Globally, environmental challenges have been recognised as a matter of concern. Among these challenges are the reduced availability and quality of drinking water, and greenhouse gases that give rise to change in climate by entrapping heat, which result in respirational illness from smog and air pollution. Globally, the rate of demand for the use of freshwater has outgrown the rate of population increase; as the rapid growth in town and cities place a huge pressure on neighbouring water resources. Besides, the rapid growth in anthropogenic activities, such as the generation of energy and its conveyance, release carbon dioxide and other greenhouse gases, warming the planet. Polymer nanocomposite has played a significant role in finding solutions to current environmental problems. It has found interest due to its high potential for the reduction of gas emission, and elimination of pollutants, heavy metals, dyes, and oil in wastewater. The revolution of integrating developed novel nanomaterials such as nanoparticles, carbon nanotubes, nanofibers and activated carbon, in polymers, have instigated revitalizing and favourable inventive nanotechnologies for the treatment of wastewater and gas separation. This review discusses the effective employment of polymer nanocomposites for environmental utilizations. Polymer nanocomposite membranes for wastewater treatment and gas separation were reviewed together with their mechanisms. The use of polymer nanocomposites as an adsorbent for toxic metals ions removal and an adsorbent for dye removal were also discussed, together with the mechanism of the adsorption process. Patents in the utilization of innovative polymeric nanocomposite membranes for environmental utilizations were discussed.

This study was aimed at evaluating the adsorption capacity of novel banana peel activated carbon (BPAC) modified with Al₃O₂@chitosan for the removal of cadmium (Cd²⁺) and lead (Pb²⁺) from wastewater. Characterization techniques such as X-ray diffraction, scanning electron microscopy, transmission electron microscopy, Fourier transformed infrared (FTIR) spectroscopy, and Brunauer–Emmett–Teller analysis confirmed the synthesized BPAC@Al₃O₂@chitosan composite material. The univariate approach was used to study the influence of different experimental parameters (such as adsorbent mass, sample pH, and contact time) that affects simultaneous removal of Cd²⁺ and Pb²⁺ ions. Kinetic results showed that adsorption favored the pseudo-second-order kinetic model, whereas the adsorption of Cd²⁺ and Pb²⁺ was best described by the Langmuir model and the adsorption capacity for Cd²⁺ and Pb²⁺ was 46.9 mg g⁻¹ and 57.1 mg g⁻¹, respectively, for monolayer adsorption. It was shown the BPAC composite can be re-used until the third cycle of adsorption–desorption (% Re > 80). Based on the obtained results, it can be concluded that the prepared BPAC@Al₃O₂@chitosan composite material is cost effective, as it is generated from waste banana peels and can be re-used. In addition, the prepared material was able to remove Cd²⁺ and Pb²⁺ up to 99.9%.

This paper presents the investigation of a two dimensional coupled model of heat and mass transfer in a mixture of AB ₂ – AB ₅ metal hydride (MH) systems of a cylindrical configuration during hydrogen sorption using COMSOL 5.3a commercial software. The parametric study on the sorption process has been studied with variation of heat transfer coefficient (HTC), and activation energy (AE) to understand the effects they have on the reaction kinetics of the sorption process. The simulation results demonstrate the importance of mutual dependence between the temperature propagation in the body of metal hydride, the absorbed concentration of the hydrogen gas, and the gas pressure for the absorption of hydrogen gas in metal hydrides. The decrease in the activation energy is found to have significant effect on the dynamic performances of hydrogen absorption in the MH reactors with an increased amount of hydrogen conversion, whilst the variation of heat transfer coefficient displayed insignificant change in hydrogen conversion. The simulated results show good agreement with the experimental results obtained from HYSA Systems and were implemented for use in the STILL RX60-30L electric forklift fuel cell applications designed by HYSA Systems in the University of the Western Cape.

Solvent-assisted APTES grafting on LiCoO₂ establishes stable Si–O–Co/Si–O–Si linkages, preserves crystallinity, and exposes amines for further modification. Kinetic modeling confirms pseudo-second-order chemisorption as the dominant mechanism.

Artificial intelligence (AI) is revolutionizing polymer engineering by shifting materials development from traditional trial-and-error to integrated, data-driven, and autonomous workflows. This review summarizes recent progress in AI-driven polymer science, including structural representation, generative molecular design, property prediction, retrosynthetic planning, and closed-loop experimental processes. We discuss how polymer-specific encodings like BigSMILES and graph-based models enable machine-readable macromolecular structures, supporting transformer models, graph neural networks, and uncertainty-aware predictors that accurately estimate thermal, mechanical, dielectric, and functional properties. Beyond prediction, we examine generative architectures such as variational autoencoders, adversarial models, diffusion models, and reinforcement learning that enable inverse polymer design by exploring vast chemical spaces under performance constraints. Combining these computational approaches with techniques like Bayesian optimization, active learning, and autonomous labs signifies a shift toward closed-loop polymer engineering, where AI continuously suggests, synthesizes, analyzes, and refines materials with minimal human intervention. Key challenges, including data scarcity, polymer-specific representation learning, model interpretability, infrastructure integration, and sustainability goals, are thoroughly considered. Emerging solutions such as FAIR data ecosystems, physics-informed neural networks, explainable AI, and modular self-driving laboratories are evaluated for scalable implementation. Overall, these advancements point toward autonomous polymer discovery, with AI serving not only as a predictive tool but as the foundation for intelligent, adaptive, and sustainable polymer engineering.