The dyeing mechanism of bio-based polyamide 11 fibers with acid dyes was explored in order to provide a theoretical foundation for the dyeing properties of polyamide 11 fibers. The surface morphology and diameters of polyamide 11 fibers and polyamide 6 fibers were compared. Both polyamide 11 fibers and polyamide 6 fibers were dyed with acid dye Isolan blue NHF-S, and the effects of the initial dye concentration, pH values, dyeing temperature and time on the dyeing properties were investigated. The effects of the dyeing kinetics and thermodynamic model of Isolan blue NHF-S on polyamide 11 fibers were analyzed, and the corresponding parameters were calculated. It was found that the dyeing properties of polyamide 11 fibers were not comparable to those of polyamide 6 fibers, while the washing and rubbing fastness were more or less the same. The dyeing mechanism of Isolan blue NHF-S on polyamide 11 was a combination of localized and non-localized adsorption, which conformed to the pseudo-second-order kinetic model and the Redlich–Peterson thermodynamic model. Increasing dyeing temperature could shorten the half-dyeing time and improve the average diffusion coefficient and affinity between the dye and polyamide 11 fibers.
Microscopic hyperspectral imaging technology is a potential non‐destructive and non‐contact method for colour measurement of micrometre‐sized textile fibres. However, specularity on the fibre surface can distort the accurate colour information and affect the accuracy of the colour measurement. This paper proposed a specular‐constrained sparse approximation (SCSA) for specular‐diffuse reflection separation from hyperspectral images of wool fibres. First, a specular prior map is generated based on the lightness dissimilarity. Then the SCSA model is used to decompose the processed hyperspectral image A into low‐rank data L, sparse specularity data S constrained by the specular prior map, sparse noise E, and Gaussian noise N. A non‐linear logistic sigmoid function and a sparse approximation of A – L – N to S are used to improve the performance of specularity removal during iterative optimization. The experimental results show that the proposed method significantly preserves diffuse reflectance and texture details in the specular highlight regions to obtain actual spectral reflectance and chromatic values from hyperspectral images of wool fibres.
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