Mahmuda Akter

Sustainable synthesis has emerged as the most effective method for producing metallic nanoparticles in this century. The current experimental studies present an eco‐friendly and cost‐effective fabrication technique for developing multifunctional textiles. This research study examines the use of Cassia angustifolia (Senna) leaves, which is employed as a natural reducing and stabilizing agent in the in situ biosynthesis of biogenic silver nanoparticles (AgNPs) from silver nitrate (AgNO ₃ ). This experiment uses 100% cotton and linen fabrics coated with biosynthesized AgNPs to examine antibacterial, UV‐shielding, and metal‐sensing performance using an assortment of characterization approaches. Characterization techniques, including FE‐SEM, EDX, and FTIR, were employed to confirm the presence of AgNPs on the textiles. The crystalline structure was evaluated by x‐ray diffraction. Also, TEM, HRTEM, and SAED investigations verified semi‐spherical, monodispersed AgNPs with an average size of ∼19 nm and a polycrystalline face‐centered cubic (FCC) structure. The antibacterial assay demonstrated that AgNPs‐coated linen fabric reduced Escherichia coli by 99.9% and S. aureus by 99.5%, while cotton fabric reduced them by 87.66% and 82.17%. UV–visible (UV–vis) experiments show that combinations of AgNPs and Senna‐derived polyphenols block UV light well. This work facilitates an eco‐friendly approach for creating multifunctional smart fabrics applicable for medical, protective, and environmental purposes.

This study analyzes two machine learning models, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), to predict thermal insulation of cotton fabric woven with twill. The input parameters include fabric thickness, ends per inch (EPI), and picks per inch (PPI). The ANN model has a 3-8-1 network structure, with output and hidden layers having sigmoid and linear activation functions. The ANFIS model employs sugeno-type fuzzy logic, while the network is trained using the feedforward backpropagation Levenberg-Marquardt technique. The weighted average approach was used in the defuzzification process. MATLAB was used to create both models. The ANN model performs well in predictions, as evidenced by its R2 value of 0.9942, which indicates a significant correlation between the target and prediction values. The ANN model's exceptional performance metrics, such as a mean absolute percentage error (MAPE) of 1.31401 and a root mean squared error (RMSE) of 0.00176, demonstrate its precision and reliability. However, the ANFIS model has considerably lower accuracy metrics, with an R2 value of 0.9570. The ANN offers more accuracy and precision than the ANFIS model, which has an RMSE of 0.00489 and a MAPE of 2.07495. This study will improve the textile engineering prediction model by revealing the intricate connection between fabric characteristics and the thermal insulation of clothing composed of cotton fabric's twill structure.

The study looks at two machine learning methods-multiple linear regression and artificial neural networks-to predict the thermal insulation value, known as CLO, of knit sportswear fabrics. The artificial neural network is created using feed-forward backpropagation and the trainlm training function in MATLAB. Its weights and basic values are adjusted using the Levenberg-Marquardt optimization method. The network uses the sigmoid transfer function to set the layer output and track its performance. When it comes to accuracy and how well it handles complex situations, the artificial neural network outperforms the multiple linear regression model. It better captures how thermal insulation values relate to fabric qualities. For the artificial neural network model, the mean absolute error percentage was 2.2771, the root mean squared error was 0.0017, and the coefficient of determination was 0.9268. The multiple linear regression model had a coefficient of determination of 0.8046 and mean absolute error percentages of 4.4170 and 0.0027. Compared to a time-consuming trial-and-error approach, the study shows that artificial neural networks are a better way to predict thermal insulation in textiles. This emphasizes how important these models are for creating energy-efficient designs and improving material engineering.

The textile industry is facing a growing issue of greenwashing and bluewashing, which are misleading environmental and social claims made by companies to appeal to customers' environmental and social consciousness. This review examines the causes and consequences of these practices, focusing on the holistic impact on consumers , corporations, stakeholders, and society. The analysis was conducted using various sources, including review papers, case studies, book chapters, and websites, to identify relevant publications. The study explores the concept of greenwashing and bluewashing in fast fashion, their impact on the environment and society. It suggests promoting transparency, accountability, and independent certification in the textile industry to combat these issues. The lack of research on these practices in the industry makes it difficult to understand their effects. The study emphasizes the need for optimal measures to control these practices, aiming to create a sustainable and responsible fashion sector.