Thermophysiological comfort of zinc oxide nanoparticles coated woven fabrics

ZnO Nanoparticles Enhance the Antimicrobial Properties of Two-Sided-Coated Cotton Textile

Cotton textiles improved with metal oxide nanoparticles acquire additional features that may enhance their action against antimicrobial-resistant pathogens due to the unique properties and characteristics of the nanoparticles. The main objective of this work is to evaluate the antimicrobial features of two-sided-coated cotton textiles with ZnO nanoparticles. Nanoparticles were deposited using green chemistry technology with low-temperature oxygen plasma. ZnO particles formed stable structures on textile fibers. The optimal deposition parameters (150 W plasma power, 120 min immersion time) achieved the best effects against Gram-negative and Gram-positive bacteria and microscopic fungi. Two-sided-coated cotton with ZnO nanoparticles showed high antibacterial action on Gram-negative and Gram-positive bacteria. Modification with zinc oxide inhibited the growth of Candida albicans by more than half.

Therapeutic Textiles Functionalized with Keratin-Based Particles Encapsulating Terbinafine for the Treatment of Onychomycosis

Onychomycosis is the most common nail fungal infection worldwide. There are several therapy options available for onychomycosis, such as oral antifungals, topicals, and physical treatments. Terbinafine is in the frontline for the treatment of onychomycosis; however, several adverse effects are associated to its oral administration. In this work, innovative keratin-based carriers encapsulating terbinafine were designed to overcome the drawbacks related to the use this drug. Therapeutic textiles functionalized with keratin-based particles (100% keratin; 80% keratin/20% keratin-PEG) encapsulating terbinafine were developed. The controlled release of terbinafine from the functionalized textiles was evaluated against different mimetic biologic solutions (PBS buffer-pH = 7.4, micellar solution and acidic sweat solution-pH = 4.3). The modification of keratin with polyethylene glycol (PEG) moieties favored the release of terbinafine at the end of 48 h for all the solution conditions. When the activity of functionalized textiles was tested against Trichophyton rubrum, a differentiated inhibition was observed. Textiles functionalized with 80% keratin/20% keratin-PEG encapsulating terbinafine showed a 2-fold inhibition halo compared with the textiles containing 100% keratin-encapsulating terbinafine. No activity was observed for the textiles functionalized with keratin-based particles without terbinafine. The systems herein developed revealed therapeutic potential towards nail fungal infections, taking advantage of keratin-based particles affinity to keratin structures and of the keratinase activity of T. rubrum.

Green synthesis and characterization of zinc oxide nanoparticles using bush tea (Athrixia phylicoides DC) natural extract: assessment of the synthesis process

Background: Nanoparticles are globally synthesized for their antimicrobial, anti-inflammatory, wound healing, catalytic, magnetic, optical, and electronic properties that have put them at the forefront of a wide variety of studies. Among them, zinc oxide (ZnO) has received much consideration due to its technological and medicinal applications. In this study, we report on the synthesis process of ZnO nanoparticles using Athrixia phylicoides DC natural extract as a reducing agent.   Methods: Liquid chromatography–mass spectrometry (LC-MS) was used to identify the compounds responsible for the synthesis of ZnO nanoparticles. Structural, morphological and optical properties of the synthesized nanoparticles have been characterized through X-ray diffraction (XRD), Ultraviolet-visible spectroscopy (UV-Vis), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS).   Results: LC-MS results showed that different flavonoids and polyphenols, as well as Coumarin, an aromatic compound, reacted with the precursor to form ZnO nanoparticles. XRD and UV-Vis analysis confirmed the synthesis of ZnO nanoparticles, with a spherical shape showed in SEM images. The quasi-spherical ZnO crystals had an average crystallite size of 24 nm. EDS and FTIR analysis confirmed that the powders were pure with no other phase or impurity.   Conclusions: This study successfully demonstrated that the natural plant extract of A. phylicoides DC. can be used in the bio-reduction of zinc nitrate hexahydrate to prepare pure ZnO nanoparticles, thus, extending the use of this plant to an industrial level.

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.

Superior Unidirectional Water Transport and Mechanically Stable 3D Orthogonal Woven Fabric for Human Body Moisture and Thermal Management

Unidirectional water transport performance is vital for maintaining human thermal and wet comfort in the field of garment materials. In this work, a 3D orthogonal woven fabric (3DOWF) with excellent one-way transport capacity and mechanical properties is developed via 3D weaving and plasma treatment. The 3DOWF consists of polyester yarns (first layer), cotton yarns (second layer), and viscose yarns (third layer) with successively enhanced water absorption capacity. This allows droplets to penetrate spontaneously from the hydrophobic layer to the hydrophilic layer but not vice versa. Moreover, the Coolmax yarn with the core suction effect in the Z-direction and the plasma-treated polyester of the 3DOWF are shown to efficiently speed up the water transport process. In particular, the water penetration rate of the 3DOWF reaches 25 µl s^-1 . In turn, the surface temperature after water absorption is increased by 2.6 °C compared with the cotton fabric, while the tensile strengths in the weft and warp directions of the 3DOWF are 49.62 and 18 MPa, respectively. These values represent the best insulation and mechanical characteristics thus far reported among unidirectional water transport fabrics. Therefore, the 3DOWF has great potential for use in watchbands, backpack belts, insoles, and other functional textiles.

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.

Synergistic Effect of Screen-Printed Single-Walled Carbon Nanotubes and Phosphorylated Cellulose Nanofibrils on Thermophysiological Comfort, Thermal/UV Resistance, Mechanical and Electroconductive Properties of Flame-Retardant Fabric

Single-walled carbon nanotubes (SWCNTs) and phosphorylated nanocellulose fibrils (PCNFs) were used as functional screen-print coatings on flame-retardant (FR) fabric, to improve its thermal resistance and thermophysiological comfort (wetting, water vapour and heat transmission) properties, while inducing it with electrical conductivity and UV protection. The effect of PCNF printing, followed by applying a hydrophobic polyacrylate (AP), on the same (back/B, turned outwards) or other (front/F, turned towards skin) side of the fabric, with and without the addition of 0.1–0.4 wt% SWCNTs, was studied by determining the amount of applied coating and its distribution (microscopic imaging), and measuring the fabric’s colour, air permeability, thickness, mechanical, flame and abrasion resistance properties. Due to the synergistic effect of PCNF and SWCNTs, both-sided printed fabric (front-side printed with PCNF and back-side with SWCNTs within AP) resulted in an increased heat transfer (25%) and an improved thermal resistance (shift of degradation temperature by up to 18 °C towards a higher value) and UV protection (UPF of 109) without changing the colour of the fabric. Such treatment also affected the moisture management properties with an increased water-vapour transfer (17%), reduced water uptake (39%) and asymmetric wettability due to the hydrophilic front (Contact Angle 46°) and hydrophobic back (129°) side. The increased tensile (16%) and tear (39%) strengths were also assessed in the warp direction, without worsening the abrasion resistance of the front-side. A pressure-sensing electrical conductivity (up to 4.9∙10⁻⁴ S/cm with an increase to 12.0∙10⁻⁴ S/cm at 2 bars) of the SWCNT-printed side ranks the fabric among the antistatic, electrostatic discharge (ESD) or electromagnetic interference (EMI) shielding protectives.

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.