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Nanotechnology has recently found applications in many fields such as consumer products, medicine and environment. Nanoparticles display unique properties and vary widely according to their dimensions, morphology, composition, agglomeration and uniformity states. Nanomaterials include carbon-based nanoparticles, metal-based nanoparticles, organic-based nanoparticles and composite-based nanoparticles. The increasing production and use of nanoparticles result in higher exposure to humans and the environment, thus raising issues of toxicity. Here we review the properties, applications and toxicity of metal and non-metal-based nanoparticles. Nanoparticles are likely to be accumulated in sensitive organs such as heart, liver, spleen, kidney and brain after inhalation, ingestion and skin contact. In vitro and in vivo studies indicate that exposure to nanoparticles could induce the production of reactive oxygen species (ROS), which is a predominant mechanism leading to toxicity. Excessive production of ROS causes oxidative stress, inflammation and subsequent damage to proteins, cell membranes and DNA. ROS production induced by nanoparticles is controlled by size, shape, surface, composition, solubility, aggregation and particle uptake. The toxicity of a metallic nanomaterial may differ depending on the oxidation state, ligands, solubility and morphology, and on environmental and health conditions.

Waste generation has been increasing drastically based on the world’s population and economic growth. This has significantly affected human health, natural life, and ecology. The utilization of limited natural resources, and the harming of the earth in the process of mineral extraction, and waste management have far exceeded limits. The recycling rate are continuously increasing; however, assessments show that humans will be creating more waste than ever before. Some difficulties during recycling include the significant expense involved during the separation of recyclable waste from non-disposable waste. Machine learning is the utilization of artificial intelligence (AI) that provides a framework to take as a structural improvement of the fact without being programmed. Machine learning concentrates on the advancement of programs that can obtain the information and use it to learn to make future decisions. The classification and separation of materials in a mixed recycling application in machine learning is a division of AI that is playing an important role for better separation of complex waste. The primary purpose of this study is to analyze AI by focusing on machine learning algorithms used in recycling systems. This study is a compilation of the most recent developments in machine learning used in recycling industries.

Engineered nanomaterial manufacturing and usage have been increasing in commercial products. There were 1814 nanotechnological consumer products available in the market in March 2015. Nanomaterials can accumulate, transform and increase in concentrations in biological systems. Nanomaterials offer many benefits over traditional materials, yet their small size also increases their toxicity. Bioaccumulation of nanomaterials begins with nanoparticle accumulation in the organism, then biomagnification follows in the predatory organism. Biotransformation is the last stage, whereby the chemical concentration of toxins in the organism exceeds that in the environment. Here, we review the interaction of nanomaterials with biological substances. It has been observed that the effects of nanomaterials begin at the bottom of the food chain and move all the way through the human body. We have summarized the mechanisms of interaction between engineered nanomaterials and the environment.

By greatly increasing automation, accuracy, and flexibility at every step of the additive manufacturing process, from design and production to quality assurance, artificial intelligence (AI) is revolutionizing the 3D printing industry. The integration of AI algorithms into 3D printing systems enables real-time optimization of print parameters, accurate prediction of material behavior, and early defect detection using computer vision and sensor data. Machine learning (ML) techniques further streamline the design-to-production pipeline by generating complex geometries, automating slicing processes, and enabling adaptive, self-correcting control during printing—functions that align directly with the principles of Industry 4.0/5.0, where cyber-physical integration, autonomous decision-making, and human–machine collaboration drive intelligent manufacturing systems. Along with improving operational effectiveness and product uniformity, this potent combination of AI and 3D printing also propels the creation of intelligent manufacturing systems that are capable of self-learning. This confluence has the potential to completely transform sectors including consumer products, healthcare, construction, and aerospace as it develops. This comprehensive review explores how AI enhances the capabilities of 3D printing, with a focus on process optimization, defect detection, and intelligent control mechanisms. Moreover, unresolved challenges are highlighted—including data scarcity, limited generalizability across printers and materials, certification barriers in safety-critical domains, computational costs, and the need for explainable AI.

The combination of smart materials to print a three-dimensional (3D) product has primarily driven the development of innovative technology, or four-dimensional (4D) printing. 3D-printing technology seems to have provided extensive enhancement with materials, printers, and processes in the past decade. The additive manufacturing (AM) industry is discovering the latest applications, materials, and 3D printers. AM can be defined as a method of formulating 3D parts through compiling the material layer by layer, which is conventionally made of plastics, metals, or ceramics; nevertheless, “smart” materials are also being used these days. These smart materials can be adjusted with printable characteristics or structures when additional stimulants are implemented. These 3D-printed materials modify their shape or properties with time, which is the fourth dimension and can merge with conventional 3D printing. 4D printing is the system whereby a 3D-printed object changes itself into a different structure as the result of the impact of environmental stimuli such as temperature, light, or other factors. 4D printing will open new possibilities that are convenient in significant applications, will work in extreme surroundings, and will help create a transformable structure. The objective of this review is to examine and assess the reputation and development of 4D-printing technology, including the 4D-printing process, materials, and potential applications. This review determines that 4D-printing technology has potential applications in various fields, but more research work will be essential for prospective accomplishments of this technology.

PurposeTypically, the high energy required to manufacture nanomaterials is weighed against the benefits transferred to a product. Adequately establishing the environmental characteristics of a product that contains nanomaterials requires a complete methodology. The objectives of this study are to draw attentions on life cycle information and to demonstrate the methodology for the scientific assessment of the environmental benefits of using a nanomaterial in a product to extend the product life and to provide a real example for the calculations of the approach.MethodsAbout 1317 products with nanomaterials in the market were analyzed to identify the outcomes of lifetime extension by the nanomaterial additions. Five life cycle elements were quantified to establish the cradle-to-gate (CTG) life cycle footprint of a product comprised of a nanomaterial. These are the following: the life cycle of the conventional product with the usual construction and without added nanomaterial, the life cycle of the nanomaterial manufactured from CTG per kilogram of nanomaterial, the amount of nanomaterial incorporated into the product, the quantitative improvement in the product performance due to the presence of the nanomaterial (such as increased lifespan), and the incremental energy and auxiliary materials (often negligible) involved in the incorporation of the nanomaterial into the conventional productResults and discussionThe primary challenge here is to have all five of the informational pieces in order to ensure that the environmental footprint of using a nanomaterial is complete. The results can be seen for the range of products with life extension via nanomaterials, ranging from 130 to 3100%. In these cases, the higher energy to manufacture the nanomaterial is more than offset by the avoidance of manufacturing non-nanoproducts multiple times over the life extension period.ConclusionsIt was found that several nanoscale inclusions in the products greatly increased many properties of the final product along with the lifetime. Increasing the lifetime of products by adding nanoscale inclusions will thus reduce environmental and health concerns, as well as the use of virgin materials, energy consumption, landfill allocations in the long term, and product marketability.

Metamaterials are artificial materials composed of special microstructures that have properties with unusual and useful features and can be applied to many fields. With their unique properties and sensitivity to external stimuli, metamaterials offer design flexibility to users. Traditional manufacturing is often not up to the task of creating metamaterials, which are now more accurately and more effectively analyzed than they were in the past. Recent advances in additive manufacturing (AM) have achieved remarkable success, with ensemble machine learning models demonstrating R2 values exceeding 0.97 and accuracy improvements of 9.6% over individual approaches. State-of-the-art multiphoton polymerization (MPP) techniques now reach submicron resolution (<1 μm), while selective laser melting (SLM) processes provide 20–100 μm precision for metallic metamaterials. This work offers a comprehensive review of additively manufactured 3D metamaterials, focusing on three categories of their fabrication: electromagnetic (achieving bandgaps up to 470 GHz), acoustic (providing 90% sound suppression at targeted frequencies), and mechanical (demonstrating Poisson’s ratios from −0.8 to +0.8). The relationship between different types of AM processes used in creating 3D objects and the properties of the resulting materials has been systematically reviewed. This research aims to address gaps and develop new applications to meet the modern demand for the broader use of metamaterials in advanced devices and systems that require high efficiency for sophisticated, high-performance applications.

Purpose The composites industry is constantly being formed by myriad forces—technologies, markets, people- all encouraging innovative ways to apply carbon, Kevlar®, and glass fiber composites to produce vital parts for a wide range of applications. The expanded demand for fiber-reinforced plastic (FRP) composites has prompted high manufacturing scrap and end-of-life waste volumes. Limited clearance on landfills and the high energy for virgin material production motivate the companies for practical composite recycling techniques. The work described in this study involves an array of experiments including acid treatment of outdated resin-impregnated composite fibers (prepreg) to study its effects and reclaiming the fibers for future sustainable manufacturing. Method The experiments were carried out at two different temperatures: 25 °C and also 60 °C. Sulfuric acid, nitric acid, acetone, and distilled water were used in the process, with varying treatment times of 60, 120, 240, 360, and 420 s. The recovered fibers were characterized by scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). Result The optimum treatment time, and temperature were different for all three types of fibers. Initially, the glass fiber yielded promising results at room temperature and with a minimal 120-s processing time. Carbon fiber treatment was successful at 60 °C with a 420-s treatment time. However, some surface damage was observed in the Kevlar® fiber at 60 °C. Conclusion The chemical recycling process, is the most sustainable, energy- and cost-efficient approach compared to all other available recycling processes. Also, it is possible to recover much cleaner fibers with the weave intact with an acid treatment and solvent-based recovery. Graphical Abstract

The scarcity of useable water is severe and increasing in several regions of the Middle East, Central and Southern Asia, and Northern Africa. However, the earth’s atmosphere contains 37.5 million billion gallons of water in the invisible vapor phase with fast replenishment. The United Nations Convention to Combat Desertification reports that by 2025 about 2.4 billion people will suffer from a lack of access to safe drinking water. Extensive research has been conducted during the last two decades to develop nature-inspired nanotechnology-based atmospheric water-harvesting technology (atmospheric water generator, AWG) to provide clean water to humanity. However, the performance of this technology is humidity sensitive, particularly when the relative humidity (RH) is high (>~80% RH). Moreover, the fundamental design principle of the materials system for harvesting atmospheric water is mostly unknown. In this work, we present a promising technology for solar energy-driven clean water production in arid and semi-arid regions and remote communities. A polymeric electrospun hybrid hydrogel consisting of deliquescent salt (CaCl2) and nanomaterials was fabricated, and the atmospheric water vapor harvesting capacity was measured. The harvested water was easily released from the hydrogel under regular sunlight via the photothermal effect. The experimental tests of this hybrid hydrogel (PAN/AM/graphene/CaCl2) demonstrated the feasibility of around 1.04 L of freshwater production per kilogram of the hydrogel (RH 60%). The synergistic effect enabled by photothermal materials and deliquescent salt in the hydrogel network architecture presents controllable interaction with water molecules, simultaneously realizing efficient water harvesting. This technology requires no additional input of energy. When considering the global environmental challenges and exploring the available technologies, a sustainable clean water supply for households, industry, and agriculture can be achieved from the air using this economical and practical technology.

Electrospun nanofibers have gained prominence as a versatile material, with applications spanning tissue engineering, drug delivery, energy storage, filtration, sensors, and textiles. Their unique properties, including high surface area, permeability, tunable porosity, low basic weight, and mechanical flexibility, alongside adjustable fiber diameter distribution and modifiable wettability, make them highly desirable across diverse fields. However, optimizing the properties of electrospun nanofibers to meet specific requirements has proven to be a challenging endeavor. The electrospinning process is inherently complex and influenced by numerous variables, including applied voltage, polymer concentration, solution concentration, solution flow rate, molecular weight of the polymer, and needle-to-collector distance. This complexity often results in variations in the properties of electrospun nanofibers, making it difficult to achieve the desired characteristics consistently. Traditional trial-and-error approaches to parameter optimization have been time-consuming and costly, and they lack the precision necessary to address these challenges effectively. In recent years, the convergence of materials science and machine learning (ML) has offered a transformative approach to electrospinning. By harnessing the power of ML algorithms, scientists and researchers can navigate the intricate parameter space of electrospinning more efficiently, bypassing the need for extensive trial-and-error experimentation. This transformative approach holds the potential to significantly reduce the time and resources invested in producing electrospun nanofibers with specific properties for a wide range of applications. Herein, we provide an in-depth analysis of current work that leverages ML to obtain the target properties of electrospun nanofibers. By examining current work, we explore the intersection of electrospinning and ML, shedding light on advancements, challenges, and future directions. This comprehensive analysis not only highlights the potential of ML in optimizing electrospinning processes but also provides valuable insights into the evolving landscape, paving the way for innovative and precisely engineered electrospun nanofibers to meet the target properties for various applications. Graphical abstract