Temperature dependent equilibrium dye adsorption data reported for Chlorazol Fast Scarlet 8B, Durazol Red 2B and Chrysophenine G on cotton, CV and CUP fibres and Durazol Grey RG on cotton and CV fibres in the presence of a constant amount of added inorganic electrolyte between 60°C and 90°C, as well as Chrysophenine G on cellophane sheet in the presence of varying amounts of added inorganic electrolyte, between 60°C and 97.5°C, was re-evaluated using the Williams-Landel-Ferry equation. The results obtained suggest that the plasticisation model of dye diffusion appears to offer a reasonable explanation of the temperature dependent equilibrium adsorption of direct dyes on cellulosic fibres/polymers.
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.
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.