Márcio Oliveira Alves, Felipe Silva Carvalho, João Pedro Braga
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引用次数: 0
Abstract
Context
Underlying the adsorption mechanism is not a simple task due to the complexity of the process. In this context, statistical mechanics provides a versatile approach with relatively low computational cost for describing adsorption phenomena. An alternative strategy consists of obtaining the adsorption potential energy from experimental data through the solution of an inverse problem. In this work, the adsorption potential energy was determined directly from adsorption isotherms using experimental datasets for both aqueous- and gas-phase adsorption systems. The proposed methodology was compared with the traditional two-step procedure, in which the second adsorption virial coefficient is first determined and subsequently used for inversion of the potential energy. Due to the ill-posed nature of the problem, the conventional approach exhibits significant instability and error propagation. In contrast, the direct inversion method provides excellent results, achieving values of \(R^2 \sim 0.99\).
Methods
A methodology grounded in statistical physics was developed to obtain the adsorption potential energy directly from isotherm data. The proposed approach formulates the problem as a tensor contraction, eliminating the need for the traditional two-step procedure involving the intermediate determination of the second adsorption virial coefficient.
期刊介绍:
The Journal of Molecular Modeling focuses on "hardcore" modeling, publishing high-quality research and reports. Founded in 1995 as a purely electronic journal, it has adapted its format to include a full-color print edition, and adjusted its aims and scope fit the fast-changing field of molecular modeling, with a particular focus on three-dimensional modeling.
Today, the journal covers all aspects of molecular modeling including life science modeling; materials modeling; new methods; and computational chemistry.
Topics include computer-aided molecular design; rational drug design, de novo ligand design, receptor modeling and docking; cheminformatics, data analysis, visualization and mining; computational medicinal chemistry; homology modeling; simulation of peptides, DNA and other biopolymers; quantitative structure-activity relationships (QSAR) and ADME-modeling; modeling of biological reaction mechanisms; and combined experimental and computational studies in which calculations play a major role.