Growing concerns about environmental pollution have highlighted the need for efficient and sustainable methods to remove dye contamination from various ecosystems. In this context, computational methods such as molecular dynamics (MD), Monte Carlo (MC) simulations, quantum mechanics (QM) calculations, and machine learning (ML) methods are powerful tools used to study and predict the adsorption processes of dyes on various adsorbents. These methods provide detailed insights into the molecular interactions and mechanisms involved, which can be crucial for designing efficient adsorption systems. MD simulations, detailing molecular arrangements, predict dyes' adsorption behaviour and interaction energies with adsorbents. They simulate the entire adsorption process, including surface diffusion, solvent layer penetration, and physisorption. QM calculations, especially density functional theory (DFT), determine molecular structures and reactivity descriptors, aiding in understanding adsorption mechanisms. They identify stable adsorption configurations and interactions like hydrogen bonding and electrostatic forces. MC simulations predict equilibrium properties and adsorption energies by sampling molecular configurations. ML methods have proven highly effective in predicting and optimizing dye adsorption processes. These models offer significant advantages over traditional methods, including higher accuracy and the ability to handle complex datasets. These methods optimize adsorption conditions, clarify adsorbent functionalization roles, and predict dye removal efficiency under various conditions. This research explores MD, MC, QM, and ML approaches to connect molecular interactions with macroscopic adsorption phenomena. Probing these techniques provides insights into the dynamics and energetics of dye pollutants on adsorption surfaces. The findings will aid in developing and optimizing new materials for dye removal. This review has significant implications for environmental remediation, offering a comprehensive understanding of adsorption at various scales. Merging microscopic data with macroscopic observations enhances knowledge of dye pollutant adsorption, laying the groundwork for efficient, sustainable removal technologies. Addressing the growing challenges of ecosystem protection, this study contributes to a cleaner, more sustainable future.

The development of stimuli-responsive nanomaterials holds immense promise for enhancing the efficiency and effectiveness of water treatment processes. These smart materials exhibit a remarkable ability to respond to specific external stimuli, such as light, pH, or magnetic fields, and trigger the controlled release of encapsulated pollutants. By precisely regulating the release kinetics, these nanomaterials can effectively target and eliminate contaminants without compromising the integrity of the water system. This review article provides a comprehensive overview of the advancements in light-activated and pH-sensitive nanomaterials for controlled pollutant release in water treatment. It delves into the fundamental principles underlying these materials' stimuli-responsive behaviour, exploring the design strategies and applications in various water treatment scenarios. In particular, the article indicates how integrating stimuli-responsive nanomaterials into existing water treatment technologies can significantly enhance their performance, leading to more sustainable and cost-effective solutions. The synergy between these advanced materials and traditional treatment methods could pave the way for innovative approaches to water purification, offering enhanced selectivity and efficiency. Furthermore, the review highlights the critical challenges and future directions in this rapidly evolving field, emphasizing the need for further research and development to fully realize the potential of these materials in addressing the pressing challenges of water purification.

Gastrointestinal drug pollutants pose environmental risks. Our study explores the adsorption of famotidine (FA) and pantoprazole (PA) using Au-ddoped@ZIF-90-gglycerol adsorbent (A@Z/G), emphasizing pH-sensitive effects on ecosystems.