Fastness and PF/3 Evaluations of Reactive Dyestuffs

Full Length Research Paper

Fastness and PF/3 evaluations of reactive dyestuffs

Yüksel Ikiz* and Reyhan Keskin

Department of Textile Engineering, Faculty of Engineering, Pamukkale University, Turkey.

Accepted 11 February, 2011

Fastness measurement by eyes is subjective, since it is dependent on evaluation of the observer. Sometimes problems occur because the producer and customer give different values to the same fastness test. To eliminate subjective perception of evaluating colour fastness by eyes, instrumental colour fastness measurement methods have been developed and it has been presented to the service of textile sector. The aim of this study is to evaluate acid and basic colour fastness to perspiration and colour fastness to washing of reactive dyed cotton products both with the eye and spectrophotometer. To make comparison between visual and instrumental results, developed PF/3 factor has been used. The highest PF/3 value was 12.35. This shows that the instrumental and visual evaluations of colour fastness tests exhibit 87.65% agreement even in the worst case.

Key words: Basic weaves, colour fastness, PF/3 value, reactive dyestuffs.

INTRODUCTION

Reactive dyestuffs short-run, high fashion nature of the textile industry, which also requires that coloration should be delayed to The chemical reaction between reactive dyestuffs and a be piece goods or even garment stage. cellulosic fibre takes place in the presence of a base and Dye is lost to dye-house effluent for a number of rea- reactive dyestuffs are covalently bonded to cellulosic fibre sons, but reactive dye hydrolysis during the application is according to the following two sided reaction: the most important one. An analysis of this situation was carried out and published in Table 1 comparing dye fiber Cel − OH + Cl − dye _ mol ⇔ Cel − O − dye _ mol + salt (1) covalent bonding efficiencies versus type of reactive group; “fixation” or covalent-bonding efficiency by X-ray The covalent bond thus formed provides very good fluorescence of bound sulfur associated with the fastness properties and is much stronger than the weak sulfonated chromophore. Table 1 indicates that in hydrogen bonds of direct dyestuffs with cellulose fibres. medium shades up to 30% of the dye applied ends up in Reactive dyestuffs react in a similar way with amino the effluent, whereas in full depths up to 50% of the dye groups. The reaction with amino groups also provides may be washed away. Given that reactive dyes are highly their use on dyeing protein fibres and polyamide fibres water soluble, it is difficult to remove from dye-house (Tarakcioglu, 1979; Rivlin, 1992). Reactive dyes are effluent at cellulose fiber dyeing using reactive dyes applied to cellulosic fibers by a variety of processes (Lewis, 2008). It is well known that increasing the number including exhaust or batch dyeing method, pad-batch, of reactive groups attached to the dye molecule pad-steam, pad-bake, print-steam, and print-bake. increases fixation yields and in consequence, there are Currently, padding and printing processes account for many bi-functional dyes on the market. If tri-functional about 30% of the market, the residual, most popular dyes could be economically manufactured then it might application method being “exhaust” dyeing. The reason be expected that improved cotton reactive dyeing for the current popularity of the latter method lies in the efficiencies would result (Lewis, 2008). As early as 1975 Hoechst launched the tri-functional dye, Remazol Red SBB which contained a monochloro-s- triazine group plus two divinylsulfone groups linked *Corresponding author. E-mail: [email protected]. through an aliphatic amine to the triazine residue; Ikiz and Keskin 1499

Table 1. The fixation yield of various reactive red dyes numeric list and the colour communication is provided (Douthwaite et al., 1996). easily by means of a data system through using this list (Williams, 2006). Dye- reactive group 3% o.m.f. 6% o.m.f. MCT/VS 76 68 MFT 64 56 Observer and CIE standard observer functions MFT/VS 61 50 DFMCP 74 67 In CIE system, some changes were made to more VS 68 58 accurately define the viewpoint and other view conditions MCT/MCT 57 49 for a standard observer. One of the aims happened to add L*, a* and b* values to the system. A more uniform MFT, monofluoro-s-triazine, MCT, monochloro-s-triazine, VS, vinyl sulfone, and DFMCP, difluoro-monochloro-pyrimidine. colour space was got with the changes made and it was called VIELAB. There are many three dimensional colour spaces or colour grading which were formed to classify colours. Among these, Munsell, Ostwald and CIELAB Hoechst also patented other dyes of this type (Hoechst, systems are most widely used colour grading systems. 1993). Ostwald system is mostly used in the production of painting items, colorants, dyestuffs and dyes. It emphasizes the states of depth and brightness. Munsell Colour and colour measurement system was developed in 1905 by the artist A. H. Munsell. In Munsell colour system, sensed colour of To sense the colour, light source, object and the observer objects is stated with three terms as the colour hue, value are necessary. To develop a device to digitize the colour and chroma. Hue is the quality of the colours like yellow, sense, it is necessary to standardize these three factors: blue and green. The value indicates the characteristic of the colour stated as light and dark. Chroma is the difference between a colour and a grey colour having the Source of light and standard light same value. CIE tristimulus system was formed in 1931. A human, Relative energy of light in each wavelength forms a by using CIE chromaticity diagram, can say if the two power distribution curve measuring spectral properties of colours fit to each other. He can state their certain places light source. For the device measurement of the colour, in CIE diagram but can not state the colour difference standard light types to show the same characteristics with between them. 3 dimensional colour systems were light sources have been developed. Some widespread developed for this (Vigo, 1994). standard light sources are those:

D65 (day light): is mean day light having 6500K colour Colour difference temperature. A (white-hot light): is yellow electrical lamp light having It is expected from producers of coloured products to 2856K colour temperature. make production in adequate colour quality for satisfying F2: is fluorescent lamp light having 4230K colour their customers. That to realize the customer needs is not temperature. easy shows that acceptable colour process management F11: is fluorescent light having 4000K colour temperature. is difficult as well. In taking acceptability decisions it is TL 84 : Marks and Spencer light is in this group (Duran, benefited from various colour difference formulas. To 2001). solve the problem of evaluating different colours, the colour difference assessment is necessary. To be able to form a colour system in that the difference between two Object and reflectance/transmission curve colours can be measured finely, lots of studies have been made but it has not yet been arrived to a system in that Colorants such as pigments or dyestuffs taking place in the colour differences can get precisely and finely. With the object reflect some wavelengths of light coming onto the same aim, mathematical colour difference formulas themselves permeate some of them and select and have been developed but it has not been arrived to any absorb some wavelengths. In each wavelength, the solution here again (Duran, 2001). amount of reflecting and permeating light can be According to experts of the subject, these three measured. This forms spectral curve of colour dimensional colour space systems, the colour difference characteristic of the object. Relative reflecting (R%) or equations and differences between visual and device relative permeability (T%) curves are just like the observations show there is not any perfect colour system fingerprint of the colour. Therefore spectral reflectance which can measure the colour differences. It is indicated data in each wavelength of the colour are given in a that this is probably never ever possible due to 1500 Sci. Res. Essays

complicated interaction of factors determining visual or follows: device colour senses and colour measurement (Vigo, 2 1994). N   loge (γ) = 1  ∆E   ∆E  (3) ∑ log  i  − log  i  N e  ∆V  e  ∆V  i=1   i   i  Evaluation of colour running through colour measurement Finally, V AB variance between two data sets is calculated as follows: Fastness tests committee (FTC) being connected to the unity carries on the studies in the field of fastness tests. 2/1  N ∆ − ∆ 2  In late 1970’s in FTC a sub-committee was founded to  1 ()Ei F Vi  VAB = ∑ .……. (4) establish methods evaluated with the device instead of    N i=1 ∆Ei × F × ∆Vi  evaluating fastnesses with visual methods. The sub- committee started its studies with the contamination tests 2/1 in that a white sample being simpler was compared with N  ∆Ei  the contaminated sample. In England in 9 different  ∑   i=1 ∆V  laboratories composing of 27 to 40 estimators, 350 test F = i couples were assessed. E values of the test  N ∆V  CIELAB  ∑ i  samples in each laboratory were measured in 8 different ∆E spectrophotometers and 3 different colorimeters. In the  i=1 i  results of visual evaluations, while 16% of them showed 1 or 2 grey scale value difference, the device results PF/3 = 100 ( γ – 1 + VAB + CV /100) /3 (5) generally showed much accordance to each other. After different suggestions of various countries, in the meeting PF/3 factor is only used to state higher or lower variance made in West Germany in 1987 the formula being the between two data sets. suggestion of German committee was accepted as international standard (Sato et al., 1997; Sato et al., The researchers were not able to make any statistical 1997). calculation for PF/3 differences (Mangine, 2005). Duran To compare the device and visual results data, Luo and et al. (2007) in the studies they made, dyed 5 different Rigg developed PF/3 factor in 1987. PF/3 factor is cotton fabrics the weft and warp densities and porosities defined as a measure which is used in the colour of which were different, with reactive dyestuffs and then research data and includes one value. Low PF/3 value measured the fabric samples with spectrophotometer. shows that there is better accordance between the device According to the results they got, they detected that the measurement and visual results. In the calculation of change in the fabric density and porosity did not make PF/3 value it is benefited from three statistical measures much effect on the nuance of the color got but changed as CV , γ and VAB . PF/3 value is found like in Equation 2 efficiency (darkness) of the color (Duran et al., 2007). by using coefficient of variation (CV), gamma factor ( γ) In a study made by Balci and Ogulata, CIELab values and variance (V AB) values) (Mangine, 2005). CV is of dyed textile materials were measured and the colour coefficient of variation and indicates the deviation from difference values after dyeing and finishing were linearity between two data sets: calculated with the formula CIELab 1976. Later, suitable models to estimate CIELab values of samples finished N 1 2 with YSA technique and their color change (output) after ∑ (∆Ei − f∆Vi ) finishing were established. It was determined that N 2 CV = i=1 (2) correlation coefficients and R values of established nets ∆E were quite good and it was assessed that estimated values showed deviation within acceptable limits N according to real values (Table 5). ∑ ∆Ei × ∆Vi This result showed that established YSA models could i=1 1 be used in the estimation of the color changes which f = N , ∆Ei = ∑ ∆Ei 2 N could occur in dyed fabric after the finishing operations ∑()∆Vi (Balci and Ogulata, 2009). i=1

E and V are any two data sets. MATERIALS AND METHODS

Materials Used second statistical measure is gamma ( γ) factor. It measures proportional relation between two data sets In this study, three different 100 % cotton, bleached and and prevents E and V units from affecting the result as mercerized, fabrics were used; plain, 3/1 twill and 5/1 satin weaves. Ikiz and Keskin 1501

Plain1/1 180 Twill 3/1 200 Satin 5/1 240 g/m 2 g/m 2 g/m 2

Remazol Remazol Remazol 1/2 1/2 1/2 1/3 Remazol red GWF blue RR yellow Remazol Remazol Remazol red GWF + (Red) (Blue) 4GL red GWF red GWF blue RR 1/3 Remazol (Yellow) + 1/2 + 1/2 + ½ blue RR + Remazol Remazol Remazol 1/3 Remazol blue RR yellow yellow yellow 4GL (Purple) 4GL 4GL (Black) (Orange) (Green)

Figure 1. Scheme of dyeing.

60ºC 60ºC 30 min 20 min 60ºC 30 min

25ºC 40 25º C min 20 min 2nd batch soda addition

Addition of salt, fabric and dye at 1st batch soda 25ºC addition

Figure 2. Applied exhaust or batch dyeing method.

In these fabrics, the surfaces on which the warp is loose were taken dyeing method used was shown in Figure 2. After dyeing, the as the right side and the warp direction was held the same in all samples were rinsed and washed with soft water by using Gemsan fabrics. Fabrics were dyed with reactive Remazol 100% concen- washing substance as described follows: tration powder state dyes in total 12 different concentrations as 0.1, 0.3, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5 and 5% with dyestuffs of Red Rinsing twice with cold water, adjusting to pH 5 to 6 with acetic GWF, Blue RR, Yellow 4GL and also double and triple combina- acid, washing for 20 min in hot boiling water with ECE detergent, tions of these dyestuffs. The scheme was shown in Figure 1. All rinsing once with warm water and rinsing three times with cold dyeings were carried out in 1:10 liquor ratios at 60°C, according to water or until seeing no colorant in rinsing water. Acid and basic exhaust or batch dyeing method. Sodium sulphate, calcium color fastness to perspiration and color fastness to washing tests carbonate and moistener were used as auxiliary chemicals. The were applied to exhaust dyed fabric samples and fastness values 1502 Sci. Res. Essays

Table 2. Variables entered/removed.

Model Variables entered Variables removed Method 1 EYE , Enter

a All requested variables entered and b Dependent Variable: MACHINE.

Table 3. Model summary.

Change statistics Model R R square Adjusted R square Std. error of the estimate Durbin-Watson R square change F change df 1 df 2 Sig. F change 1 0.904 0.817 0.817 0.26138 0.817 20238.715 1 4534 0.000 0.982 a) Predictors: (Constant), EYE and b) Dependent Variable: MACHINE.

Table 4. Anova.

Model Sum of squares df Mean square F Sig. Regression 1382.694 1 1382.694 20238.715 0.000 1 Residual 309.760 4534 0.068 Total 1692.454 4535

a Predictors: (Constant), EYE, b Dependent Variable: MACHINE.

Table 5. Coefficients.

Unstandardized Standardized 95% confidence Interval for B Model coefficients Std. error coefficients t Sig. B Beta Lower bound Upper bound 1 (Constant) 0.152 0.031 4.981 0.000 0.092 0.212 EYE 0.954 0.007 0.904 142.263 0.000 0.941 0.967

a) Dependent Variable: MACHINE.

of test samples were evaluated both with the eye and eyes were evaluated using SPSS 11.0 statistical data spectrophotometer. In fastness evaluations made with the eye, the analysis program. Simple linear regression model applied to following standards were used: compare the results of the spectrophotometer and eye. While spectrophotometer results (MACHINE) were i) Color fastness to washing: EN ISO 105-C06. ii) Color fastness to perspiration: EN-ISO 105-D01 (14). considered dependent variable, eye results (EYE) were considered independent variable. Tables 2 and 3 were gotten from the program. R is the correlation coefficient of Devices and machines the model. A simple linear regression model is closely related to the calculation of correlation coefficient to Fabrics were dyed by Automat sample dyeing machine and dried in measure the degree of association between two a sterilizer at 120°C. Washing fastness tests in Automat device, perspiration fastness tests in M231 perspirometer and rubbing variables. A positive sample correlation coefficient fastness tests in AATCC automatic crockmeter were performed. indicates that there is a tendency for the other variable to Visual evaluations were made in Verivide light box, under D 65 light, increase as well. The closer the sample correlation on 45° angle non-optical white ground, by using colour change and coefficient is to 1, the stronger is the linear association. colour staining grey scales according to ISO standards by two Calculated R is 0.904 and adjusted R square is 0.817 experts. Instrumental measurements of fastness were made with that, there is a strong linear association between Datacolor 6500 spectrophotometer according to ISO standards. spectrophotometer and eye test results. ANOVA analysis (Table 4) is used to understand RESULTS AND DISCUSSION whether the regression model is significant by evaluating the “F” value. Since the “F” value is 20.238, 715 and Sig. Fastness values with the spectrophotometer and the is 0.000, the regression model is significant to evaluate Ikiz and Keskin 1503

4.74,7 4.74,7

4.64,6

4.5 4,5

4.54,5 ye 4.44,4 e

ean M ean device

M 4.4

4.3 Mean Eye 4,4

Mean Device 4,3 Acid persp. fastness Washing fastness Acid persp. fastness Washing fastness Basic persp fastness Basic persp fastness

Fastness Fastness

Figure 3. Mean fastness values of spectrophotometer and eye, pertaining to acid perspiration, basic perspiration and washing.

5 wool 4 acrylic polyester 3 polyamide 2 cotton

Fastness values Fastness 1 diacetate

0 red blue yellow orange purple green black

Colours

Figure 4. Spectrophotometer acid perspiration fastness values.

two test method results. Regression equation is given as value for both measurement methods. follows:

Machine = 0.152 + 0.954 Eye (6) Comparison of PF/3 values

PF/3 factors for the spectrophotometer and eye were Comparison of fastness values given in Figures 5, 7 and 9. The highest number which is disagreement between the spectrophotometer and visual Figure 3 shows the mean test results of acid and basic evaluation is seen as 12.35 for yellow colour, twill weave colour fastness to perspiration and colour fastness to and cotton fibre. This also shows that the instrumental washing of exhaust dyed samples of all colours, both with and visual evaluations of colour fastness tests exhibit the spectrophotometer and eye. Eye measures the test 87.65% agreement even in the worst case. When acid results insignificantly higher than the spectrophotometer. perspiration fastness graphic is examined as in Figure 4, It is seen that washing fastness has the highest mean it is seen that polyamide have the lowest values. As value and acid perspiration fastness has the lowest mean expected cotton have also low fastness values because 1504 Sci. Res. Essays

14 12 wool 10 acrylic 8 polyester 6 polyamide 4

PF/3 Values cotton 2 diacetate 0

d w k e o ge r blue ll n ra green blac ye o purple Colours

Figure 5. Calculated acid perspiration fastness PF/3 values.

5 wool

4 acrylic polyester 3 polyamide 2 cotton

Fastness values Fastness 1 diacetate

0 red blue yellow orange purple green black

Colours

Figure 6. Spectrophotometer basic perspiration fastness values.

of the affinity to reactive dyestuffs. Since there is no When figures of washing fastness are examined, it can affinity to reactive dyestuffs, polyester have the highest be seen that fastness values show similar tendency with fastness values. While yellow and green colours show the acid and basic perspiration values (Figure 8). Cotton the worst fastness values, red and purple colours show and polyamide fibers have affinity to reactive dyestuffs, the best. When PF/3 values are examined for acid so they have lower fastness values than the others. In perspiration fastness, hydrophilic fibres such as polyester terms of color, it is seen that the highest fastness drop and acrylic have quite consistent results. Depending on occurs in orange and green colors but the lowest drop in the fibre type, PF/3 values of colours change strongly. black color. It can be seen from Figure 9 that the best For example yellow colour has highest PF/3 values for agreement has been provided with washing fastness cotton fibre, while black colour has highest for polyamide. PF/3 values. The highest deviation has occurred with When basic perspiration fastness is examined, it is 10.86 factors for cotton fabric, satin weave and green seen that cotton has the lowest values (Figure 6). colour. Opposite of acid perspiration fastness results, polyamide comes next. As earlier stated while yellow and green colours show the worst fastness values, red and black Conclusions colours show the best. It is difficult to comment on basic perspiration fastness PF/3 values of the color weave and In this study it was seen that light shades have lower fiber type, because of very high variations. However the deviations than medium shades. The highest PF/3 factor most variation is seen in yellow color, satin weave and was 12.35 within all data sets and this means that cotton fiber, running with 11.48 PF/3 factor. consistency changes between 87.65 and 100%. Ikiz and Keskin 1505

10 wool 8 acrylic polyester 6 polyamide 4

PF/3 Values cotton 2 diacetate

0 red blue yellow orange purple green black

coloursColours

Figure 7. Calculated basic perspiration fastness PF/3 values.

5 wool 4 acrylic polyester 3 polyamide 2

PF/3 Values cotton 1 diacetate

0 red blue yellow orange purple green black

Colourscolours

Figure 8. Spectrophotometer washing fastness values.

10 wool 8 acrylic polyester 6 polyamide 4

PF/3 Values cotton 2 diacetate

0 red blue yellow orange purple green black

ColoursColours

Figure 9. Calculated washing fastness PF/3 values.

Because of the affinity to the reactive dyestuffs, cotton higher fastness values than the others. These data verify and polyamide fibres have lower fastness values. On the the affinity of fibres having cellulosic character towards other hand, hydrophilic fibres polyester and acrylic have reactive dyes. It can be considered that yellow and green 1506 Sci. Res. Essays

Colours have the lowest fastness values but the highest Hoechst AG (1993). European patent 624630. PF/3 values. According to the data, acid fastness values Lewis DM (2008). Color and Textile Chemistry. AATCC 2008 Olney Award Winner. of double and triple mixed dye coloured fabrics are lower Mangine HN (2005). Variability in Experimental Color Matching than that of single dye coloured fabrics. Conditions Effects of Observers, Daylight Simulators, and Color Weave type has no significant effect on the fastness Inconstancy. PhD Thesis, The Ohio State University, Ohio, 140s. values. Rivlin J (1992). The Dyeing of Textile Fibers: Theory and Practice. J. Rivlin Associates, Philadelphia, 127: 137-140. Sato T, Takada N, Ueda M, Nakamura T, Luo MR (1997). Comparison of Instrumental Methods for Assessing Color Fastness. Part 1 - REFERENCES Change in Color J. Soc. Dyers Colorists, 113(1): 17-34. Sato T, Ueda M, Nakamura T, Luo MR (1997). Instrumental Methods for Balci O, Oğulata RT (2009). Color Change As the Result of The Assessing Color Fastness. Part 2- Staining J. Society Dyers Chemical Finishing Applications in Dyed Fabrics and Prediction of Colorists, 113: 356-361. CIELab Values Through Artificial Neural Networks, Textile Apparel., Tarakcioglu I (1979). Textile Dyeing 1st Duplication, A.U., Engineering 1: 61-69. Faculty Publications, p. 271. Duran K (2001). Color Measurement Color Recipe Preparation. A.U. Vigo TL (1994). Textile Processing and Properties. Elsevier Science Textile and Apparel Res. Appl. Center, pp. 28-29, 278-280. B.V., The Netherlands, pp. 325-336. Duran K, Cay A, Atav R (2007). Wept-Wrap Frequency of Cotton Williams S (2006). Practical Color Management. Optics & Laser Fabrics and The Relationship Between Their Porosity and Color Technol., 38: 399-404. Effects Obtained in Reactive Dyeing, Textile Apparel, 1: 52-58. Douthwaite FJ, Harada N, Washimi T (1996). Proceedings of AATCC Conference. (Vienna), pp. 447-451.