Coloration Technology最新文献

Traditional textile dyeing processes usually require large quantities of water and energy and generate wastewater that can be harmful to the environment. Dyeing in supercritical carbon dioxide (sc-CO₂) media is promising in textile coloration due especially to it providing a waterless process and eliminating the need for an energy intensive drying step. The natural anthraquinone emodin showed promising results for dyeing different fibres through sc-CO₂ process. However, emodin is mutagenic. The aim of this study was to develop non-mutagenic derivatives of emodin that can be applied to textiles using sc-CO₂. Emodin structure was modified incorporating acryloyl groups, which are considered suitable for decreasing potential for DNA intercalation, and thus mutagenicity. The presence of acryloyl groups would also enable atmospheric plasma induced bonding with fibres. Molecular modelling studies showed that emodin derivatives became less planar with increasing number of attached acryloyl groups, making intercalation unlikely. The derivatives produced were tested to assess mutagenicity in vitro (Salmonella/microsome assay, TA1537, 10% S9) and in vivo (micronucleus test in hemocytes of aquatic crustacean). We found that emodin can be derivatised using acryloyl chlorides to give mono- and di-acrylate esters suitable for dyeing polyester fibres in sc-CO₂. However, the new dyes presented mutagenicity for both in vitro and in vivo. Although the derivatives provided greenish-yellow alternatives to emodin for dyeing synthetic fibres, they do not appear to be viable alternatives from the point of view of preserving human and environmental health. Plasma bonding studies are underway.

In this research article, it is reported the preparation and characterisation of four types of blended cotton-polyester based textile materials in different dye ratios with four kinds of natural dyes of agro waste (walnut shell, onion peel) and wildflowers (tansy, Hypericum) with/without mordants as antibacterial and antifungal species for shoe materials. The dyed samples were spectrophotometrically measured and the results were obtained as colour strength (K/S) as well as CIELab values. As a result, dyed fabrics with natural dyes showed yellow, grey, orange and brown colours. With the help of a mordant, aluminium-potassium, the dyed fabrics obtained natural, not distorted shades of these colours, and with the addition of copper sulphate, they showed changing yellow to light green, orange to dark red, grey to a darker shade of grey and brown to a darker colour. The dyed samples were tested for colour fastness to circle rubbing and satisfactory results were found. The fabrics dyed only with natural dyes and treated with natural dye and mordants were evaluated for antibacterial and antifungal properties against Escherichia coli, Staphylococcus aureus, and Candida albicans. The results indicated that fabrics treated with these natural dyes and mordants had excellent antibacterial and antifungal activity. All natural dyes and mordants used in this study were the most effective and showed the maximum inhibition zone, indicating the best antimicrobial and antifungal activity against tested microbes. The results showed that all these natural dyes can provide some antibacterial and antifungal activity on mixed fabrics.

Electrochromic materials (ECMs) have important potential applications in the field of intelligent optoelectronic displays. However, ECMs and its devices cannot be truly applied on a large scale until the problem of their long-period performance degradation is really solved. This article focuses on summarising the attenuation mechanism of common ECMs, which is mainly divided into three types: structural damage, irreversible reaction and ion capture, and summarises four optimisation strategies for the cyclic stability of ECMs at the present stage: doping is used to change the internal structure of the material in order to achieve the purpose of improving the cyclic stability; composite means to allow a variety of materials to interact with each other in order that the original material has no excellent performance; improve the cyclic stability of ECMs by the special characteristics of nanostructures; and enhance the performance by optimising the preparation process and regulating the parameters in the preparation process. Finally, the article discusses the potential for optimising the long-term stability of ECMs in the future, based on the earlier-mentioned research.

Hypochlorite is a kind of endogenously produced reactive oxygen species (ROS) in the human immune system and plays a crucial role in many pathophysiological processes. In this work a novel fluorescent probe derived from 6-methylthio coumarin was designed and synthesised for detection of hypochlorite in acetonitrile/phosphate-buffered saline (MeCN/PBS) (1:1, v/v, pH 7.4) medium. The probe displayed high specificity toward hypochlorite over other ROS analytes, rapid response to hypochlorite within 30 s, and a broad applicable range of pH 3–8. The detection limit was calculated to be 1.48 μM. Application of this probe for in vitro Hecat cell imaging to detect hypochlorite ions was confirmed. A sensing mechanism based on thioether to sulfoxide oxidation by hypochlorite was proposed and demonstrated. The study presented an effective approach for construction of highly efficient hypochlorite chemosensors from hydrophilic methylthio-substituted coumarins considering their facile access.

Accurate whiteness measurement is critical across various industries including textiles, paper, and detergents. With the advent of fluorescent whitening agents (FWAs), the assessment of whiteness has evolved from simply measuring reflectance to determining the spectral radiance factor (SRF) of materials. While multispectral imaging (MSI) can effectively measure object reflectance and colour, its accuracy is compromised by FWAs that significantly augment spectral reflectance. Thus, reliable whiteness measurement requires the determination of both reflectance and SRF. This article presents the design of a specialised light source equipped with an ultraviolet (UV) filter to provide adjustable illumination within an integrating sphere system. By tuning the position of the UV filter, multispectral images are captured of a fabric under varying UV exposures. These images are subsequently processed and combined to reconstruct full-range visible spectrum information. This approach is shown to achieve high accuracy and spatial uniformity in quantifying the whiteness of textiles using MSI.

Coordination complexes were widely used in advanced oxidation processes. The ligands with various substituents could lead to differences in the catalytic properties and mechanisms. In this work, the iron(III)–N,N′-dipicolinamide (FeL) complexes (the iron(III) complexes with substituents –CH₃, –H, –Cl and –NO₂ named as FeL1, FeL2, FeL3 and FeL4, respectively) were used to activate hydrogen peroxide (H₂O₂) for degrading dyes wastewater. Mechanism studies indicated that the FeL4/H₂O₂ system contains the Fe^V=O in addition to the same ^•OH and O₂^•− as the other systems, which made it exhibit more excellent performance than others. The results of the performance tests showed that the FeL4/H₂O₂ system could remove 97%, 89%, 100%, 83%, 100%, and 99% of RR195, RY145, RB194, RB19, MB, and RhB, respectively, which proved the good application performance of the FeL4/H₂O₂ system. In addition, the performance of the FeL4/H₂O₂ system was not influenced by anions and natural organics. This study verified the feasibility of modulating the catalytic performance of the complexes by changing the substituents and provided an efficient catalytic system for dyeing wastewater treatment.

A full gamut colour solid model consisting of three lightness planes, 18 colour mixing units and 360 grid points is constructed from nine primary coloured fibres: red (R), yellow (Y), green (G), cyan (C), blue (B), magenta (M), dark grey (O¹), medium grey (O²) and light grey (O³). Subsequently, the 213 coloured yarns and fabrics containing different lightness, hue and saturation were prepared according to the mixing ratio parameters in the colour solid. The Stearns–Noechel colour prediction algorithm, which predicts reflectance using coloured fibre mixing ratios, was improved and applied according to the requirements of colour prediction; and the Stearns–Noechel proportion prediction algorithm, which predicts coloured fibre mixing ratios by reflectance, was refined and employed in accordance with the demands of proportion prediction. Then, the 12 additional coloured fabrics were fabricated and their corresponding measurement data were used on the algorithm for validating its forecasting capabilities. The final experimental results reveal that the maximum colour difference for colour prediction is 5.5, the minimum is 1.7, and the average is 3.7; the maximum colour difference for proportion prediction is 3.3, the minimum is 0.3, and the average is 1.6. Therefore, this approach is promising to improve the colour reproduction issues encountered in the processing of three-channel computer numerical control (CNC) spinning.

To address the issue of low precision in classifying the colour differences of yarn-dyed fabrics and the high cost of manual detection, a colour difference classification method relying on an improved seagull optimisation algorithm (SOA) optimised regularised random vector functional link (RRVFL) model is proposed for dyed fabrics. First, to address the issue of the slow convergence speed of the SOA, the current study optimises the initial SOA group with the marine predators algorithm (MPA) so that it can effectively improve the convergence ability and global optimisation ability of the SOA. Subsequently, the enhanced SOA is applied to fine-tune the parameters of the RRVFL. Compared with the methods that only optimise weights and bias, the proposed algorithm obtained by optimizing the initial group of SOA through the Marine Predators Algorithm (MSOA)-RRVFL model in this paper also increases the optimisation of the number of nodes in the hidden layer and regularisation parameters, which also effectively avoids the issue of the low classification accuracy of the RRVFL model due to random related parameters. Finally, by comparing the RRVFL model with other optimisation algorithms, the experimental outcomes demonstrate that the convergence ability of the improved SOA has been improved, and that the average accuracy of colour difference classification by the MSOA-RRVFL model is as high as 99.79%, and that the classification error fluctuation can be stabilised below 0.2%. In general, the MSOA-RRVFL model displays an excellent performance in terms of stability and significance.

According to the demand for colour prediction for coloured yarn, two adjacent colours chosen from red (R), yellow (Y), green (G), cyan (C), blue (B) and magenta (M) fibres were combined with fibres of dark grey (O¹), medium grey (O²) and light grey (O³), respectively, and then ternary coupling-superposition mixing was performed to acquire a colour solid consisting of three lightnesses, 18 colour mixing units and 18 × (m + 1) × n grid points. An integrated colour mixing with 20% hue gradient and 33.33% saturation gradient was performed to achieve a colour solid containing 360 grid points, then using it as the sample space for the colour prediction model. A total of 360 typical samples were established by the grid points, 213 yarns and fabrics were prepared by the typical sample parameters, and the corresponding reflectance was accessed by a spectrophotometer. Neural network models for predicting reflectance by mixing ratios as well as forecasting mixing ratios by reflectance, were established. The 12 non-grid point parameters were chosen to prepare corresponding yarns and fabrics, and the corresponding reflectance was measured. The predicted and measured values of the neural network model were compared to verify its predictive ability and generalisability. The results showed that when predicting the colour by the mixing ratios, the colour difference between the predicted and measured samples ranged from 1.5 to 3.4, with an average of 2.4; and when forecasting the mixing ratios by the colour, the colour difference ranged from 0.8 to 5.6, with an average of 2.4.

Aiming at the defects in the process of fabric production, a defect detection model of fabric based on a mixed-attention-based multi-scale non-skipping U-shaped deep convolutional autoencoder (MADCAE) was proposed. In a traditional encoder, the convolutional layer treats each pixel equally, so the importance of different pixels cannot be reflected. It is difficult to obtain richer and more effective information. The reconstruction of the defect region and the detection of the defect region are further affected. In this article, three different scale features of input images are extracted by enlarging the receptive field with large kernel convolution blocks. A hybrid attention module is used to ensure the richness of the extracted information in terms of space and channel. Experiments show that this method can effectively reconstruct fabric parts without requiring a large number of defect marking samples. It can quickly detect and locate defective areas of fabric patterns.