Abstract The synthesis of a range of new linear substituted heptamethine dyes has been designed and described. The photophysical properties of all the dyes were investigated, with many exhibiting improved fluorescent quantum yields when compared with indocyanine green. Finally, growth inhibition studies were performed in the fission yeast Saccharomyces pombe, which suggests potential antifungals activity in the μM range.

Abstract Fabric defect detection is a crucial aspect of fabric production. At present, deep learning detection methods mostly rely on supervised learning. To tackle this issue, this study proposes an unsupervised fabric defect detection approach based‐on normalising flow. The method only needs to train the mapping of the feature probability distribution of defect‐free samples to a Gaussian distribution. In the inference process, the location of defects can be determined by testing the distance between the probability distribution of image features and the estimated distribution. To adapt to the complex background and various minor defects of the fabric, a feature pyramid structure is adopted. Moreover, considering the gradient vanishing and network degradation caused by deep layers during training, a residual structure is incorporated into the model. Experimental results demonstrate that the feature pyramid flow model outperforms other methods in defect detection across multiple datasets, with an average score rate of 98.7% and 100% for pixel‐level area under the curve (AUC) receiver operating characteristic and image‐level AUC, respectively, compared to an average score rate of 91.1% and 85.4% for other methods.

Abstract There is a lot of repetitive work involved in exploring the dyeing performance of natural dyes. To improve the experimental efficiency, save material, reduce time costs and shorten the research cycle, this study collects and analyses the literature data of 350 natural dye experiments to construct the Natural Dyes Dataset, and achieves rapid prediction of the optimal reaction conditions and dyeing effects of natural dyes using a lightweight integrated learning model. The size of the trained XGBoost model is only 562 KB; only the name of the dye and its approximate chemical composition need to be input to predict the results of the reaction environment pH, colour fastness to washing (CFW) and colour fastness to rubbing (CFR) of the natural dye on silk fabrics with the highest K/S in a very short time of 52 ms. The prediction accuracies for pH, CFW and CFR in the validation set are as high as 94.12%, 93.75% and 100%, respectively, and 77.78%, 91.67% and 83.33% for the real test set, with both validity and transferability. The integrated learning approach provides valuable guidance for exploring the dyeing performance of natural dyes with very small deployment costs and a very short inference time, expanding the possibilities of cross‐application of the disciplines of machine learning and textile dyeing.

Abstract The laundering process stands as the most commonly employed method for the maintenance of textile products. This investigation focused on boron, a pivotal mineral resource for our nation, and aims to elucidate alterations in fabric properties, specifically about colour changes and pilling, after the utilisation of various washing detergents. Comparative analyses and assessments were conducted across three distinct washing cycles, employing three distinct detergents: a boron‐infused washing detergent, a standard detergent conventionally utilised in textile trials, and a widely adopted commercial detergent. The investigation encompassed three diverse fabric constituents—cotton, polyester, and viscose—subjected to scrutiny in terms of fabric parameters subsequent to 1, 5, and 25 washing cycles. Following the laundering processes conducted at 40°C, evaluations were conducted concerning the pilling degree and colour alteration of the fabrics. Consumer perception regarding colour alterations, and subjective colour assessments was undertaken with a panel of 10 individuals. Furthermore, the research delved into the stain removal efficacy of the boron‐infused washing detergent. To assess stain removal, all fabrics were intentionally stained with six diverse contaminants: ketchup, mayonnaise, coffee, sweetened coffee, tea, and sweetened tea. A post‐washing evaluation of stain conditions was carried out subjectively by an assembled arbitration committee. Over the course of 25 washing cycles, it was observed that the boron‐containing detergent notably diminished colour strength, particularly evident in polyester and cotton fabrics. Pilling outcomes indicated a decrease in pilling as the number of washing cycles increased. Boron‐containing detergent, denoted as detergent B, consistently demonstrated commendable stain removal properties, particularly on cotton fabric.

Abstract Polyethylene terephthalate (PET) fibre, widely utilised across diverse industries, suffers from inherent drawbacks such as inadequate moisture absorption, limited hydrophilicity, and susceptibility to static electricity. To address these limitations, Porel fibre has emerged as a novel modified polyester fibre that incorporates flexible aliphatic polyester into its macromolecular chain. This innovative design not only overcomes the deficiencies of PET fibre but also enables it to be dyed using cationic dyes or disperse dyes under atmospheric pressure conditions. However, the investigation of the dyeing process of cationic dyes on Porel textiles remains relatively scarce. In this work, response surface methodology (RSM) was used to optimise the dyeing process of Porel fabrics with C.I. Basic Blue 162. The effects of dyeing temperature, dyeing time, and dye concentration on exhaustion percentage and colour strength (K/S value) were investigated using the Central Composite Design (CCD) method. The optimal dyeing process condition was as follows: a dyeing temperature of 96°C, a dyeing time of 51 min, a dye concentration of 3.6% owf, a dye solution pH value of 4–5, and a liquor ratio of 1:20. Under the optimal dyeing process conditions, the Porel fabrics achieved an exhaustion percentage of 96.2% and a K/S value of 36.0. The aforementioned efforts endow Porel fibre with high‐quality dyeing capabilities and broaden the application prospects of polyester textiles.

Abstract This paper introduces a novel plasticisation model of dye diffusion which recognises the fundamentally important mechanistic roles that macromolecule glass transition and water‐induced fibre/polymer plasticisation adopt in aqueous immersion dyeing processes. In essence, the model assumes that the temperature dependent diffusivity of all classes/types of dye molecule within the water‐saturated amorphous domains present in all types of common semi‐crystalline textile fibres/polymers, is governed by the thermally regulated relaxation times of the structural rearrangements occurring within the water‐saturated, water‐swollen, water‐plasticised fibre/polymer, as articulated in terms of the constructs of polymer chain segmental mobility and available free volume.

Abstract This review concerns the application of disperse dyes to poly(ethylene terephthalate) fibres using aqueous immersion dyeing processes and the roles of both elevated dyeing temperatures and carriers in the dyeing system. In this part of the paper, a novel plasticisation model of dye diffusion is proposed which reflects the crucially important mechanistic role that water‐induced fibre/polymer plasticisation adopts in the diffusion of disperse dyes within polyester and other types of polymeric material.

Abstract The prediction of colour formulation is an important step in reproducing the target colour. At present, there are relatively few researches on multi‐objective colour formulation problem, and the colour matching accuracy needs to be improved. In this research, a multi‐objective evolutionary meta‐heuristic method based on the Fast and Elitist Multi‐objective Genetic Algorithm (NSGA‐II) was proposed to predict the target colour recipes. The method used dye concentration as a variable and included three objective functions: (1) minimising the CMC (Colour Measurement Committee) colour difference between the formulation colour and the target colour, (2) minimising the metamerism index, and (3) minimising the cost of the formulation. The algorithm could obtain the Pareto optimal solution set after iteration. On this basis, the best combination of formulations was selected from the optimal solution set by combining the Expert Scoring Method (ESM), Entropy Weight Method (EWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The prediction effect of the model was evaluated by taking cotton fabrics and reactive dyes actually used in plant as examples. The results showed that 87.5% of the formulations met the CMC colour difference value of no more than 1, the metamerism index of 90.0% of the formulations did not exceed 1, and the cost of 92.5% of the formulations was reduced relative to the maximum extent in the Pareto optimal solution set. Further studies should be focused on removing duplicate individuals to give better diversity in the Pareto optimal solution set.