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

Photo-induced degradation of toxic recalcitrant compounds from surface water: Insights into advanced nanomaterials, hybrid photocatalytic systems, and real applications

The rapid increase in toxic recalcitrant organic compounds (ROCs) from various industrial, residential, and agricultural sources poses a significant public health concern and threatens environmental preservation. The presence of these toxic ROCs weakens the effectiveness of conventional water and wastewater treatment systems. As a result, numerous physicochemical and biological treatment processes have been explored, each demonstrating varying removal efficiencies depending on experimental conditions. Given the limitations of existing treatment methods, research has increasingly focused on advanced oxidation processes, particularly photocatalysis. Photocatalysis is a prominent treatment technique due to its low sludge production, non-toxic nature, reusable characteristics, and ability to harness visible light. This review comprehensively examines the ecotoxicological effects of ROCs, existing biological and physicochemical treatment methods, advancements in photocatalyst synthesis, the transition from conventional to advanced photocatalysts, and hybrid treatment systems. In the context of photocatalytic removal of ROCs, the review also addresses several influencing parameters, including initial pollutant concentration, solution pH, light intensity, catalyst dose, and catalyst type. Global case studies focusing on the mechanisms of photocatalytic degradation of ROCs are highlighted. The documented photocatalysts for removing ROCs from water and wastewater have shown promising results. Moreover, integrating photocatalysis with advanced physicochemical and biological processes has effectively removed various dissolved (e.g., ROCs) and suspended impurities, showcasing its practical applications. Thus, this study could serve as a valuable resource for researchers and engineers working on the treatment of various micropollutants, such as ROCs, in real wastewater.

Complex Challenges in the Textile Industry and Potential Solutions in Photocatalytic Coating Technology: A Systematic Literature Review

This study provides a systematic review of photocatalytic fiber coating technology as a potential solution to challenges in the textile industry. An analysis of recent research (2020-2024) reveals significant developments in materials and methods. Traditional photocatalysts (TiO2 and ZnO) are being enhanced through doping and nanostructure control, and novel materials such as graphene-based composites and metal-organic frameworks are emerging. Advanced coating technologies, such as plasma treatment, atomic layer deposition, and magnetron sputtering, have been introduced to improve coating uniformity and durability. Key trends include the development of multifunctional coatings that combine self-cleaning, antibacterial effects, ultraviolet (UV) protection, and superhydrophobic properties. Environmental sustainability is advancing through eco-friendly manufacturing processes, although concerns regarding nanoparticle safety persist. While applications are expanding into medical textiles, protective gear, and wastewater treatment, challenges remain in terms of mass production technology, cost-effectiveness, and long-term durability. Future research should focus on nanostructure control, the development of visible-light-active materials, the optimization of coating processes, and the investigation of environmental impacts. This review suggests that photocatalytic fiber coating technology can significantly contribute to sustainable textile industry development when these challenges are effectively addressed.

Comparative properties of ZnO modified Au/Fe nanocomposite: electronic, dynamic, and locator annealing investigation

CONTEXT

A computational representation was used to model the doping and nanomodification of ZnO nanoparticles incorporated in Au/Fe nanocomposite. Au/Fe nanostructure was geometrically and discussed to investigate its electronic properties such electronic band structure and PDOS spectra. Moreover, the ZnO interacted with Au/Fe system was illustrated concerning the modified properties present on the surface of the nanocomposite as it may behave different attribution of band gap evaluated after ZnO modification included. Molecular dynamic simulation of the whole nano system was studied to predict the system stability concerning temperature and energy parameters during 100 ps simulation time. The most effective models under investigation were evaluated using adsorption annealing computations associated with the adsorption energy surface. A highly stable energetic adsorption system was anticipated by the periodic adsorption-annealing calculation.

METHODS

Au and Fe pure metals nanostructures were studied as a separate molecule with (0 0 1) plane surface for optimum energy minimization. Dmol3 module in/materials studio software was utilized for this protocol. The designed Au/Fe layers for nanostructure building material was computationally optimized, where DFT level was considered involving generalized gradient approximation (GGA) with Perdew-Burke-Ernzerh (PBE) exchange functional. In the computations of the structure matrix simulation, the global orbital cutoff was selected. To address the weak quantification of the standard DFT functionals, Tkatchenko-Schefer (TS) (DFT + D) was utilized to precisely correct the pairwise dispersion of the functionals. The electrical parameters were interpreted using the reciprocal space of the ultrasoft pseudopotential representation. To overcome the issues of self-electron interaction, the nonlocal hybrid functional with PBE0 method was utilized to calculate the electronic properties of the studied systems. The computations generated are predicated on a particular trajectory of the gamma k-point band energy interpolations proposed in this examination. An investigation into the position of adsorption came after geometric optimization. Adsorbed on an optimized Au/Fe surface, ZnO nanostructure was computationally explored using the Dmol3 simulation software.

Revisiting the smart metallic nanomaterials: advances in nanotechnology-based antimicrobials

Despite significant advancements in diagnostics and treatments over the years, the problem of antimicrobial drug resistance remains a pressing issue in public health. The reduced effectiveness of existing antimicrobial drugs has prompted efforts to seek alternative treatments for microbial pathogens or develop new drug candidates. Interestingly, nanomaterials are currently gaining global attention as a possible next-generation antibiotics. Nanotechnology holds significant importance, particularly when addressing infections caused by multi-drug-resistant organisms. Alternatively, these biomaterials can also be combined with antibiotics and other potent biomaterials, providing excellent synergistic effects. Over the past two decades, nanoparticles have gained significant attention among research communities. Despite the complexity of some of their synthesis strategies and chemistry, unrelenting efforts have been recorded in synthesizing potent and highly effective nanomaterials using different approaches. With the ongoing advancements in nanotechnology, integrating it into medical procedures presents novel approaches for improving the standard of patient healthcare. Although the field of nanotechnology offers promises, much remains to be learned to overcome the several inherent issues limiting their full translation to clinics. Here, we comprehensively discussed nanotechnology-based materials, focusing exclusively on metallic nanomaterials and highlighting the advances in their synthesis, chemistry, and mechanisms of action against bacterial pathogens. Importantly, we delve into the current challenges and prospects associated with the technology.

Antimicrobial Properties of Chitosan-Modified Cotton Fabric Treated with Aldehydes and Zinc Oxide Particles

Chitosan is a natural biopolymer with a proven ability to impart textile materials with antimicrobial properties when loaded onto them. The mechanism of its bacteriological activity depends on the contact between the positive and negative charges of the amino groups located on the surface of the microbes. Unfortunately, the type of microorganisms and pH influence this action-shortcomings that can be avoided by chitosan modification and by loading its film with substances possessing antimicrobial properties. In this study, chitosan was modified with benzaldehyde and crosslinked with glutaraldehyde to form a film on the surface of cotton fabric (CB). Also, another material was obtained by including zinc oxide particles (CBZ) synthesized in situ into the chitosan coating. The performed analyses (contact angle measurement, optical and scanning electron microscopy, FTIR, XRD, and thermal analysis) evidenced the modification of the cotton fabric and the alteration of the film properties after zinc oxide inclusion. A comparison of the antimicrobial properties of the new CB with materials prepared with chitosan without benzaldehyde from our previous study verified the influence of the hydrophobicity and surface roughness of the fabric surface on the enhancement of antimicrobial activity. The microbial growth inhibition increased in the following order: fungal strain Candida lipolytica >Gram-positive bacteria Bacillus cereus >Gram-negative bacteria Pseudomonas aeruginosa. The samples containing zinc oxide particles completely inhibited the growth of all three model strains. The virucidal activity of the CB was higher against human adenovirus serotype 5 (HAdV-5) than against human respiratory syncytial virus (HRSV-S2) after 60 min of exposure. The CBZ displayed higher virucidal activity with a Δlog of 0.9 against both viruses.

In-Situ Growth of ZnO Whiskers on Ti2ZnC MAX Phases

ZnO whiskers have many applications, such as in medical and photocatalysis fields. In this study, an unconventional preparation approach is reported, realizing the in-situ growth of ZnO whiskers on Ti₂ZnC. The weak bonding between the layer of Ti₆C-octahedron and the Zn-atom layers leads to the easy extraction of Zn atoms from Ti₂ZnC lattice points, resulting in the formation of ZnO whiskers on the Ti₂ZnC surface. This is the first time that ZnO whiskers have been found to grow in-situ on Ti₂ZnC substrate. Further, this phenomenon is amplified when the size of the Ti₂ZnC grains is mechanically reduced by ball-milling, which bodes a promising route to prepare ZnO in-situ on a large scale. Additionally, this finding can also help us better understand the stability of Ti₂ZnC and the whiskering mechanism of MAX phases.

Electrospinning of n-hemin/PAN Nanocomposite Membranes and Its Photo-Enhanced Enzyme-like Catalysis

Hemin possesses great potential in eliminating organic pollutants due to its mild reaction condition, light-harvesting efficiency, and environmental friendliness. However, it has drawbacks such as being easy to aggregate and hard to recycle, and poor stability should be improved in practical application. Herein, the subject developed an electrospinning approach to enable the hemin particulates to be immobilized onto polyacrylonitrile (PAN) nanofibers stably. Hydrogen peroxide (H₂O₂) was adopted as an oxidant in the system to simulate the enzymatic catalysis of hemin in an organism. Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), UV-Vis diffuse reflection spectroscopy (DRS), and electron spin resonance spectroscopy (ESR) analysis was employed to discuss the morphology, structure, and mechanism of the prepared n-hemin/PAN nanocomposite membranes, and 0.02 mmol L^-1 of the rhodamine B (RhB) removal activity in different conditions was also verified with these membranes. The kinetic studies showed that n-hemin/PAN nanocomposite membranes maintained excellent properties both in adsorption and degradation. Around 42% RhB could be adsorbed in the dark, while 91% RhB decolorized under xenon lamp irradiation in 110 min, suggesting the catalytic performance of n-hemin/PAN was greatly driven by light irradiation. Differing from the axial coordinated hemin complexes, n-hemin/PAN would catalyze hydrogen peroxide into •OH radicals rather than •OOH and high-valent metal-oxo species. This work provides an effective way to support hemin as nanocomposite membranes, in which the molecular interaction between polymer and hemin made their light adsorption an obvious red shift.

An Evaluation of the Biocatalyst for the Synthesis and Application of Zinc Oxide Nanoparticles for Water Remediation—A Review

Global water scarcity is threatening the lives of humans, and it is exacerbated by the contamination of water, which occurs because of increased industrialization and soaring population density. The available conventional physical and chemical water treatment techniques are hazardous to living organisms and are not environmentally friendly, as toxic chemical elements are used during these processes. Nanotechnology has presented a possible way in which to solve these issues by using unique materials with desirable properties. Zinc oxide nanoparticles (ZnO NPs) can be used effectively and efficiently for water treatment, along with other nanotechnologies. Owing to rising concerns regarding the environmental unfriendliness and toxicity of nanomaterials, ZnO NPs have recently been synthesized through biologically available and replenishable sources using a green chemistry or green synthesis protocol. The green-synthesized ZnO NPs are less toxic, more eco-friendly, and more biocompatible than other chemically and physically synthesized materials. In this article, the biogenic synthesis and characterization techniques of ZnO NPs using plants, bacteria, fungi, algae, and biological derivatives are reviewed and discussed. The applications of the biologically prepared ZnO NPs, when used for water treatment, are outlined. Additionally, their mechanisms of action, such as the photocatalytic degradation of dyes, the production of reactive oxygen species (ROS), the generation of compounds such as hydrogen peroxide and superoxide, Zn2+ release to degrade microbes, as well as their adsorbent properties with regard to heavy metals and other contaminants in water bodies, are explained. Furthermore, challenges facing the green synthesis of these nanomaterials are outlined. Future research should focus on how nanomaterials should reach the commercialization stage, and suggestions as to how this ought to be achieved are presented.

Serratula coronata L. Mediated Synthesis of ZnO Nanoparticles and Their Application for the Removal of Alizarin Yellow R by Photocatalytic Degradation and Adsorption

In this study, the potential of biogenic zinc oxide nanoparticles (ZnO NPs) in the removal of alizarin yellow R (AY) from aqueous solutions by photocatalytic degradation, as well as adsorption, was investigated. The synthesized ZnO NPs were prepared by the simple wet-combustion method using the plant extract of Serratula coronata L. as a reducing and stabilizing agent and characterized by powder X-ray diffraction, scanning electron microscopy, energy dispersive X-ray and X-ray photoelectron spectroscopy. Photocatalytic degradation of AY was monitored by UV-visible spectroscopy and the effects of parameters, such as light source type (UV-, visible- and sunlight), incubation time, pH, catalyst dosage and temperature on degradation were investigated. It was demonstrated that the source of light plays an important role in the efficiency of the reaction and the UV-assisted degradation of AY was the most effective, compared to the others. The degradation reaction of AY was found to follow the Langmuir-Hinshelwood mechanism and a pseudo-first-order kinetic model. The degradation kinetics of AY accelerated with increasing temperature, and the lowest activation energy (E_a) was calculated as 3.4 kJ/mol for the UV-light irradiation system, while the E_a values were 4.18 and 7.37 kJ/mol for visible light and sunlight, respectively. The dye removal by the adsorption process was also affected by several parameters, such as pH, sorbent amount and contact time. The data obtained in the kinetics study fit the pseudo-second-order equation best model and the rate constant was calculated as 0.001 g/mg·min. The isotherm analysis indicated that the equilibrium data fit well with the Freundlich isotherm model. The maximum adsorption capacity of AY on biogenic ZnO NPs was 5.34 mg/g.

A Novel Cu2O/ZnO@PET Composite Membrane for the Photocatalytic Degradation of Carbendazim

The extremely high levels of water pollution caused by various industrial activities represent one of the most important environmental problems. Efficient techniques and advanced materials have been extensively developed for the removal of highly toxic organic pollutants, including pesticides. This study investigated the photocatalytic degradation of the fungicide carbendazim (Czm) using composite track-etched membranes (TeMs) in an aqueous solution. Copper(I) oxide (Cu₂O) and zinc oxide (ZnO) microtubes (MTs) were prepared using an electroless template deposition technique in porous poly(ethylene terephthalate) (PET) TeMs with nanochannels with a density of 4 × 10⁷ pores/cm⁻² and diameter of 385 ± 9 nm to yield Cu₂O@PET and ZnO@PET composite membranes, respectively. A mixed Cu₂O/ZnO@PET composite was prepared via a two-step deposition process, containing ZnO (87%) and CuZ (13%) as crystalline phases. The structure and composition of all composite membranes were elucidated using scanning electron microscopy (SEM), atomic force microscopy (AFM), energy-dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD) techniques. Under UV–visible light irradiation, the Cu₂O/ZnO@PET composite displayed enhanced photocatalytic activity, reaching 98% Czm degradation, higher than Cu₂O@PET and ZnO@PET composites. The maximum Czm degradation efficiency from aqueous solution was obtained at an optimal pH of 6 and contact time of 140 min. The effects of various parameters such as temperature, catalyst dosage and sample exposure time on the photocatalytic degradation process were studied. The degradation reaction of Czm was found to follow the Langmuir–Hinshelwood mechanism and a pseudo-first order kinetic model. The degradation kinetics of Czm accelerated with increasing temperature, and the activation energy (E_a) levels were calculated as 11.9 kJ/mol, 14.22 kJ/mol and 15.82 kJ/mol for Cu₂O/ZnO@PET, ZnO@PET and Cu₂O@PET composite membranes, respectively. The reusability of the Cu₂O/ZnO@PET catalyst was also investigated at different temperatures for 10 consecutive runs, without any activation or regeneration processes. The Cu₂O/ZnO@PET composite exhibited degradation efficiency levels of over 50% at 14 °C and over 30% at 52 °C after 5 consecutive uses.

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.

Novel Alkali-Activated Materials with Photocatalytic and Bactericidal Properties Based on Ceramic Tile Waste

Ceramics tile wastes (CWs) were mechanically conditioned for the preparation of alkali-activated hybrid-cements from CW (90 wt.%) and Portland cement (10 wt.%) mixtures using sodium silicate (SS) + NaOH as alkaline activators. Molar ratios of SiO2/Al2O3 (6.3 to 7.7) and Na2O/SiO2 (0.07 to 0.16) were used. The cements were prepared at room temperature (25 °C) and characterized by mechanical and physical properties and microstructure. The optimized cement was used for the preparation of novel photoactivated composite materials by incorporating 5 and 10 wt.% TiO2 (Ti) and ZnO (Z) nanoparticles, and its self-cleaning and bactericidal properties were evaluated by means of the degradation of rhodamine-B (Rh-B) and the growth inhibition of Klebsiella pneumoniae and Pseudomonas aeruginosa bacteria. The results of this study showed that the 100SS-5Z and 50SS:50G-10Ti cements have an effective photocatalytic activity for Rh-B degradation of 98.4% and 76.4%, respectively, after 24 h. Additionally, the 100SS-5Z and 50SS:50G-10Ti cement pastes and their respective mortars were effective in inhibiting the growth of Pseudomonas Aeruginosa and Klebsiella pneumoniae bacterial strains, evidenced by the formation of bacterial inhibition halos around the sample discs. Finally, these results are novel, and open the possibility of using constructions and demolition tile waste in high proportions for the elaboration of new rendering mortar with innovative properties.

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.

Influence of Polyethylene Terephthalate Powder on Hydration of Portland Cement

The management of plastic waste is a massive challenge and the recycling of plastics for newer applications is a potential solution. This study investigates the feasibility of using polyethylene terephthalate (PET) powder in cementitious composites. The changes in the strength and microstructure of Portland cement incorporating PET powder with different replacement ratios were systematically analyzed through the measurements of compressive strength, isothermal calorimetry, X-ray diffraction, thermogravimetric analysis, and Raman spectroscopy. In addition, the possible chemical changes of cement paste samples were studied upon exposure to different conditions, including deionized water, seawater, and simulated pore solution. Based on the test results and analysis, no apparent chemical changes were observed in the cement paste samples, regardless of the exposure conditions. In contrast, the PET powder incorporated into concrete exhibited remarkable changes, which may have occurred during the mixing process. The results also suggested that the maximum replacement ratio of PET powder should be less than 10% of the binder (by mass) to minimize its influence on cement hydration, due to the interaction between water and PET. The PET-containing samples showed the presence of calcium aluminate hydrates which were absent in the neat paste sample.

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.