, v_{2}}

is the F value with significance level α; α is the significance level, the confidence level is 1-α;

v_{2}

is the dof of the pooled error mean square;

V_{e}

is the pooled error mean square; r is the total number of experiments, and

n_{eff}

is the effective observed value. The 95% CI is used to validate that the predicted average is effective. The verification expression is:

\hat{S} N - C I \leq μ \leq \hat{S} N + C I

(3)

Multi-objective optimization using GRA

The objective of this study was to establish the optimal combination of the melt-spinning process parameters that at the same time maximize the multi-characteristic response of the yarn. This study applied GRA to transform optimization consideration from a multi-response to a single response. The steps of GRA are as follows:²³

Step 1: Gray relational generation with normalization (in the range between 0 and 1)

As the factors come from different experiments and are measured in different units, the data must be converted into the same scale to normalize the experimental data. The objective value of the original sequence in this study has a ‘the larger the better’ characteristic and the original sequence will be normalized as follows:

Y_{i}^{*} (j) = \frac{Y_{i} (j) - \min (Y_{i}^{0} (j))}{\max (Y_{i}^{0} (j)) - \min (Y_{i}^{0} (j))}

(4)

where

Y_{i}^{*} (j)

and

Y_{i} (j)

are normalized and experimental observed values, respectively.

\max (Y_{i}^{0} (j))

and

\min Y_{i}^{0} (j))

are the largest and smallest values of

Y_{i}^{0} (j)

for the jth response, respectively, where j = 1, 2, …, m., i = 1, 2, …, n, the index m is the number of responses and index n is the count of experimental data parameters.

Step 2: Gray relational coefficient

The grey relational coefficient $δ_{i} (j)$ can be determined as equation (5). It indicates the relationship between the optimal and the real experiment results:

δ_{i} (j) = \frac{T_{\min} + {λ T}_{\max}}{T_{o i} (j) + {λ T}_{\max}}

(5)

0 < δ_{i} (j) \leq 1

(6)

T_{o i} (j) = ∥ Y_{o}^{*} (j) - Y_{i}^{*} (j) ∥

(7)

where

T_{o i} (j)

is the deviation sequence,

Y_{o}^{*} (j)

is the reference sequence,

Y_{i}^{*} (j)

is the comparability sequence,

T_{\min}

is the smallest value of the deviation sequence,

T_{\max}

is the largest value of the deviation sequence,

λ

is the distinguished coefficient in the range of

0 < λ \leq 1

. It is generally taken that

λ

is 0.5, if all parameters have same weight, to fit the practical requirements.²⁸

Step 3: Gray relational grade (GRG) representation

The GRG expresses the similarity between the compared series and reference series. It can be represented as:

β_{i} = \frac{1}{n} \sum_{j = 1}^{n} δ_{i} (j)

(8)

where

n

is the number of quality characteristics (in this study n is 5). The higher GRG indicates that the corresponding parameter combination is more relevant to the best.

The optimal parameter setting for optimizing the overall GRG can be executed by the Taguchi method. The best combination of design parameters can be obtained from the response graph and response table in the main effect analysis.

Experimental process and planning

The fabrication of the nanographene modified dyeable polypropylene fiber in this study include:

Taguchi experimental design;

twin-screw mixer and melt-spinning process for material processing;

fiber properties testing;

optimal process analysis;

confirmation experiment.

The experimental process and planning are shown in Figure 3.

Figure 3.

Schematic diagram of the experimental process and planning.

Melt-spinning process parameter selection

The process parameters in this study were selected based on the following considerations:

Nanographene powder addition

In the melt blending process of a twin-screw mixer, different sizes of dyeable polypropylene and nanographene powder tend to induce nonuniform mixing, leading to concentrated stress and degraded mechanical properties of materials. The compatibility agent, PPg-MA (2%), is applied to improve their interfaces and enhance the mechanical properties of the polypropylene composite.^15–17 The nanographene powder addition level was based on test results: the fiber with a powder ratio lower than 1.0 wt% exhibited a poor performance, and when the powder ratio exceeded 2.0 wt%, the yarn break occurred in the melt-spinning process. In this experiment, the content of nanographene master batch obtained by biaxial processing is 1.0 wt%, 1.5 wt% and 2.0 wt%.

Melt-spinning temperature

A temperature lower than the pyrolysis temperature of dyeable polypropylene (288.04°C) was selected. According to our experiment, the fiber is to be spun in the melt-spinning process for a temperature range of 220–240°C. Therefore, the melt-spinning process temperature of 220–240°C was applicable to the dyeable polypropylene material.

Gear pump speed

Considering the specification of gear pumps, the speed of the gear pump speed was 19∼23 rpm for producing the 75 d fully drawn yarn according to the empirical value of this machine.

Hot roller speed and take-up speed

The take-up speed was 2300–2500 m/min according to the processing speed of the take-up machine in producing the 75 d fully drawn yarn. The heat roller speed was higher than the take-up speed range by 60 m/min for take-up forming to reduce the product tension. Therefore, the speed of the hot roller was set at 2360–2560 m/min.

Experimental planning

This study focused on the nanographene powder addition ratio and combined it with the important processing parameters in the melt-spinning process of melting temperature, mold temperature, gear pump speed, hot roller speed and take-up speed as control factors, designing a six control factor and three-level (3⁶) experiment, as shown in Table 2.

Table 2.

Parameter levels of nanographene modified fiber experiments

Control factor		Level
Control factor		1	2	3
A	Nanographene powder (wt%)	1.0	1.5	2.0
B	Mold temperature (°C)	220	230	240
C	Gear pump speed (rpm)	19	21	23
D	Melt temperature (°C )	230	235	240
E	Hot roller speed (m/min)	2360	2460	2560
F	Take-up speed (m/min)	2300	2400	2500

The L18 orthogonal array^25–27 was used for the design of the experiments. The yarn count of the fiber was tested according to the ASTM D1577 standard. The tensile strength and the elongation at break were tested according to the ASTM D3822 standard. The far-infrared emissivity and temperature rise were tested according to the FTTS-FA-010 standard.³⁰ The results for the three iterations of the 18 experiments, with averages, and S/N ratios of five quality characteristics are shown in Table 3.

Table 3.

L18 orthogonal array of experimental data

Exp. no.	Yarn count		Tensile strength		Elongation at break		Far-infrared emissivity		Far-infrared temperature rise
Exp. no.	Mean (d)	S/N ratio (dB)	Mean (g/d)	S/N ratio (dB)	Mean (%)	S/N ratio (dB)	Mean (%)	S/N ratio (dB)	Mean (°C)	S/N ratio (dB)
1	75.3	37.53	3.2	10.11	41.0	32.26	80.6	38.13	20.1	26.07
2	79.6	38.01	3.4	10.50	39.4	31.90	80.2	38.09	20.1	26.05
3	78.9	37.92	3.4	10.58	38.9	31.78	80.9	38.16	20.2	26.12
4	74.9	37.48	3.3	10.36	37.6	31.51	81.1	38.17	20.3	26.13
5	76.4	37.59	3.3	10.33	43.0	32.66	81.1	38.18	20.6	26.29
6	77.6	37.77	3.4	10.51	42.0	32.45	80.6	38.12	20.2	26.09
7	76.8	37.70	3.3	10.37	39.2	31.86	82.5	38.33	21.7	26.71
8	74.2	37.37	3.3	10.40	40.2	32.08	82.2	38.29	21.6	26.67
9	78.4	37.85	3.4	10.72	40.9	32.22	82.4	38.31	21.6	26.67
10	77.1	37.73	3.4	10.55	40.3	32.09	79.8	38.03	20.9	26.41
11	71.8	37.09	3.4	10.52	39.3	31.88	79.4	37.99	20.8	26.37
12	77.5	37.76	3.2	10.17	47.1	33.44	83.1	38.38	20.5	26.22
13	76.6	37.68	3.3	10.45	39.5	31.93	81.7	38.24	21.4	26.62
14	77.5	37.77	3.5	10.78	41.2	32.28	83.4	38.42	21.8	26.77
15	77.1	37.73	3.4	10.69	41.2	32.30	82.6	38.33	21.6	26.70
16	77.1	37.73	3.4	10.57	48.5	33.66	82.8	38.35	21.7	26.73
17	78.2	37.86	3.4	10.60	41.7	32.40	84.0	38.48	22.0	26.84
18	73.5	37.30	3.4	10.63	41.7	32.40	82.8	38.36	21.7	26.74

Results and discussion

Yarn count single quality optimization analysis

Main effects analysis

The MEA of the yarn count obtained from all experiments was used to compile the response graph shown in Figure 4.

Figure 4.

Response graph of yarn count.

The response graph shows that the best factor levels were A1 (1% nanographene powder content), B3 (240°C mold temperature), C3 (gear pump speed 23 rpm), D3 (melt temperature 240°C), E2 (hot roller speed 2460 m/min), and F2 (take-up speed 2400 m/min). The control factors from the most influential to the least influential were take-up speed > melt temperature > gear pump speed > mold temperature > hot roller speed > nanographene powder content.

Analysis of variance

In order to quantify the degree of influence further, the study calculated the contribution of each factor to the quality characteristics through ANOVA. The ANOVA of the yarn count is shown in Table 4.

Table 4.

Yarn count ANOVA

Factor	DOF	SS	MS	F ratio	SS′	Contribution (%)
A	2	0.0055	0.0027	0.0489	–	–
B	2	0.0376	0.0188	0.3337	–	–
C	2	0.0493	0.0246	0.4371	–	–
D	2	0.1827	0.0913	1.6191	0.0698	7.7325
E	2	0.0121	0.0060	0.1075	–	–
F	2	0.3340	0.1670	2.9606	0.2212	24.4859
Error	5	0.280	0.0564	–	–	–
Combined error	13	0.810	0.0623	–	0.6123	67.7816
Total	17	0.903	–	–	0.903	100

ANOVA: analysis of variance.

From Table 4, it is confirmed that the larger the F ratio, the greater the influencing factor. The factor that has the greatest influence on the yarn count is F (take-up speed), followed by D (melting temperature), C (gear pump speed), B (mold temperature), E (hot roller speed), and A (nanographene powder content).

Confirmation experiment

A confirmation experiment was designed for the major control factors D3 and F2 using their optimal parameter levels, as shown in Table 5.

Table 5.

Yarn count confirmation experiment (nit: denier)

Control factor	Data 1	Data 2	Data 3	Average	S/N (dB)
D3, F2	75.7	76.8	76.5	76.3	37.65

According to Table 5, using equation (3), CI was calculated as 0.42, and the 95% CI was 37.49 ≦ µ ≦8.33. The experiment result of µ = 37.65 fell in the 95% CI. This shows that the optimized parameter combination obtained by the Taguchi method has good reproducibility.

Tensile strength single quality optimization analysis

Main effects analysis

The MEA of the tensile strength data obtained from all experiments was used to compile the response graph shown in Figure 5.

Figure 5.

Response graph of tensile strength.

The response graph shows that the best factor levels were A3 (2% nanographene powder content), B3 (mold temperature 240°C), C3 (gear pump speed 23 rpm), D3 (melt temperature 240°C), E2 (hot roller speed 2460 m/min), and F2 (take-up speed 2400 m/min). The control factors from the most influential to the least influential are hot roller speed > gear pump speed > mold temperature > take-up speed > nanographene powder content > melt temperature.

Analysis of variance

The ANOVA of tensile strength is shown in Table 6.

Table 6.

ANOVA of tensile strength

Factor	DOF	SS	MS	F ratio	SS′	Contribution (%)
A	2	0.0698	0.0349	1.327	0.017	3.178
B	2	0.0727	0.0363	1.381	0.020	3.705
C	2	0.0727	0.0363	1.381	0.020	3.704
D	2	0.0240	0.0120	0.457	–	–
E	2	0.145	0.0723	2.748	0.092	16.980
F	2	0.0263	0.0132	0.501	–	–
Error	5	0.130	0.0263	–	–	–
Combined error	9	0.252	0.029	–	0.409	72.433
Total	17	0.542	–	–	0.542	100

ANOVA: analysis of variance.

It can be observed from Table 6 that the factor that has the greatest influence on the tensile strength is E (hot roller speed), followed by B (mold temperature), C (gear pump speed), A (nanographene powder content), F (take-up speed), and D (melt temperature).

Confirmation experiment

A confirmation experiment was designed for the major control factors A3, B3, C3 and E2 using their optimal parameter levels, as shown in Table 7.

Table 7.

Tensile strength confirmation experiment (unit: g/d)

Control factor	Data 1	Data 2	Data 3	Average	S/N (dB)
A3, B3, C3, E2	3.5	3.4	3.4	3.4	10.71

The CI was calculated as 0.35, and the 95% CI was 10.45 ≦ µ ≦ 11.15. The experiment result of µ = 10.71 fell in the 95% CI. This shows that the optimized parameter combination obtained by the Taguchi method has good reproducibility.

Elongation at break single quality optimization analysis

Main effects analysis

The MEA of the elongation data obtained from all experiments was used to compile the response graph shown in Figure 6.

Figure 6.

Response graph of elongation at break.

The response graph shows that the best factor levels were A3 (nanographene powder content 2%), B3 (mold temperature 240°C), C3 (gear pump speed 23 rpm), D2 (melt temperature 235°C), E1 (hot roller speed 2360 m/min), and F1 (take-up speed 2300 m/min). The control factors from the most influential to the least influential are take-up speed > hot roller speed > melt temperature >mold temperature > gear pump speed > nanographene powder content.

Analysis of variance

The ANOVA of elongation at break is shown in Table 8.

Table 8.

ANOVA of elongation at break

Factor	DOF	SS	MS	F ratio	SS’	Contribution (%)
A	2	0.2136	0.1068	0.6325	–	–
B	2	0.2012	0.1006	0.5959	–	–
C	2	0.3205	0.1602	0.9490	–	–
D	2	0.3494	0.1747	1.0347	–	–
E	2	0.4314	0.2157	1.2770	0.0937	2.075
F	2	2.1578	1.0789	6.3892	1.8201	40.282
Error	5	0.84	0.1688	–	–	–
Combined error	13	1.9290	0.1483	–	3.331	57.643
Total	17	4.52	–	–	4.52	100

ANOVA: analysis of variance.

It can be observed from Table 8 that the factor that has the greatest influence on the elongation at break is F (take-up speed), followed by E (hot roller speed), D (melt temperature), C (gear pump speed), A (nanographene powder content), and B (mold temperature).

Confirmation experiment

A confirmation experiment was designed for the major control factors E1 and F1 using their optimal parameter levels, as shown in Table 9.

Table 9.

Elongation at break confirmation experiment (unit: %)

Control factor	Data 1	Data 2	Data 3	Average	S/N (dB)
E1, F1	46.1	46.8	45.8	45.3	33.26

The CI was calculated as 0.65, and the 95% CI was 32.25 ≦ µ ≦ 33.55. The experiment result of µ = 33.26 fell in the 95% CI. This shows that the optimized parameter combination obtained by the Taguchi method has good reproducibility.

Far-infrared emissivity single quality optimization analysis

Main effects analysis

The MEA of the far-infrared emissivity data obtained from all experiments was used to compile the response table and response graph shown in Figure 7.

Figure 7.

Response graph of far-infrared emissivity.

The response graph shows that the best factor levels were A3 (nanographene powder content 2%), B3 (mold temperature 240°C), C2 (gear pump speed 21 rpm), D2 (melt temperature 235°C), E1 (hot roller speed 2360 m/min) and F1 (take-up speed 2300 m/min). The control factors from the most influential to the least influential are nanographene powder content > take-up speed >mold temperature > hot roller speed > melt temperature >gear pump speed.

Analysis of variance

The ANOVA of far-infrared emissivity is shown in Table 10.

Table 10.

ANOVA of far-infrared emissivity

Factor	DOF	SS	MS	F ratio	SS’	Contribution (%)
A	2	0.1517	0.0758	40.0699	0.1479	66.306
B	2	0.0145	0.0072	3.8459	0.0107	4.829
C	2	0.0035	0.0018	0.9460	–	–
D	2	0.0071	0.0036	1.8983	–	–
E	2	0.0088	0.0044	2.3355	0.0050	2.226
F	2	0.0277	0.0138	7.3275	0.0239	10.738
Error	5	0.010	0.0019	–	–	–
Combined error	9	0.029	0.0026	–	0.0404	15.861
Total	17	0.223	–	–	0.223	100

ANOVA: analysis of variance.

It can be observed from Table 10 that the factor that has the greatest influence on the far-infrared emissivity is A (nanographene powder content), followed by F (take-up speed), B (mold temperature), E (hot roller speed), D (melt tmperature), and C (gear pump speed).

Confirmation experiment

A confirmation experiment was designed for the major control factors A3, B3, E1 and F1 using their optimal parameter levels, as shown in Table 11.

Table 11.

Far-infrared emissivity confirmation experiment (unit: %)

Control factor	Data 1	Data 2	Data 3	Average	S/N (dB)
A3, B3, E1, F1	83.5	84.1	82.7	83.4	38.42

The CI was calculated as 0.10, and the 95% CI was 38.38 ≦ µ ≦ 38.58. The experiment result of µ = 38.42 fell in the 95% CI. This shows that the optimized parameter combination obtained by the Taguchi method has good reproducibility.

Far-infrared temperature rise single optimization analysis

Main effects analysis

The MEA of far-infrared temperature rise data obtained from all experiments was used to compile the response graph shown in Figure 8.

Figure 8.

Response graph of far-infrared temperature rise.

The response graph shows that the best factor levels were A3 (nanographene powder content 2%), B2 (mold temperature 230°C), C1 (gear pump speed 19 rpm), D3 (melt temperature 240°C), E3 (hot roller speed 2560 m/min), and F2 (take-up speed 2500 m/min).The control factors from the most influential to the least influential are nanographene powder content > melt temperature > mold temperature > hot roller speed > take-up speed > gear pump speed.

Analysis of variance

The ANOVA of far-infrared temperature rise shown in Table 12.

Table 12.

ANOVA of far-infrared temperature rise

Factor	DOF	SS	MS	F ratio	SS’	Contribution (%)
A	2	0.8156	0.4078	3.7903	0.6004	42.63
B	2	0.0177	0.0087	0.0824	–	–
C	2	0.0032	0.0016	0.0148	–	–
D	2	0.018	0.0088	0.0826	–	–
E	2	0.0138	0.0069	0.0645	–	–
F	2	0.0022	0.0011	0.0102	–	–
Error	5	0.540	0.1076	–	–	–
Combined	15	0.5927	0.0395	–	–	–
error					0.808	57.37
Total	17	1.408	–	–	1.408	100

ANOVA: analysis of variance.

It can be observed from Table 12 that the factor that has the greatest influence on the far-infrared temperature rise is A (nanographene powder content), followed by D (melt tmperature), B (mold temperature), E (hot roller speed), C (gear pump speed), and F (take-up speed).

Confirmation experiment

A confirmation experiment was designed for the major control factor A3 using its optimal parameter level, as shown in Table 13.

Table 13.

Far-infrared temperature rise confirmation experiment (unit: ^oC)

Control factor	Data 1	Data 2	Data 3	Average	S/N (dB)
A3	21.5	22.1	21.4	21.7	26.71

The CI was calculated as 0.30, and the 95% CI was 26.43 ≦ µ ≦ 27.03. The experiment result of µ = 26.71 fell in the 95% CI. This shows that the optimized parameter combination obtained by the Taguchi method has good reproducibility.

Multi-quality optimization analysis

The GRG of GRA was shown in Table 14. The response graph of multi-quality parameter optimization was established from the MEA as shown in Figure 9.

Table 14.

The GRG of multi-quality for Table 3

Exp. no.	GRG	Exp. no.	GRG	Exp. no.	GRG
1	0.4715	7	0.6270	13	0.5987
2	0.5893	8	0.5819	14	0.7649
3	0.5914	9	0.7158	15	0.6985
4	0.4842	10	0.5658	16	0.8264
5	0.5401	11	0.4828	17	0.7782
6	0.5576	12	0.6600	18	0.6538

GRG: gray relational grade.

Figure 9.

Response graph of gray relational grade.

The GRG is an indicator of how close the characteristics are to the reference sequence (37.65, 10.71, 33.26, 38.42, 26.71). The reference sequence is the maximum value of the S/N ratio in the L18 orthogonal table of the five quality characteristics. A rank of 1 indicates complete overlap with the reference sequence. Therefore, the larger the correlation coefficient, the better.

The response graph shows that the best factor levels were A3 (nanographene powder content 2%), B3 (mold temperature 240°C), C3 (gear pump speed 23 rpm), D2 (melt temperature 235°C), E2 (hot roller speed 2460 m/min), and F1 (take-up speed 2300 m/min). The control factors from the most influential to the least influential are nanographene powder content > take-up speed >mold temperature > melt s temperature > gear pump speed > hot roller speed.

Comparing the optimal combinations in Table 15, showing single-quality optimization parameters, it can be seen that the optimization parameter combinations are different.

Table 15.

Single quality optimum parameter combination

ParameterQuality	Nanographene powder (wt%)	Mold temperature (°C)	Gear pump speed (rpm)	Melt temperature (°C)	Hot roller speed (m/min)	Take-up speed (m/min)
Yarn count (d)	1	240	23	240	2460	2400
Tensile strength (g/d)	2	240	23	240	2460	2400
Elongation at break (%)	1	240	23	240	2360	2300
Far-infrared emissivity (%)	2	240	21	235	2360	2300
Far-infrared temperature rise (°C)	2	230	19	240	2560	2500

Therefore, the optimal parameter combination of multiple quality characteristics should be obtained by using GRA.

Confirmation experiment

The confirmation experiments verified that the S/N ratio of the five quality characteristics are all in 95% CI. Yarn count is 37.65 dB (95% CI 37.49∼38.33 dB), tensile strength is 10.71 dB (95% CI 9.45∼11.15 dB), the elongation at breaks is 33.26 dB (95% CI 32.25∼33.55 dB), far-infrared emissivity is 38.42 dB (95% CI 38.38∼38.58 dB), and the far-infrared temperature rise is 26.71 dB (95% CI 26.43∼27.03 dB). This indicates that the multi-quality optimization conclusions derived from the Taguchi and GRA has good reproducibility.

Results of optimized modified polypropylene from GRA

The comparison of the nanographene modified dyeable polypropylene yarn with dyeable polypropylene yarn in the research is shown in Table 16. It can be seen that the quality is comparable not only with the nonmodified fiber, but also its functional far-infrared emissivity and far-infrared temperature rise are significantly improved.

Table 16.

Comparison of the nanographene modified dyeable polypropylene yarn and dyeable polypropylene yarn in the research

QualityMaterial	Yarn count (d)	Tensile strength (g/d)	Elongation at break (%)	Far-infrared emissivity (%)	Far-infrared temperature rise (°C)
Dyeable polypropylene	76.6	4.20	39.8	77	4.6
Optimized modified polypropylene by GRA	75.3	3.50	41.0	82	21.0

GRA: gray relational analysis.

The optimized nanographene modified dyeable polypropylene yarn in this study has a tensile strength of 3.5 g/d, which exceeds the industry standard of 3.0 g/d. Its elongation at break is 41%, which is 1.2% higher than the 39.8% of the dyeable polypropylene yarn. These mechanical property improvements are the result of experimentation with the production parameters. The denier number of the optimized nanographene modified dyeable polypropylene yarn is 75.3 d, which is closer to the target value (75 d/24 f) than the 76.6 d of the dyeable polypropylene yarn. This shows that the multi-quality optimization methods adopted by this study can result in yarn quality characteristics that are closer to target values.

The far-infrared emission rate of the optimized nanographene modified dyeable polypropylene yarn is 82%, which is 5.0% higher than the 77% of the dyeable polypropylene yarn. The far-infrared temperature rise is 21.0°C, which is 16.4°C higher than the 4.6°C of the dyeable polypropylene yarn, as shown in Figure 10. This achieves the 80% emissivity and 0.5°C temperature rise FTTS-FA-010 far-infrared textile industry standards. This development of multifunctional nanographene modified dyeable polypropylene fabric is suitable for use winter clothing fabrics.

Figure 10.

The far-infrared temperature rise curve of optimized modified yarn.

Comparison of 75 d/24 f optimized fabric dyed with disperse dyes

The results of dyeing fabric woven from the optimized nanographene modified dyeable polypropylene yarn 75d/24 fully drawn yarn and from dyeable polypropylene with red, yellow, and blue disperse dyes are shown in Figure 11.

Figure 11.

75 d/24 f Fully drawn yarn fabric dyed yellow, blue, red color.

The optimized nanographene modified dyeable polypropylene yarn developed in this study has a high affinity for disperse dyes because both the dyeable polypropylene and nanographene modified dyeable polypropylene fabric contains low melting point polybutylene adipate terephthalate copolymer. The modified polypropylene fabric contains graphene nanopowder, and its fiber crystallinity is increased by 4.6%. Therefore, the absorption of disperse dyes by the yarn is slightly reduced, and the coloration is lighter. However, as the graphene nanopowder itself is black, the fabric appears darker relative to the dyeable polypropylene fabric.

Physical properties of nanographene modified dyeable polypropylene yarn

The dispersion of nanographene powder in the fiber was observed with scanning electron microscopy (SEM). The thermal stability of the fiber was investigated using thermogravimetric analysis (TGA) and a differential scanning calorimeter (DSC). In addition, the surface resistance of the fiber was analyzed with a resistivity meter. The washing fastness of fabrics was tested according to AATCC 61-1981A.³¹

Fiber surface observation

The SEM (model Hitachi TM 3000, test conditions: accelerating voltage 10 kV multiplier × 500∼3000) was used to observe the graphene nanopowder particle size in cross-sections of the nonmodified and modified yarns having different proportions of nanographene powder content (1.0 wt% to 2.0 wt%), with results as shown in Figure 12. It can be seen from Figure 13 that as the proportion of nanographene powder increases, the distribution of particle content in the fiber also increases, which confirms that the nanographeme powder in this study is indeed mixed in with polypropylene in the fiber.

Figure 12.

Scanning electron microscopy (SEM) observation of fiber cross-section. Magnification is 2000 times. (a) Dyeable polypropylene; (b) 1.0 wt% nanographene powder; (c) 1.5 wt% nanographene powder and (d) 2.0 wt% nano graphene powder.

Figure 13.

Observations of the fiber distribution with 2.0 wt% nanographene powder content at different magnification. (a) Magnification is 500 times; (b) magnification is 1000 times; (c) magnification is 2000 times and (d) magnification is 3000 times.

The observations of the fiber distribution with 2.0 wt% nanographene powder content at different magnifications are shown in Figure 13. They confirm that the graphene nanopowder did not agglomerate, but was evenly dispersed in the fiber. It can be concluded that PPg-MA compatibilizer improves the mixing and dispersibility of dyeable polypropylene and graphene materials, and that this good dispersion is achieved by interaction between maleic anhydride groups and graphene terminal hydroxyl groups.

2. Thermal properties of nanographene modified dyeable polypropylene yarn

The DSC (model Perkin Elmer DSC 4000) was used to analyze the melting point temperature and crystallinity of the modified and nonmodified yarns developed in this study. The test conditions were: (a) the first stage of heating: 10°C/min from 0°C to 250°C; (b) the second stage of cooling: 3°C/min from 250°C to 0°C; (c) the third stage of heating: 10°C/min from 0°C to 250°C.

TGA (model TA-Q500) was mainly used to analyze the thermal pyrolysis temperature of the optimized nanographene modified dyeable polypropylene yarn developed in this study. The test conditions were: the temperature range was 0∼600°C.

The thermal properties, surface resistance and fabric washing fastness of the optimized nanographene modified dyeable polypropylene yarns developed in this study are shown in Table 17.

Table 17.

Physical properties of optimized nanographene modified dyeable polypropylene yarn

	Sample property	Dyeable polypropylene	Nanographene modified dyeable polypropylene
DSC	Melting point (°C)	161.5	162.6
	Crystallinity (%)	42.9	47.5
	Crystallization temperature (°C)	120.12	122.9
TGA	Pyrolysis temperature (°C)	317.54	334.86
TGA	Weight loss 1% pyrolysis temp. (°C)	288.04	303.99
Surface resistance	Surface resistance (Ω)	10¹²	3 × 10⁸
Washing fastness	Washing fastness of three colors of dyeing fabric (grade)	3–4	3–4

DSC: differential scanning calorimeter; TGA: thermogravimetric analysis.

It can be observed from Table 17:

DSC: that the melting point, crystallinity and crystallization temperature of the optimized nanographene modified dyeable polypropylene yarn are higher than those of the dyeable polypropylene, indicating that the addition of graphene nanopowder can improve the crystallinity and melting point of the dyeable polypropylene.

TGA: that the thermal pyrolysis temperature of the optimized nanographene modified dyeable polypropylene yarn is higher than that of dyeable polypropylene, which proves that the optimized nanographene modified dyeable polypropylene yarn has higher heat resistance. The thermal pyrolysis temperature of the optimized nanographene modified dyeable polypropylene yarn is higher than that of dyeable polypropylene, which proves that the optimized nanographene modified dyeable polypropylene yarn has higher heat resistance.

Surface resistance: that the surface resistance of the optimized nanographene modified dyeable polypropylene yarn is lower than that of dyeable polypropylene, showing that adding nanographene powder can reduce static electricity.

Washing fastness: that the optimized nanographene modified dyeable polypropylene yarn developed has high affinity for disperse dyes and good washing fastness.

Fourier-transform infrared (FTIR) spectroscopy analysis

Functional group analysis of dyeable polypropylene, PPg-MA, 20 wt% graphene master batch, and dyeable polypropylene with nanographene using Fourier transform infrared (FTIR) (model Digilab FTS 1000, test conditions: the range of wavelengths is 400 to 4000 cm⁻¹) is shown in Figure 14.

Figure 14.

Fourier transform infrared (FTIR) analysis of various polymers.

It is observed that the same wave number and the infrared transmittance of each polymer is about 45–100%. From FTIR analysis of graphene dyeable polypropylene as shown in Figure 14, it is observed that there was a characteristic absorption peak of the –C=O functional group at wave number at 1717 cm⁻¹. The characteristic absorption peak of the ester functional group, at the wave number of 1272 cm⁻¹, represents the stretching of the CO bond in esters with the PBAT structure on the dyeable polypropylene.³² It is seen that the graphene dyeable polypropylene has polar groups and is capable of dispersibility. A dye produces hydrogen bonds to achieve its dyeing effect, and stretching improves the dyeability of the original polypropylene structure, which has only alkane groups and lacks polar groups. A characteristic absorption peak of the graphene structure-OH alcohol functional group was found at wave numbers 1103 cm⁻¹ and 1167 cm⁻¹, indicating that nanographene powder interacts with PPg-MA/dyeable-polypropylene during mixing processing, improving compatibility.

This study showed that the optimized nanographene modified dyeable polypropylene fiber developed from dyeable polypropylene to which nanographene powder has been added meets the FTTS-FA-010 far-infrared radiation and the AATCC 60-198IA washing fastness test standards.

Conclusions

In this study, a base polymer made up of dyeable polypropylene was melt-mixed with nanographene to produce nanographene modified dyeable polypropylene. The dyeable polypropylene was ground and melted with nanographene in a dispersant PPg-MA to improve the uniformity of the mixing of the nanographene and polypropylene. In the process of melt-spinning, nanographene content, mold temperature, gear pump speed, melt temperature, hot roller speed and take-up speed were used as experimental processing parameters. Physical properties such as yarn count, tensile strength, elongation at break, far-infrared emissivity and far-infrared temperature rise were taken as desirable qualities of the yarn. The Taguchi method was combined with GRA to obtain the best set of melt-spinning processing parameters that optimized the full set of quality characteristics overall. The best combination of factors was 2% nanographene powder content, mold temperature 240°C, gear pump speed 23 rpm, melt temperature 235°C, hot roller speed 2460 m/min, and coiling speed 2300 m/min. The physical properties of the optimized nanographene modified dyeable polypropylene yarn were as follows: yarn count was 75.3 d; tensile strength was 3.5 g/d, reaching the industry standard of 3.0 g/d; elongation at break was 41.0%, which is 1.2% higher than the 39.8% of our dyeable polypropylene yarn; infrared radiation emissivity was 82%, which is 5.0% higher than the 77% of the polypropylene yarn; the far-infrared temperature rise was 21.0°C, which is 16.4°C higher than the 4.6°C of the other yarn. This multifunctional nanographene modified dyeable polypropylene fabrics is suitable for winter clothing fabrics.

Footnotes

Declaration of conflicting interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This research was supported by the Ministry of Science and Technology of the Republic of China under grant number 111-2622-E-011-005.

ORCID iD

Chung-Feng Jeffrey Kuo

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