Document Type

Honors Project On-Campus Access Only


Being able to predict binding energies with computation would provide great utility to several scientists working in fields where surface-related phenomena are important. Here a Gibbs ensemble Monte Carlo simulation is written in Python and used to estimate the binding energy of several environmental contaminants to potential remediation materials. Sampling efficiency is improved by implementing simulated annealing and local elevation. Quantum chemistry energy calculations are achieved with the linear-scaling density functional theory program SIESTA. The program is then used to estimate the gas-phase binding energies of four military contaminants to two potential sequestration materials. The military contami- nants are TNT, FOX-7, DNAN, and NTO. The two adsorbents are cellulose Iα and cellulose triacetate (CTA). The results suggest that cellulose, compared to CTA, has similar or higher gas-phase binding energies to these small molecules. This is a result of cellulose’s ability to hydrogen bond donate. The method provides a way to gain vital chemical insights into adsorption with relatively little marginal human effort. This method may present potential for studying several technologically relevant chemical systems where adsorption is important such as heterogeneous catalysis, photocatalytic reduction, electrochemical energy storage, corrosion inhibition and—as I have shown in this project—to the sequestration of environ- mental contaminants.



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