Nabil Abdel-Jabbar

The global textile industry generates waste at multiple stages, including fiber production, fabric manufacturing, chemical processing, garment production, finishing, and post-consumption. This study examines approaches for effective textile waste management and maps them across stages of activity. It includes two components: a bibliometric trend analysis and a review of developments, practices, and strategies in textile waste management and valorization. The review identifies 2749 research articles and 988 distinct keywords, highlighting themes such as recycling, value-added products, circular economy, and sustainability. The study maps in-process, after-process, and post-consumer waste within the supply chain or redirects it into value-added products within a circular economy framework. In some cases, textile waste is reused in production through reduction, reuse, and repurposing without recycling. Recent research focuses on converting natural and synthetic textile waste such as fibers, yarns, fabrics, trims, and dyes into outputs including recycled fibers and fabrics, nonwovens, lactic acid, glucose, ethanol, biogas, char, and compost. Mechanical recycling is gaining importance for post-industrial waste. When reuse or mechanical recycling is not feasible, chemical and biological treatments are applied. Advances in artificial intelligence and machine learning support efficient sorting, waste prediction, and recycling processes.

Water scarcity has increased the need for efficient treatment technologies such as membrane distillation (MD). PMD performance depends strongly on membrane fabrication parameters, particularly polymer concentration and nanoparticle incorporation, which control key transport and separation properties. This study considers fabrication of membranes using different concentrations of polyvinylidene fluoride (PVDF) with the incorporation of different types of nanoparticles to determine the optimum membrane formulation for membrane distillation applications. The results demonstrate that both PVDF concentration and nanoparticle type play a critical role in membrane performance in terms of permeate flux and salt rejection. Among the nanoparticles studied in this work, carbon nanotubes (CNTs) exhibited the most significant enhancement, leading to a substantial increase in water vapor flux while maintaining excellent separation efficiency. The optimized CNT incorporated membrane achieved approximately 99% salt rejection, with superior flux performance, indicating its strong potential for high-efficiency desalination and water treatment using membrane distillation.

Water scarcity has increased the need for efficient treatment technologies such as membrane distillation (MD). MD performance depends strongly on membrane fabrication parameters, particularly polymer concentration and nanoparticle incorporation, which control key transport and separation properties. This study considers fabrication of membranes using different concentrations of polyvinylidene fluoride (PVDF) with the incorporation of different types of nanoparticles to determine the optimum membrane formulation for membrane distillation applications. The results demonstrate that both PVDF concentration and nanoparticle type play a critical role in membrane performance in terms of permeate flux and salt rejection. Among the nanoparticles studied in this work, carbon nanotubes (CNTs) exhibited the most significant enhancement, leading to a substantial increase in water vapor flux while maintaining excellent separation efficiency. The optimized CNT incorporated membrane achieved approximately 99% salt rejection, with superior flux performance, indicating its strong potential for high-efficiency desalination and water treatment using membrane distillation.

The increasing global energy demand and the transition toward sustainable energy systems have highlighted the importance of energy storage technologies by ensuring efficiency, reliability, and decarbonization. This study reviews chemical and thermal energy storage technologies, focusing on how they integrate with renewable energy sources, industrial applications, and emerging challenges. Chemical Energy Storage systems, including hydrogen storage and power-to-fuel strategies, enable long-term energy retention and efficient use, while thermal energy storage technologies facilitate waste heat recovery and grid stability. Key contributions to this work are the exploration of emerging technologies, challenges in large-scale implementation, and the role of artificial intelligence in optimizing Energy Storage Systems through predictive analytics, real-time monitoring, and advanced control strategies. This study also addresses regulatory and economic barriers that hinder widespread adoption, emphasizing the need for policy incentives and interdisciplinary collaboration. The findings suggest that energy storage will be a fundamental pillar of the sustainable energy transition. Future research should focus on improving material stability, enhancing operational efficiency, and integrating intelligent management systems to maximize the benefits of these technologies for a resilient and low-carbon energy infrastructure.

Over the last decade, increasing interest in drug delivery systems (DDS) has been directed toward the application of machine learning (ML), a sub-field of artif

While several group contribution method (GCM) models have been developed in recent years for the prediction of ionic liquid (IL) properties, some challenges exist in their effective application. Firstly, the models have been developed and tested based on different datasets; therefore, direct comparison based on reported statistical measures is not reliable. Secondly, many of the existing models are limited in the range of ILs for which they can be used due to the lack of functional group parameters. In this paper, we examine two of the most diverse GCMs for the estimation of IL melting point; a key property in the selection and design of ILs for materials and energy applications. A comprehensive database consisting of over 1300 data points for 933 unique ILs, has been compiled and used to critically evaluate the two GCMs. One of the GCMs has been refined by introducing new functional groups and reparametrized to give improved performance for melting point estimation over a wider range of ILs. This work will aid in the targeted design of ILs for materials and energy applications.

Extraction of phenol from aqueous phase to ionic liquid phase.

The thermal conductivities of selected deep eutectic solvents (DESs) were determined using the modified transient plane source (MTPS) method over the temperature range from 295 K to 363 K at atmospheric pressure. The results were found to range from 0.198 W·m⁻¹·K⁻¹ to 0.250 W·m⁻¹·K⁻¹. Various empirical and thermodynamic correlations present in literature, including the group contribution method and mixing correlations, were used to model the thermal conductivities of these DES at different temperatures. The predictions of these correlations were compared and consolidated with the reported experimental values. In addition, the thermal conductivities of DES mixtures with water over a wide range of compositions at 298 K and atmospheric pressure were measured. The standard uncertainty in thermal conductivity was estimated to be less than ± 0.001 W·m⁻¹·K⁻¹ and ± 0.05 K in temperature. The results indicated that DES have significant potential for use as heat transfer fluids.