棉织物洗涤中染料转移抑制的影响因素研究

doi:10.3969/j.issn.1001-1803.2022.06.006

Abstract

Abstract:

Dye transfer in household washing has always been a problem that disturbs people. Herein, the influences of four factors including water hardness, water amount, washing temperature and Dye transfer inhibitor （DTI） on the transfer inhibition of Direct Blue 6 on cotton fabrics were studied. The response surface method （RSM） models of the 4 factors were built. All the RSM models show that water hardness is the most significant negative factor. Based on the DLVO theory, due to the presence of Ca²⁺ and Mg²⁺, the increase of water hardness can not only reduce the zeta potential of the fabric surface, but also cause the decrease of electrostatic repulsion between Direct Blue 6 and the fabric, and thus the dye won’t go off the fabric easily, that is, the dye is more likely to be adsorbed on the surface of the fabric. The increase of water amount can enhance the dye transfer inhibition effect. When the water amount increases, the initial mass concentration of dye is decreased, and the initial dynamic pressure of washing is increased, so that the dye flows through the fabric rapidly and the deposition on the fabric is reduced. The washing temperature also has a positive effect. When the washing temperature increases, the movement speed of dye molecules is increased, and the desorption rate is greater than the dyeing rate, so the dye molecules can shed from the fiber. Within the mass concentration range of 0.3 g/L DTI, only polyvinylpyrrolidone （PVP） and its cross-linked polymer have obvious inhibition effects on dye. Polymers have many hydrophilic groups and are easy to bind with direct dyes. However, pyridine-n-oxide （PNO） and lauryl betaine are small-molecule surfactants. Their structural differences make the RSM model show that polymers have better effects. The results of quantum chemical calculation also prove that the interactions between polymers and Direct Blue 6 are greater than the interactions between PNO and Direct Blue 6 or between lauryl betaine and Direct Blue 6. In addition, there is interplay between different washing factors. This work provides necessary basic information for the optimization of washing process for cotton fabrics and the modification of DTI.

Key words: dye transfer, water hardness, water consumption, temperature, mass concentration

CLC Number:

TQ423

Meng Mingzhu,Zhang Zhenzhen,Liang Shuaitong,Zhang Hongjuan,Wang Jiping. Study on the influencing factors of dye transfer inhibition in cotton fabric washing[J].China Surfactant Detergent & Cosmetics, 2022, 52(6): 613-619.

Figures/Tables 12

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References 20

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项	自由度	平方和	均方	F统计量
模型	5	3.36	0.67	42.13
误差	57	0.91	0.02	概率0.99
校正总和	62	4.27

项	估计值	标准误差	t统计量
水硬度	-1×10^-3	1×10^-4	-10.17
PVP质量浓度	0.94	0.14	6.96
用水量	7×10^-4	1×10^-4	5.01
PVP质量浓度* PVP质量浓度	-7.04	1.52	-4.63
水硬度*水硬度	4.57×10^-6	1.13×10^-6	4.05

项	自由度	平方和	均方	F统计量
模型	9	4.63	0.51	212.42
误差	53	0.13	0.002	概率0.99
校正总和	62	4.76

项	估计值	标准误差	t统计量	概率>t
水硬度	-2×10^-3	4×10^-5	-36.09	0.00
水硬度*水硬度	6.81×10^-6	4.4×10^-7	15.47	0.00
用水量	7×10^-4	5.33×10^-5	12.94	0.00
PVPP质量浓度	0.48	0.05	9.20	0.00
温度	2×10^-3	3×10^-4	8.53	0.00
温度*PVPP质量浓度	-0.01	2×10^-3	-6.49	0.00
水硬度*PVPP质量浓度	2×10^-3	3×10^-4	4.78	0.00
水硬度*用水量	8.98×10^-7	3.55×10^-7	2.53	0.00
PVPP质量浓度* PVPP质量浓度	-1.48	0.59	-2.49	0.02

项	自由度	平方和	均方	F统计量
模型	6	5.92	0.99	495.31
误差	56	0.11	0.002	概率0.99
校正总和	62	6.03

Study on the influencing factors of dye transfer inhibition in cotton fabric washing

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序号	水硬度/ppm	温度/ ℃	DTI质量浓度/（g/L）	用水量/mL	序号	水硬度/ppm	温度/ ℃	DTI质量浓度/（g/L）	用水量/mL	序号	水硬度/ppm	温度/ ℃	DTI质量浓度/（g/L）	用水量/mL
1	350	60	0.3	250	22	0	90	0.3	400	43	175	60	0	250
2	0	90	0.3	250	23	175	30	0.3	100	44	0	90	0	400
3	175	60	0.15	250	24	175	90	0.15	100	45	0	90	0.15	250
4	350	60	0.15	250	25	175	60	0	250	46	0	60	0.3	400
5	0	60	0.15	100	26	175	30	0.15	250	47	0	60	0.15	100
6	350	30	0.3	400	27	0	30	0	100	48	0	60	0	400
7	0	60	0.3	100	28	175	60	0	100	49	350	90	0.3	400
8	175	90	0.15	250	29	350	30	0	100	50	175	30	0.3	400
9	350	90	0	100	30	350	30	0.15	100	51	350	30	0.15	400
10	175	60	0.15	100	31	0	90	0	100	52	175	60	0.15	250
11	175	60	0	250	32	0	30	0.3	250	53	350	90	0	400
12	0	90	0.3	100	33	350	90	0.3	100	54	0	30	0	100
13	175	60	0.15	250	34	350	30	0	400	55	175	90	0.3	250
14	0	30	0	400	35	175	60	0.15	400	56	350	60	0.15	250
15	350	90	0.3	100	36	0	30	0.15	250	57	350	30	0.3	100
16	175	90	0.15	250	37	175	90	0.15	400	58	350	90	0	400
17	0	90	0	250	38	350	90	0	100	59	175	60	0.15	250
18	350	60	0.15	250	39	0	60	0.15	400	60	0	30	0.15	400
19	175	60	0.15	250	40	175	30	0	400	61	350	90	0.3	400
20	175	60	0.15	250	41	175	60	0.3	400	62	350	30	0	250
21	175	30	0.3	100	42	350	60	0.3	250	63	0	30	0.3	250

项	估计值	标准误差	t统计量	概率>t
水硬度	-2×10^-3	4.03×10^-5	-47.53	0.00
温度	4×10^-3	2×10^-4	17.44	0.00
水硬度*水硬度	6.74×10^-6	3.95×10^-7	17.06	0.00
用水量	6×10^-4	4.83×10^-5	11.48	0.00
水硬度*温度	4.18×10^-6	1.58×10^-6	2.64	0.00
用水量*用水量	-1.12×10^-6	5.31×10^-7	-2.11	0.04

项	自由度	平方和	均方	F统计量
模型	5	5.85	1.17	477.04
误差	57	0.14	0.002	概率0.99
校正总和	62	5.99

项	估计值	标准误差	t统计量	概率>t
水硬度	-2×10^-3	4.48×10^-5	-41.86	0.00
水硬度*水硬度	7.07×10^-6	4.23×10^-7	16.69	0.00
温度	4×10^-3	3×10^-4	15.36	0.00
用水量	6×10^-4	5.36×10^-5	11.56	0.00
水硬度*温度	4.25×10^-6	1.75×10^-6	2.43	0.02

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