Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model.

This paper introduces a novel technique to evaluate comfort properties of zinc oxide nanoparticles (ZnO NPs) coated woven fabrics. The proposed technique combines artificial neural network (ANN) and golden eagle optimizer (GEO) to ameliorate the training process of ANN. Neural networks are state-of-the-art machine learning models used for optimal state prediction of complex problems. Recent studies showed that the use of metaheuristic algorithms improve the prediction accuracy of ANN. GEO is the most advanced methaheurstic algorithm inspired by golden eagles and their intelligence for hunting by tuning their speed according to spiral trajectory. From application point of view, this study is a very first attempt where GEO is applied along with ANN to improve the training process of ANN for any textiles and composites application. Furthermore, the proposed algorithm ANN with GEO (ANN-GEO) was applied to map out the complex input-output conditions for optimal results. Coated amount of ZnO NPs, fabric mass and fabric thickness were selected as input variables and comfort properties were evaluated as output results. The obtained results reveal that ANN-GEO model provides high performance accuracy than standard ANN model, ANN models trained with latest metaheuristic algorithms including particle swarm optimizer and crow search optimizer, and conventional multiple linear regression.

Use of an Artificial Neural Network for Tensile Strength Prediction of Nano Titanium Dioxide Coated Cotton.

In this study, an artificial neural network (ANN) is used for the prediction of tensile strength of nano titanium dioxide (TiO2) coated cotton. The coating process was performed by ultraviolet (UV) radiations. Later on, a backpropagation ANN algorithm trained with Bayesian regularization was applied to predict the tensile strength. For a comparative study, ANN results were compared with traditional methods including multiple linear regression (MLR) and polynomial regression analysis (PRA). The input conditions for the experiment were dosage of TiO2, UV irradiation time and temperature of the system. Simulation results elucidated that ANN model provides high performance accuracy than MLR and PRA. In addition, statistical analysis was also performed to check the significance of this study. The results show a strong correlation between predicted and measured tensile strength of nano TiO2-coated cotton with small error values.

Aerogels for Biomedical, Energy and Sensing Applications.

The term aerogel is used for unique solid-state structures composed of three-dimensional (3D) interconnected networks filled with a huge amount of air. These air-filled pores enhance the physicochemical properties and the structural characteristics in macroscale as well as integrate typical characteristics of aerogels, e.g., low density, high porosity and some specific properties of their constituents. These characteristics equip aerogels for highly sensitive and highly selective sensing and energy materials, e.g., biosensors, gas sensors, pressure and strain sensors, supercapacitors, catalysts and ion batteries, etc. In recent years, considerable research efforts are devoted towards the applications of aerogels and promising results have been achieved and reported. In this thematic issue, ground-breaking and recent advances in the field of biomedical, energy and sensing are presented and discussed in detail. In addition, some other perspectives and recent challenges for the synthesis of high performance and low-cost aerogels and their applications are also summarized.

Excellent UV-Light Triggered Photocatalytic Performance of ZnO.SiO2 Nanocomposite for Water Pollutant Compound Methyl Orange Dye.

The photocatalytic activity of eco-friendly zinc oxide doped silica nanocomposites, synthesized via a co-precipitation method followed by heat-treatment at 300, 600, and 900 °C is investigated. The samples have been characterized by employing X-ray diffraction method, and further analyzed using the Rietveld Refinement method. The samples show a space group P63mc with hexagonal structure. The prepared composites are tested for their photocatalytic activities for the degradation of methyl orange-based water pollutants under ultra-violet (UV) irradiation using a 125 W mercury lamp. A systematic analysis of parameters such as the irradiation time, pH value, annealing temperatures, and the concentration of sodium hydroxide impacting the degradation of the methyl orange (MO) is carried out using UV-visible spectroscopy. The ZnO.SiO2 nanocomposite annealed at 300 °C at a pH value of seven shows a maximum photo-degradation ability (~98.1%) towards methyl orange, while the photo-degradation ability of ZnO.SiO2 nanocomposites decreases with annealing temperature (i.e., for 600 and 900 °C) due to the aspect ratio. Moreover, it is seen that with increment in the concentration of the NaOH (i.e., from 1 to 3 g), the photo-degradation of the dye component is enhanced from 20.9 to 53.8%, whereas a reverse trend of degradation ability is observed for higher concentrations.

Prediction of Methylene Blue Removal by Nano TiO2 Using Deep Neural Network.

This paper deals with the prediction of methylene blue (MB) dye removal under the influence of titanium dioxide nanoparticles (TiO2 NPs) through deep neural network (DNN). In the first step, TiO2 NPs were prepared and their morphological properties were analysed by scanning electron microscopy. Later, the influence of as synthesized TiO2 NPs was tested against MB dye removal and in the final step, DNN was used for the prediction. DNN is an efficient machine learning tools and widely used model for the prediction of highly complex problems. However, it has never been used for the prediction of MB dye removal. Therefore, this paper investigates the prediction accuracy of MB dye removal under the influence of TiO2 NPs using DNN. Furthermore, the proposed DNN model was used to map out the complex input-output conditions for the prediction of optimal results. The amount of chemicals, i.e., amount of TiO2 NPs, amount of ehylene glycol and reaction time were chosen as input variables and MB dye removal percentage was evaluated as a response. DNN model provides significantly high performance accuracy for the prediction of MB dye removal and can be used as a powerful tool for the prediction of other functional properties of nanocomposites.

Classification of Textile Polymer Composites: Recent Trends and Challenges.

Polymer based textile composites have gained much attention in recent years and gradually transformed the growth of industries especially automobiles, construction, aerospace and composites. The inclusion of natural polymeric fibres as reinforcement in carbon fibre reinforced composites manufacturing delineates an economic way, enhances their surface, structural and mechanical properties by providing better bonding conditions. Almost all textile-based products are associated with quality, price and consumer's satisfaction. Therefore, classification of textiles products and fibre reinforced polymer composites is a challenging task. This paper focuses on the classification of various problems in textile processes and fibre reinforced polymer composites by artificial neural networks, genetic algorithm and fuzzy logic. Moreover, their limitations associated with state-of-the-art processes and some relatively new and sequential classification methods are also proposed and discussed in detail in this paper.

Combined Use of Modal Analysis and Machine Learning for Materials Classification.

The present study deals with modal work that is a type of framework for structural dynamic testing of linear structures. Modal analysis is a powerful tool that works on the modal parameters to ensure the safety of materials and eliminate the failure possibilities. The concept of classification through this study is validated for isotropic and orthotropic materials, reaching up to a 100% accuracy when deploying the machine learning approach between the mode number and the associated frequency of the interrelated variables that were extracted from modal analysis performed by ANSYS. This study shows a new classification method dependent only on the knowledge of resonance frequency of a specific material and opens new directions for future developments to create a single device that can identify and classify different engineering materials.

Neural network-crow search model for the prediction of functional properties of nano TiO2 coated cotton composites.

This paper presents a new hybrid approach for the prediction of functional properties i.e., self-cleaning efficiency, antimicrobial efficiency and ultraviolet protection factor (UPF), of titanium dioxide nanoparticles (TiO2 NPs) coated cotton fabric. The proposed approach is based on feedforward artificial neural network (ANN) model called a multilayer perceptron (MLP), trained by an optimized algorithm known as crow search algorithm (CSA). ANN is an effective and widely used approach for the prediction of extremely complex problems. Various studies have been proposed to improve the weight training of ANN using metaheuristic algorithms. CSA is a latest and an effective metaheuristic method relies on the intelligent behavior of crows. CSA has been never proposed to improve the weight training of ANN. Therefore, CSA is adopted to optimize the initial weights and thresholds of the ANN model, in order to improve the training accuracy and prediction performance of functional properties of TiO2 NPs coated cotton composites. Furthermore, our proposed algorithm i.e., multilayer perceptron with crow search algorithm (MLP-CSA) was applied to map out the complex input-output conditions to predict the optimal results. The amount of chemicals and reaction time were selected as input variables and the amount of titanium dioxide coated on cotton, self-cleaning efficiency, antimicrobial efficiency and UPF were evaluated as output results. A sensitivity analysis was carried out to assess the performance of CSA in prediction process. MLP-CSA provided excellent result that were statistically significant and highly accurate as compared to standard MLP model and other metaheuristic algorithms used in the training of ANN reported in the literature.

Geopolymers and Fiber-Reinforced Concrete Composites in Civil Engineering.

This paper discusses the influence of fiber reinforcement on the properties of geopolymer concrete composites, based on fly ash, ground granulated blast furnace slag and metakaolin. Traditional concrete composites are brittle in nature due to low tensile strength. The inclusion of fibrous material alters brittle behavior of concrete along with a significant improvement in mechanical properties i.e., toughness, strain and flexural strength. Ordinary Portland cement (OPC) is mainly used as a binding agent in concrete composites. However, current environmental awareness promotes the use of alternative binders i.e., geopolymers, to replace OPC because in OPC production, significant quantity of CO2 is released that creates environmental pollution. Geopolymer concrete composites have been characterized using a wide range of analytical tools including scanning electron microscopy (SEM) and elemental detection X-ray spectroscopy (EDX). Insight into the physicochemical behavior of geopolymers, their constituents and reinforcement with natural polymeric fibers for the making of concrete composites has been gained. Focus has been given to the use of sisal, jute, basalt and glass fibers.

Prediction of functional properties of nano [Formula: see text] coated cotton composites by artificial neural network.

This paper represents the efficiency of machine learning tool, i.e., artificial neural network (ANN), for the prediction of functional properties of nano titanium dioxide coated cotton composites. A comparative analysis was performed between the predicted results of ANN, multiple linear regression (MLR) and experimental results. ANN was applied to map out the complex input-output conditions to predict the optimal results. A backpropagation ANN model called a multilayer perceptron (MLP), trained with Bayesian regularization were used in this study. The amount of chemicals and reaction time were selected as input variables and the amount of titanium dioxide coated on cotton, self-cleaning efficiency, antimicrobial efficiency and ultraviolet protection factor were analysed as output results. The accuracy of the proposed algorithm was evaluated and compared with MLR results. The obtained results reveal that MLP provides efficient results that are statistically significant in the prediction of functional properties ([Formula: see text]) compared to MLR. The correlation coefficient of MLP model ([Formula: see text]) indicates that there is a strong correlation between the measured and predicted functional properties with a trivial mean absolute error and root mean square errors values. MLP model is suitable for the functional properties and can be used for the investigation of other properties of nano coated fabrics.

Photocatalytic Behaviour of Zinc Oxide Nanostructures on Surface Activation of Polymeric Fibres.

Zinc oxide (ZnO) in various nano forms (nanoparticles, nanorods, nanosheets, nanowires and nanoflowers) has received remarkable attention worldwide for its functional diversity in different fields i.e., paints, cosmetics, coatings, rubber and composites. The purpose of this article is to investigate the role of photocatalytic activity (role of photogenerated radical scavengers) of nano ZnO (nZnO) for the surface activation of polymeric natural fibres especially cotton and their combined effect in photocatalytic applications. Photocatalytic behaviour is a crucial property that enables nZnO as a potential and competitive candidate for commercial applications. The confirmed features of nZnO were characterised by different analytical tools, i.e., scanning electron microscopy (SEM), field emission SEM (FESEM) and elemental detection spectroscopy (EDX). These techniques confirm the size, morphology, structure, crystallinity, shape and dimensions of nZnO. The morphology and size play a crucial role in surface activation of polymeric fibres. In addition, synthesis methods, variables and some of the critical aspects of nZnO that significantly affect the photocatalytic activity are also discussed in detail. This paper delineates a vivid picture to new comers about the significance of nZnO in photocatalytic applications.

Thermophysiological comfort of zinc oxide nanoparticles coated woven fabrics.

This study investigates physicochemical impact of ultrasonic irradiations on surface topography of woven fabrics. In a simultaneous in-situ sonochemical method, the synthesis and coating of zinc oxide nanoparticles (ZnO NPs) on woven textiles were successfully achieved. Different instruments i.e. Alambeta, moisture management tester, air permeability tester and permetester were utilised during experimentation for thermal evaluation, moisture transportation and air permeation. The results regarding thermophysiological comfort of ZnO coated fabrics were evaluated on the basis of thickness and ZnO NPs coated amount on fabrics. In addition, the achieved results depict the impact of sonication (pressure gradient) on surface roughness of cotton and polyester. The coating of ZnO NPs on fabrics, crystal phase identification, surface topography and fluctuations in surface roughness were estimated by inductively coupled plasma atomic emission spectroscopy (ICP-AES), X-ray Diffractometry (XRD), ultrahigh-resolution scanning electron microscopy (UHR-SEM) and energy dispersive X-ray (EDX). Moreover, thermophysiological properties i.e. thermal conductivity, absolute evaporative resistance, thermal absorptivity, air permeability, overall moisture management capacity and relative water vapour permeability of untreated and ZnO treated samples were evaluated by standard test methods.

Thermophysiological comfort of sonochemically synthesized nano TiO2 coated woven fabrics.

This work investigates thermophysiological comfort properties of sonochemically synthesized nano TiO2 coated cotton and polyester woven fabrics. The obtained results were analysed on heat and mass transfer basis. Moisture management tester and Alambeta were utilised for moisture transportation and thermal evaluation. This study precisely investigates the effects of sonication on surface roughness of nano TiO2 coated and uncoated samples. Ultrasonic acoustic method was applied to imbibe nano TiO2 on fabric samples. Surface topography, morphology and the existence of nano TiO2 on investigated samples were analysed by scanning electron microscopy and inductively coupled plasma atomic emission spectroscopy. In addition, standard test methods were applied to estimate physical and thermophysiological comfort properties i.e. thermal resistance, thermal diffusivity, heat flow, wetting time and accumulative one-way transport index of uncoated and nano TiO2 coated samples.