Functional Properties of Sonochemically Synthesized Zinc Oxide Nanoparticles and Cotton Composites

Tutorial review on the processing and performance of fabrics with antipathogenic inorganic agents

Functionalized textiles have been increasingly used for enhancing antimicrobial or antiviral (antipathogenic) action. Those pathogens can cause recurring diseases by direct or indirect transmission. Particularly, airborne microorganisms may cause respiratory diseases or skin infections like allergies and acne and the use of inorganic agents such as metal and metal oxides has proven effective in antipathogen applications. This review is a tutorial on how to obtain functional fabric with processes easily applied for industrial scale. Also, this paper summarizes relevant textiles and respective incorporated inorganic agents, including their antipathogenic mechanism of action. In addition, the processing methods and functional finishing, on a laboratory and industrial scale, to obtain a functional textile are shown. Characterization techniques, including antipathogenic activity and durability, mechanical properties, safety, and environmental issues, are presented. Challenges and perspectives on the broader use of antipathogenic fabrics are discussed.

Anticancer effect of zinc oxide nanoparticles prepared by varying entry time of ion carriers against A431 skin cancer cells in vitro

Although, zinc oxide nanoparticles (ZRTs) as an anti-cancer agent have been the subject of numerous studies, none of the reports has investigated the impact of the reaction entry time of ion-carriers on the preparation of ZRTs. Therefore, we synthesized variants of ZRTs by extending the entry time of NaOH (that acts as a carrier of hydroxyl ions) in the reaction mixture. The anti-proliferative action, morphological changes, reactive oxygen species (ROS) production, and nuclear apoptosis of ZRTs on human A431 skin carcinoma cells were observed. The samples revealed crystallinity and purity by X-ray diffraction (XRD). Scanning electron microscopy (SEM) images of ZRT-1 (5 min ion carrier entry) and ZRT-2 (10 min ion carrier entry) revealed microtubule like morphology. On prolonging the entry time for ion carrier (NaOH) introduction in the reaction mixture, a relative ascent in the aspect ratio was seen. The typical ZnO band with a slight shift in the absorption maxima was evident with UV-visible spectroscopy. Both ZRT-1 and ZRT-2 exhibited non-toxic behavior as evident by RBC lysis assay. Additionally, ZRT-2 showed better anti-cancer potential against A431 cells as seen by MTT assay, ROS generation and chromatin condensation analyses. At 25 μM of ZRT-2, 5.56% cells were viable in MTT test, ROS production was enhanced to 166.71%, while 33.0% of apoptotic cells were observed. The IC50 for ZRT-2 was slightly lower (6 μM) than that for ZRT-1 (8 μM) against A431 cells. In conclusion, this paper presents a modest, economical procedure to generate ZRT nano-structures exhibiting strong cytotoxicity against the A431 cell line, indicating that ZRTs may have application in combating cancer.

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.

Antimicrobial and Antiviral Activities of Durable Cotton Fabrics Treated with Nanocomposite Based on Zinc Oxide Nanoparticles, Acyclovir, Nanochitosan, and Clove Oil

In this study, cotton fabrics based on zinc oxide nanoparticles in situ synthesis, acyclovir, nanochitosan, and clove oil were treated. The treated cotton fabrics were examined by FTIR, HR-TEM, FE-SEM, EDAX, and the surface roughness processing of FE-SEM images. The obtained characterization data emphasized the nano-size of nanocomposite with high homogeneity of particles in spherical shape as well as affirmed the deposition of nanocomposite onto the textile fibers with concluded that the deposition of nanocomposite was increased parallel with sonication time. Antimicrobial and antiviral activities of treated cotton fabrics were evaluated. Results revealed that treated cotton fabrics exhibited promising antibacterial activity toward Gram-positive higher than Gram-negative bacteria. Likewise, treated cotton fabrics are still effective as antibacterial after washing for 100 cycles. Moreover, treated cotton fabrics exhibited potential antifungal activity against Candida albicans, Aspergillus niger, and Aspergillus fumigatus. The antiviral activity significantly depended on the type of virus. The treated cotton fabrics showed antiviral activity against tested viral particles (HSV-1, Adeno, and CoxB2) with viral inhibition of 95.9, 76.4, and 86.9% respectively, while in the case of coated cotton textile with acyclovir, it only exhibited viral inhibition of 49.9, 41, and 22.3% respectively.

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.

A Novel and Highly Effective Natural Vibration Modal Analysis to Predict Nominal Strength of Open Hole Glass Fiber Reinforced Polymer Composites Structure

Glass fiber reinforced polymer (GFRP) composite laminates are considered the key material in many industries such as the infrastructure industries and the aerospace sector, and in building structures due to their superior specific strength and lightweight properties. The prediction of specimens’ nominal strength with open holes is still an attractive and questionable field of study. The specimen size effect is referred to its strength degradation due to the presence of holes when specimen geometry gets scaled. The non-destructive test used to measure the nominal strength of such material is a great tool for fast selection purposes, but not secure enough for several purposes. Furthermore, the destructive tests which are more expensive and time-consuming should be avoided in such structures. The present work aims to predict the nominal strength of open-hole GFRP’s composite using modal analysis of their natural frequency as non-destructive tests. At this end, the natural frequency, which is measured using modal analysis procedures, is combined with both linear elastic fracture mechanics (LEFM) and the theory of elasticity to predict the nominal strength of open-hole composite laminates. This advanced model employs two parameters of surface release energy resulting from a simple tension test and Young’s modulus based on vibration modal analysis. It is well established that these types of materials are also subjected to a size effect in dynamic response. Inversely to the known static loading size effect, the size effect in dynamic response increases with specimen size. The novel model gives excellent and acceptable results when compared with experimental and finite element ones. Size effects curves of a nominal strength of these laminates have a very close relative value with those obtained from finite element and analytical modeling. Moreover, the received design tables and graphs would be highly applicable when selecting suitable materials for similar industrial applications.

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.

The Effect of Fiber Type and Yarn Diameter on Superhydrophobicity, Self-Cleaning Property, and Water Spray Resistance

In this study, we proved that micro/micro hierarchical structures are enough to achieve a superhydrophobic surface using polydimethylsiloxane (PDMS) dip-coating. Furthermore, the effect of fiber type and yarn diameter on superhydrophobicity and water spray resistance was investigated. Polyester fabrics with two types of fibers (staple fabric and filament) and three types of yarn diameters (177D, 314D, and 475D) were used. The changes in the surface properties and chemical composition were investigated. Static contact angles and shedding angles were measured for superhydrophobicity, and the self-cleaning test was conducted. Water spray repellency was also tested, as well as the water vapor transmission rate and air permeability. The PDMS-coated staple fabric showed better superhydrophobicity and oleophobicity than the PDMS-coated filament fabric, while the filament fabric showed good self-cleaning property and higher water spray repellency level. When the yarn diameter increased, the fabrics needed higher PDMS concentrations and longer coating durations for uniform coating. The water vapor transmission rate and air permeability did not change significantly after coating. Therefore, the superhydrophobic micro/micro hierarchical fabrics produced using the simple method of this study are more practical and have great potential for mass production than other superhydrophobic textiles prepared using the chemical methods.

Multifunctional Hydroxyapatite/Silver Nanoparticles/Cotton Gauze for Antimicrobial and Biomedical Applications

Medical textiles have played an increasingly important protection role in the healthcare industry. This study was aimed at improving the conventional cotton gauze for achieving advanced biomedical specifications (coloration, UV-protection, anti-inflammation, and antimicrobial activities). These features were obtained by modifying the cotton gauze fabrics via in-situ precipitation of hydroxyapatite nanoparticles (HAp NP), followed by in-situ photosynthesis of silver (Ag) NPs with ginger oil as a green reductant with anti-inflammation properties. The HAp-Ag NPs coating provides good UV-protection properties. To further improve the HAp and Ag NPs dispersion and adhesion on the surface, the cotton gauze fabrics were modified by cationization with chitosan, or by partial carboxymethylation (anionic modification). The influence of the cationic and anionic modifications and HAp and Ag NPs deposition on the cotton gauze properties (coloration, UV-protection, antimicrobial activities, and water absorption) was thoroughly assessed. Overall, the results indicate that chemical (anionic and cationic) modification of the cotton gauze enhances HAp and Ag NPs deposition. Chitosan can increase biocompatibility and promotes wound healing properties of cotton gauze. Ag NP deposition onto cotton gauze fabrics brought high antimicrobial activities against Candida albicans, Gram-positive and Gram-negative bacteria, and improved UV protection.

Antimicrobial Activity of Zinc Oxide Nano/Microparticles and Their Combinations against Pathogenic Microorganisms for Biomedical Applications: From Physicochemical Characteristics to Pharmacological Aspects

Zinc oxide (ZnO) nano/microparticles (NPs/MPs) have been studied as antibiotics to enhance antimicrobial activity against pathogenic bacteria and viruses with or without antibiotic resistance. They have unique physicochemical characteristics that can affect biological and toxicological responses in microorganisms. Metal ion release, particle adsorption, and reactive oxygen species generation are the main mechanisms underlying their antimicrobial action. In this review, we describe the physicochemical characteristics of ZnO NPs/MPs related to biological and toxicological effects and discuss the recent findings of the antimicrobial activity of ZnO NPs/MPs and their combinations with other materials against pathogenic microorganisms. Current biomedical applications of ZnO NPs/MPs and combinations with other materials are also presented. This review will provide the better understanding of ZnO NPs/MPs as antibiotic alternatives and aid in further development of antibiotic agents for industrial and clinical 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.

Enhanced Mechanical Properties of Eucalyptus-Basalt-Based Hybrid-Reinforced Cement Composites

The present study describes the manufacturing of flat sheets of eucalyptus-basalt based hybrid reinforced cement composites (EB-HRCC). The potential of basalt fibrous waste (BFW) as a reinforcement agent in cement matrices and its effects on mechanical and interfacial properties were evaluated in detail. Significantly enhanced bending (flexural) strength and ductility were observed for all developed composite samples. BFW and eucalyptus pulp (EP) were utilized as reinforcement and filling agents respectively for EB-HRCC samples. Mechanical, microstructural and physical properties of EB-HRCC samples were investigated with different formulations of BFW with EP in cement matrices. The results showed that physical properties of the composite samples were more influenced by fiber content. For standard mechanical analysis, the composite samples were placed in sealed bags for two days, thermally cured at 60 °C for five days and immersed in water in ambient conditions for one day. The obtained results showed that samples prepared under optimized conditions (4% EP and 2% BFW) had significantly higher flexural strength and bulk density with lower water absorption and apparent void volume (porosity). Moreover, the higher percentage of BFW significantly enhanced the values of modulus of rupture (MOR), modulus of elasticity (MOE), specific energy (SE) and limit of proportionality (LOP). The effects of entrapped air under the four-point bending test on the mechanical behavior of hybrid composites were also investigated in this thematic study. The composites were designed to be used as roofing tile alternatives.

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.