Simulation-Guided Synthesis and Evaluation of Advanced Nanomaterials for Environmental Remediation
DOI:
https://doi.org/10.57159/jcmm.4.3.25195Keywords:
Advanced Nanomaterials, Environmental Remediation, Molecular Dynamics, Adsorption Efficiency, Simulation-Guided SynthesisAbstract
This study presents a simulation-guided strategy for the synthesis, characterization, and environmental application of advanced nanomaterials, aiming to address the growing concerns of pollutant accumulation in air, water, and soil matrices. The research leverages atomistic and electronic modeling tools, including Molecular Dynamics (MD) and Density Functional Theory (DFT), to identify and optimize structural and thermodynamic parameters critical for nanomaterial efficacy. Simulations performed using platforms such as LAMMPS, GROMACS, VASP, and Quantum ESPRESSO were instrumental in predicting nanoparticle stability, surface energy, and reactivity under environmentally relevant conditions. The study further incorporates environmental transport modeling via COMSOL Multiphysics to predict contaminant flow and interaction with the synthesized nanostructures. Experimentally, nanomaterials synthesized through hydrothermal, sol-gel, and chemical precipitation routes were characterized using SEM, XRD, and FTIR. Surface area and morphology analyses revealed that the nanostructures possessed high porosity and uniform distribution with an average particle size of 30 nm and a specific surface area of 250 m2/g. The adsorption studies showed pollutant removal efficiencies of 95% for heavy metals and 90% for organic compounds, with an adsorption capacity of 500 mg/g. These performance metrics are indicative of favorable kinetics, supported by pseudo-second-order models suggesting chemisorption as the dominant removal mechanism. The findings demonstrate that simulation-informed synthesis can systematically guide material development toward achieving optimal interaction with environmental pollutants. The combined use of in silico and experimental approaches ensures both predictive robustness and empirical validation. This hybrid framework not only enhances the functional reliability of nanomaterials but also accelerates the development of environmentally sustainable technologies. The approach presented herein offers a scalable path toward the deployment of nanotechnology in large-scale remediation operations, contributing meaningfully to pollution control and ecosystem restoration.
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