Thermophysiological comfort of sonochemically synthesized nano TiO2 coated woven fabrics

Photothermal Janus fabrics enabling persistent directional sweat-wicking in personal wet-thermal management

Directional sweat-wicking by Janus fabrics has gained substantial attention in promoting personal wet-thermal management for optimal human comfort. During intense physical exercise, excessive sweating can cause the flooding of fabrics and weaken their wicking capabilities once the inner capillary channels are saturated. To address this issue, we develop a photothermal Janus fabric through a facile polydopamine (PDA) deposition followed by single-sided spray-coating of hydrophobic polydimethylsiloxane (PDMS). Such innovative fabrics enable directional sweat-wicking through a Janus structure and persistent removal of excessive sweat by solar-powered evaporation. Under sunlight, our photothermal Janus fabrics exhibit an enhanced evaporation rate, approximately twice compared with that of conventional Janus fabrics (∼1.143 ± 0.027 kg m-2h-1), making them suitable for high sweating rates during vigorous exercise. Furthermore, these fabrics help to maintain the skin temperature within the normal range, preventing hypothermia caused by profuse sweating. In addition, our photothermal Janus fabrics exhibit excellent washing durability even after multiple washing cycles, ensuring prolonged performance and safety.

Biosynthesis, characterization and optimization of TiO2 nanoparticles by novel marine halophilic Halomonas sp. RAM2: application of natural dye-sensitized solar cells

BACKGROUND

Metal oxide nanoparticles (NPs) are becoming valuable due to their novel applications. The green synthesis of TiO₂ NPs is more popular as a flexible and eco-friendly method compared to traditional chemical synthesis methods. TiO₂ NPs are the most commonly used semiconductor in dye-sensitized solar cells (DSSCs).

RESULTS

The biogenic TiO₂ NPs were produced extracellularly by the marine halophilic bacterium Halomonas sp. RAM2. Response surface methodology (RSM) was used to optimize the biosynthesis process, resulting in a starting TiO₂ concentration of 0.031 M and a pH of 5 for 92 min (⁓15 nm). TiO₂ NPs were well-characterized after the calcination process at different temperatures of 500, 600, 700 and 800 °C. Anatase TiO₂ NPs (calcined at 500 °C) with a smaller surface area and a wider bandgap were nominated for use in natural dye-sensitized solar cells (NDSSCs). The natural dye used as a photosensitizer is a mixture of three carotenoids extracted from the marine bacterium Kocuria sp. RAM1. NDSSCs were evaluated under standard illumination. After optimization of the counter electrode, NDSSC_Bio(10) (10 layers) demonstrated the highest photoelectric conversion efficiency (η) of 0.44%, which was almost as good as NDSSC_P25 (0.55%).

CONCLUSION

The obtained results confirmed the successful green synthesis of TiO₂ NPs and suggested a novel use in combination with bacterial carotenoids in DSSC fabrication, which represents an initial step for further efficiency enhancement studies.

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.

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.

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.

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.