Isotherm Modeling of Nanomaterial Adsorption for Water Pollution Control

A persistent challenge in water‐treatment research lies in the frequent misapplication and misinterpretation of adsorption isotherm models when evaluating nanomaterial performance, resulting in inconsistent capacity estimates and suboptimal design decisions. To address this issue, the chapter provides a systematic evaluation of classical isotherm models (Langmuir, Freundlich, Temkin) alongside a critical examination of common methodological errors—such as overreliance on correlation coefficients, inadequate equilibrium verification, and neglect of variables including pH, ionic strength, and nanoparticle aggregation. By elucidating these limitations, the study establishes a rigorous framework for the selection, fitting, and validation of isotherm models in nanoadsorption investigations. A set of best‐practice guidelines is presented, encompassing multi‐model fitting strategies, employment of residual error metrics (e.g., RMSE, AIC), implementation of controlled equilibration protocols, and adherence to FAIR data reporting standards. Key future directions are identified, including the development of nano‐specific isotherm frameworks, integration of machine‐learning algorithms for predictive model selection, validation under complex real‐world water matrices, and exploration of biodegradable bioadsorbents. These consolidated methodologies and forward‐looking perspectives equip researchers, process engineers, and regulatory stakeholders with actionable recommendations to enhance reproducibility, improve adsorption capacity estimations, and support evidence‐based policy development for responsible deployment of nanomaterial adsorbents in sustainable water‐treatment systems.