Chemometric Modeling of Emerging Materials for the Removal of Environmental Pollutants

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Widespread usage of pharmaceuticals, personal care products (PPCPs), and agrochemicals followed by the release of household waste, industrial and hospital wastes has affected the environment and ecosystems immensely. These toxic chemicals are primarily classified under contaminants of emerging concerns (CEC) and/or environmental pollutants (EPs). Due to their harmful effects, timely removal of these EPs is an utmost requirement under risk management of the environment. A series of traditional techniques are accepted by the environmental organization to remove these products from the environment. Adsorption is one of the low-budget, easy to perform, and efficient approaches. With the advancement of nanotechnology, materials like carbon nanotubes (CNTs), magnetic nanoparticles, modified activated carbons/biochar, clay polycations, polyamide nanofilters (PNF), etc. have emerged as the materials of interest at present time. Along with the existing hazardous chemicals in the ecosystems, every day thousands of new chemicals are introduced to the environment. As a result, there is a continuous requirement for efficient materials which are capable of adsorbing these contaminants from the environment. In this perspective, chemometric-based modeling and machine learning (ML) models are shown to be capable of predicting important structural and physicochemical features that are responsible for the efficient adsorption property of these emerging materials. Once these features are identified, further modification in the structure of these materials can be performed to make them much more efficient adsorbers than the existing materials. The present chapter discusses the CECs and EPs, emerging materials in the present time, along with details about the chemometric and ML models which can be employed for modeling of the adsorption of EPs. Finally, successful case studies for the prediction of adsorption of EPs onto different emerging materials are meticulously discussed with mechanistic interpretations.

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Challenges and Advances in Computational Chemistry and Physics

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