The Effect of Silicone Softener Particle Size on Color Assessment of Cotton Fabrics

Abstract

Softeners are widely applied textile treatments that affect many textile properties. Color change is an important side effect of textile treatment to consider. This paper evaluates the effect of commercially-available silicone softener particle size on color assessment of knitted cotton fabrics after treatment and repeated laundering using instrumental analyses. The results were also statistically evaluated, showing that silicone softener particle size had a significant effect on color difference after treatment; the larger the softener particle size, the greater the color difference and the darker the appearance. However, the contribution of particle size was insignificant after repeated laundering and ironing.

Introduction

Softeners are used in home laundering and in textile wet processing to improve fabric hand, drape, and various mechanical properties. Silicone softeners are known to render additional performance properties to cotton fabrics, such as improved wrinkle recovery and crease resistance.^1–3 Thus, the use of silicone softeners has grown rapidly in the textile industry.

When considering textile fabric treatment, it is important to understand the long-term effects of treatments and, in addition, any possible side effects. Fabric hand certainly plays an important role, along with appearance. Time and temperature dependent tendencies for changes in shade or color, as well as yellowing, are to be avoided.^4,5 Changes in color or shade of treated fabrics were attributed to finishing chemical structures, the deposition process around the fiber, the finishing agent acting as a solvent with the dyes, and the particle size.^6–8

In this study, the effect of silicone softener particle size on the color assessment of dyed cotton fabrics was examined. For this purpose, three commercially-available silicone softeners differing in particle size were used. Cotton fabrics were impregnated with these softeners in separate baths. Color differences were instrumentally measured and evaluated after application, repeated launderings, and ironing. The contribution of softener particle size was assessed using a completely randomized two-way analysis of variance (ANOVA). The results were evaluated at a 5% significance level.

Experimental

100% cotton single jersey fabric (25 x 10 course/wale, 110 g/ m²) was used in this study. The fabric was scoured, bleached, and dyed with reactive blue and red by the supplier (Fıstık Tekstil). The color coordinates of the fabrics according to D₆₅/10° illuminant are given in Table I.

Table I

Color Coordinates of the Fabrics

Fabric Color	L*	a*	b*	Brightness Indices (ISO R457)	Hue Angle (°)
Blue	56.11	-3.19	-34.05	49.99	94.05
Red	35.92	53.29	28.25	2.80	31.03

The commercially available silicone softeners, Silicones 1-3 (all were non-ionic, amino-functional polysiloxanes and had a density of 110 g/cm³ at 20°C with a pH value of 4- 4.5 at 10% concentration), were purchased from CHT Tekstil. The dyed fabrics were then treated with softeners in an uncontrolled laboratory environment by a laboratory type padder. Finishing conditions were as follows: 30 g/L softener, 1:20 bath ratio, 85% pickup, and drying at 120 °C for 5 min.

For color assessments, the color coordinates of samples were measured on a Hunterlab reflectance spectrophotometer (Colorquest II) under D₆₅/10° illuminant with D/0 instrument geometry. Four reflectance measurements were made on each sample, rotating the samples 90° before each measurement. Averages of the percent reflectance values at wavelengths between 400 and 700 nm were recorded. Color coordinates and differences according to the CIELAB (1976) equation were then obtained according to the method and terminology defined in CIE 15.2 as mentioned in AATCC Test Method (TM) 173, Calculation of Small Color Differences for Acceptability, and reported as ΔE. The control sample (dyed but not treated) was taken as the standard and the treated fabrics were taken as the trials when calculating color difference (ΔE) values. There were no significant color differences between control blue or red samples and samples to be treated prior to softener treatment. Assessments were conducted after softener treatment, after 10 home launderings (AATCC TM 8-2001), and after ironing done at 120 °C for 2 min.

Softener particle size measurements were performed with a Brookhaven Instruments 90Plus (Holtsville) using a dynamic light scattering technique based on passing a beam of light through a colloidal dispersion. When this happens, the particles or droplets scatter some of the light in all directions. If the particles are very small compared with the light's wavelength, the scattered light intensity is uniform in all directions (Rayleigh scattering); for larger particles (greater than 250 nm in diameter), the intensity is angle dependent (Mie scattering). If the light is coherent and monochromatic, as from a laser for example, it is possible to observe time-dependent fluctuations in the scattered intensity using a suitable detector, such as a photomultiplier capable of operating in photon counting mode. These fluctuations arise because the particles are small enough to undergo random thermal (Brownian) motion and the distance between them is therefore constantly varying. Constructive and destructive interference of light scattered by neighboring particles within the illuminated zone gives rise to the intensity fluctuation at the detector plane, which, as it arises from particle motion, contains information about this motion. Analysis of the intensity fluctuation time dependence can therefore yield the diffusion coefficient of the particles. By knowing the diffusion coefficient of the particles and the viscosity of the medium, the hydrodynamic radius or diameter of the particles can be calculated via the Stokes–Einstein equation. ⁹

To determine the silicone softener particle size, an emulsion was prepared by diluting the softener in distilled water in a ratio of 1:10. The polyacrylic measuring cell of the Brookhaven 90Plus was filled with ∼4 mL of the emulsion. The cell was located in the measuring cuvette of the instrument without shaking or mixing for good laser beam passage. The instrument applied a dynamic light scattering technique;^10,11 the duration of each measurement was ∼5 min and measurements were repeated three times for each emulsion. The wavelength of the laser light was 670 nm. Size distributions were determined at 90° of the laser beam scattering angle and measurements were carried out at 25 °C.

Costat was used to assess the contribution of softener particle size on the color assessment (ΔE value) of each fabric. Results were evaluated using a completely randomized two-way analysis of variance (ANOVA) and the Student-Newman-Keuls (SNK) range test. The ANOVA evaluation was based on defining the strength and importance of the contribution using F-ratio and probability of F-ratio values. The SNK test was applied to decide which ΔE values differed significantly from others. The results were marked in accordance with mean values. Any levels marked by same letter indicated that they were not significantly different.

Results and Discussions

Particle Size

Figs. 1a–c show the multimodal size distribution of softeners. Their effective diameter (D_eff) values (by intensity) and polydispersity values are given in Table II. The D_eff value is the diameter that a sphere would have to diffuse at the same rate as the particle being measured. It can also be called the “equivalent sphere diameter.” If the system is polydisperse, the D_eff value is an average diameter, and if weighted by intensity, it is an averaged intensity of scattered light by each particle. The most important pieces of information obtained in multimodal size distribution analysis are the positions of the peaks and ratio of their areas, but not their widths. ¹² Details about the instrument software, used for calculations of the diameter and multimodal size distribution from the scattered laser light, are available in the literature.^10,11 The effective diameter may result from one or more particle populations present in the emulsions.

Fig. 1

Multimodal size distribution of a) Silicone 1, b) Silicone 2, and c) Silicone 3 softener.

Table II

Effective Diameter and Polydispersity Values of the Softeners

Softener	Deff (μm)	Polydispersity
Silicone 1	14.52	0.235
Silicone 2	20.71	0.124
Silicone 3	25.05	0.079

From the multimodal size distributions, it appears that the investigated softener emulsions had nearly one population of particles resulting in a single and narrow peak. For Silicone 1, the main peak was present at around 14 μm with a high intensity (91.4%) and a second peak appeared around 301 μm with a relatively small intensity (8.6%). For Silicones 2 and 3, there were peaks around 20 and 25 μm, respectively. Both Silicone 2 and 3 peaks were at 100% intensity and had larger D_eff values than Silicone 1. For homogeneous emulsions, polydispersity values approach zero—the lower the polydispersity value, the greater the homogeneity of the emulsion's particle size. Results show that the softeners used in this study differed in particle size. Silicone 3 had the most homogeneous emulsion and the largest particles.

ΔE Values

Color change is defined as a change in color of any kind, whether in hue, chroma, or lightness. ¹³ Color and ΔE evaluation using CIELAB color space is based on the surface reflectance in the visible waveband (400-700 nm); any effects that change the reflectance will affect color differences. Table III shows the percent reflectance values of treated and control fabric samples at 400-700 nm wavelengths. Results show that fabric treatment changed the percent reflectance values of fabric when compared with the controls. Samples treated with smaller particle size softeners (Softeners 1 and 2) generally gave higher percent reflectance values for both colors.

Table III

Percent Reflectance Values of Treated and Control Dyed Samples

Blue Dyed	Percent Reflectance (%)
Wavelength (nm)	Silicone 1	Silicone 2	Silicone 3	Control a
400	38.42	35.30	36.35	37.23
420	44.79	40.61	41.67	44.43
440	53.34	48.55	49.44	53.74
460	52.34	47.73	48.47	52.76
480	44.60	40.78	41.44	44.56
500	36.14	33.04	33.57	36.19
520	28.89	26.49	26.96	29.08
540	23.25	21.42	21.85	23.46
560	18.88	17.41	17.80	19.25
580	15.76	14.61	14.99	16.17
600	14.32	13.33	13.72	14.62
620	13.69	12.81	13.16	13.96
640	13.40	12.59	13.03	13.55
660	16.66	15.69	16.41	16.45
680	26.31	24.97	25.94	25.63
700	43.10	41.13	42.22	42.29

Red Dyed	Percent Reflectance (%)
Wavelength (nm)	Silicone 1	Silicone 2	Silicone 3	Control a
400	5.57	5.25	5.17	5.35
420	3.39	3.15	3.08	3.28
440	3.06	2.84	2.79	2.99
460	2.82	2.63	2.59	2.78
480	2.48	2.31	2.27	2.47
500	2.20	2.06	2.04	2.22
520	2.02	1.90	1.89	2.06
540	2.14	2.02	2.00	2.19
560	2.32	2.20	2.21	2.41
580	5.25	5.00	5.01	5.41
600	18.86	18.22	17.92	18.96
620	41.70	40.66	39.69	41.13
640	61.25	60.08	58.60	59.71
660	71.21	70.00	68.37	68.62
680	75.14	73.87	72.33	71.93
700	80.76	79.59	78.13	77.66

a

Control sample was dyed, but not treated.

For a randomly rough surface on which the surface height distribution is defined by a Gaussian probability distribution, the reflectance of the rough surface can be expressed by Eq. 1. ¹⁴

$$R_r=R_s\exp [-{(4\pi \sigma \cos \hspace{0.17em}i/\lambda )}^2]$$

Eq. 1

R_s and R_r are specular reflectances of perfectly smooth and rough surfaces, respectively, σ is the standard deviation of the surface from its mean level as a function of surface roughness, i is the incident angle, and λ is the light wavelength. Eq. 1 shows that surface roughness decreases specular reflectance of light at a given wavelength, particularly at a high angle of incidence. Surface reflectance depends on roughness since surface roughening increases light scattering and decreases surface reflectance or vice versa. ¹⁵ When a similar relationship between reflectance values of control (as R_s) and treated samples (as R_r) was assumed in this study, the higher percent reflectance values of the samples treated with smaller particle size softeners can be explained by having a smoother reflecting surface. It was therefore concluded that the smaller softener particle size, providing a high surface area and uniform dispersion in the textile medium, gave a smoother reflecting surface.

The reflectance difference (ΔR) between control and treated samples was obtained by simply subtracting the percent reflectance of the control from the percent reflectance of the treated fabrics (Fig. 2). Samples treated with softeners generally gave decreased percent reflectance, indicating they had a lower refractive index than the cotton fabric itself. There was a minimum in the range of 400 to 450 nm for treated blue samples; however, it was 600 to 650 nm for the red samples. The pronounced decrease may be related to the height of surface roughness, which can scatter shorter wavelengths for blue and longer for red samples. Additionally, the softener particle size seemed to be effectively scattering light for both colors, since the silicone softeners with larger particle sizes caused higher decreases. From this point of view, the height profile distribution of the treated fabric surface may be correlated with the sample ΔR profiles (Fig. 2). The reflectance change of the treated fabric seemed to be related to the particle size and distribution of the softener used and, to some extent as assumed before, to the fabric color coordinates. ⁸

Fig. 2

Reflectance changes of treated a) blue and b) red fabrics.

ΔE values of treated samples after softener application, 10 launderings, and ironing are presented in Fig. 3. Use of softeners with smaller particle sizes gave smaller ΔE values. When this result was compared with the percent reflectance values, it was concluded that the smaller softener particle size gave minor changes in the surface fiber morphology and only a small effect on ΔE values. The softeners gave larger ΔE values for blue samples due to yellowing, which resulted in a large change in the b* coordinate value (yellow – blue axis) for blue samples. Repeated launderings and ironing changed the measured ΔE values, but it was difficult to correlate them with the softener type applied.

Fig. 3

Color differences of a) blue and b) red samples.

The ANOVA results (Table IV) reveal that both softener type and fabric color had strong contributions to and significant effects on ΔE values after treatment. This means that any change in the particle size of the softener would change the ΔE values of the fabric whether it was dyed with the blue or red dye. This situation is well-defined in the SNK rankings given in Table IV.

Table IV

ANOVA Table for Color Difference (ΔE)

Rank	F-ratio	Probability (F-ratio)
Main level
Softener type	248.381	0.000
Fabric Color	364.889	0.000
Interaction
Softener x Color	205.415	0.000

In the SNK rankings, it is observed that the contribution of the softener particle size was significant only after application; differences in softener particle size would change the ΔE values (Table V). Repeated laundering and ironing changed the measured ΔE values, but all treated fabrics in both colors gave insignificant ΔE values after those processes.

Table V

SNK Ranking of Color Difference (ΔE) at 5% Significance Level

After Application
Blue Samples			Red Samples
Rank	Softener	Mean Value (ΔE)	Rank	Softener	Mean Value (ΔE)
1	Silicone 3	1.55 a	1	Silicone 3	0.95 a
2	Silicone 2	1.13 b	2	Silicone 2	0.84 b
3	Silicone 1	0.51 c	3	Silicone 1	0.80 b

After 10 Launderings
Blue Samples			Red Samples
Rank	Softener	Mean Value (ΔE)	Rank	Softener	Mean Value (ΔE)
1	Silicone 3	2.02 a	1	Silicone 3	1.81 a
2	Silicone 2	1.99 a	2	Silicone 1	1.71 a
3	Silicone 1	1.82 a	3	Silicone 2	1.59 a

After Ironing
Blue Samples			Red Samples
Rank	Softener	Mean Value (ΔE)	Rank	Softener	Mean Value (ΔE)
1	Silicone 3	1.12 a	1	Silicone 3	0.86 a
2	Silicone 2	1.08 a	2	Silicone 2	0.74 a
3	Silicone 1	0.81 a	3	Silicone 1	0.71 a

Conclusion

This study investigated the effect of silicone softener particle size on the color assessment of cotton fabrics. Samples of two different colors and softeners with three different particle sizes were used. The study showed that softener particle size affected the color assessment of treated fabrics. Fabrics treated with the smaller particle size softeners gave higher percent reflectance values. The smaller softener particle size gave a high surface area and uniform dispersion after application on the textile medium to give a smoother reflecting surface. Also, smaller particle size softeners were found to give smaller ΔE values.

However, the effect of particle size was insignificant when the treated fabrics were subjected to repeated launderings and ironing.