The international textile wet processing industry produces large amounts of wastewater, which if discharged into the environment could have adverse effects on aquatic life and drinking water. Efforts to reduce wastewater production include the development of chemical finishing technology that employs atmospheric plasma to apply repellent finishes to textile fibers. With this in mind, the use of atmospheric plasma technology to apply dyes to textile fibers was examined in the present study, as no water is needed for the dyeing process. Our work involved the design and synthesis of suitable dyes for waterless technology and examination of their utility for dyeing cotton, nylon, and polyester (PET). Results indicated that the obtained azo dyes having one or two acrylate groups gave good bonding to and good technical properties on cotton, nylon, or PET following spray application and plasma treatment. Dyes that worked best were also nonmutagenic in the Ames test.

The main objective of this work was to test the performance of major formulas for assessment of small suprathreshold color differences in the blue region. The models examined include CIELAB color space based equations, including CIELAB, CIE94, CIEDE2000, CMC (l:c), BFD (l:c), and formulas based on more uniform color spaces, such as DIN99d, CAM02-SCD, CAM02-UCS, OSA-GP, and OSA-Eu in comparison against data obtained via visual assessments. For this purpose, a dataset around the CIE high-chroma blue color center, hereafter called NCSU-B2, was developed. The NCSU-B2 dataset comprised 65 textile substrates and a standard, with a mean ΔE(ab)* color difference of 2.72, ranging from 0.54-5.72. Samples were visually assessed by 26 subjects against the reference gray scale in three separate trials with at least 24 h between assessments. A total of 5070 assessments were obtained. The standardized residual sum of squares (STRESS) index was used to examine the performance of various formulas for this dataset, as well as a previously developed NCSU-B1 low-chroma blue dataset [Color Res. Appl. 36, 27, 2011], and blue centers from other established visual datasets. Results show that formulas based on more recent uniform color spaces provide better agreement with perceptual data compared with models based on CIELAB space.

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