Transforming waste cellulosic fabric dyed with Reactive Yellow C4GL into value textile by a microwave assisted energy efficient system of color stripping.

A million ton of cotton fabric is wasted during cutting process in garment industry as well as in textile dyeing industry due to faulty dyeing. Color stripping of cotton fabric has become a significant challenge in the textile industry because the harsh chemicals used in chemical stripping processes affects the quality of fabric very badly. Conventional stripping methods lead with severe effects due to prolonged treatment time and high chemical concentrations. Recently, microwave-assisted stripping techniques have been emerged as effective alternatives to improve stripping efficiency. In this research, the developed microwave assisted stripping system is improved by the application of Urea, which is utilized as a microwave absorber to further reduce stripping time, temperature, and chemical concentration kept focus on quality parameters of recycled cotton fabric. This study inspects the efficiency of microwave absorber-assisted alkali hydrolysis and reduction in terms of dye-fabric bond cleavage, chromophores removal, chemical consumption, and processing time and compared with sequential stripping, microwave assisted stripping without absorber and conventional methods. The results indicated that microwave absorber-assisted alkali hydrolysis and reduction achieved 90 % stripping efficiency by using lowest concentrations of chemicals, while sequential stripping yielded a stripping efficiency of 96 %. Similarly, microwave absorber assisted methods resulted in minor loss in tear strength and weight. These outputs highlight the superior performance of microwave absorber-assisted techniques, demonstrating their efficiency, novelty, time-saving nature, and reduced damage compared to other methods.

A unique physics-inspired deep-learning-based platform introducing a generalized tool for rapid optical-response prediction and parametric-optimization for all-dielectric metasurfaces.

Metasurfaces are composed of a two-dimensional array of carefully engineered subwavelength structures. They provide a novel compact alternative to conventional voluminous optical components. However, their design involves a time-consuming hit and trial procedure, requiring many iterative electromagnetic simulations through expensive commercial solvers. To overcome this non-practical design strategy, recently, various deep-learning-based fast and low computational cost networks have been proposed to design and optimize individual meta-atoms and complete metasurfaces. Most of them focus on optimizing the amplitude response of nanostructures, whereas mapping the phase response is a much more challenging problem that needs to be addressed. Since the metaatom's optical response is entirely reliant on and vulnerable to its geometrical structure, underlying material, and operating wavelength, changing any of these parameters changes the entire physics of the problem in hand. Here, we propose novel deep-learning-based generalized forward and inverse design approaches to optimize all-dielectric transmissive metasurfaces. The proposed forward predicting neural networks take all the geometrical parameters and the physical properties of the bar-shaped dielectric nano-resonators as the input and predict the cross-polarized transmission amplitude and modulated phase at eight distinct rotation angles of the nano-bar. These networks are generalized to predict the electromagnetic (EM) response of different dielectric materials at different operating wavelengths. An inverse design neural network is also proposed that takes the target transmission amplitude and phase at eight discrete orientation angles of the nano-bar as the primary input. The underlying physics of the problem is also incorporated by feeding the intrinsic material properties and the operating wavelength of the nano-bar as a second input to the inverse neural network. It predicts the optimum set of geometrical parameters to achieve maximum cross-polarized transmission and complete Pancharatnam-Berry (PB) phase coverage from 0 to 2π. The average test data mean square error (MSE) achieved for the forward predicting neural network is 1.8 × 10-3 and that of the inverse design neural network is 2.8 × 10-1. The average MSEs for different material's test samples are demonstrated to validate the generalizability of the proposed models in terms of seen and unseen materials. A comparative analysis of the proposed approach with conventional EM software optimization tools is performed to prove that the proposed inverse design works much faster than the conventional methods, also it can handle a comparatively larger range of parameters and predicts the results in a single run with high accuracy.