Steepest-Entropy-Ascent Framework for Predicting Arsenic Adsorption on Graphene Oxide Surfaces: A Case Study.
Adriana Saldaña-Robles1, Cesar Damian-Ascencio2, Michael R von Spakovsky3
1Department of Agricultural Engineering, University of Guanajuato, Irapuato 36500, Mexico.
Journal of Chemical Information and Modeling
|June 16, 2025
Summary
This study models arsenic(V) removal by graphene oxide using a novel quantum thermodynamic framework. The approach accurately predicts adsorption behavior, aiding water treatment technology development.
Area of Science:
- Environmental Chemistry
- Physical Chemistry
- Computational Chemistry
Background:
- Arsenic(V) contamination in water poses significant public health risks.
- Accurate modeling of both equilibrium and transient adsorption is crucial for effective water treatment.
- Graphene oxide (GO) shows promise as an adsorbent for arsenic removal.
Purpose of the Study:
- To apply the steepest-entropy-ascent quantum thermodynamic (SEAQT) framework to model As(V) adsorption on GO.
- To capture both equilibrium and transient adsorption dynamics without empirical rate laws.
- To predict the performance of GO-based water treatment technologies.
Main Methods:
- Developed a nonequilibrium equation of motion from the steepest-entropy-ascent principle for a five-component system.
- Utilized a Replica-Exchange Wang-Landau algorithm to generate energy eigenstructure.
- Employed an artificial neural network for extrapolation to relevant contaminant concentrations.
Main Results:
- Predicted time-dependent adsorption capacity and stable-equilibrium arsenic concentration.
- Modeled the pH dependence of removal efficiency.
- Achieved equilibrium capacity predictions within 5% of experimental isotherms.
- Demonstrated alignment of characteristic adsorption time with reported kinetics.
Conclusions:
- The SEAQT framework offers a thermodynamically consistent and fully predictive tool for water treatment.
- This model can guide the design and optimization of adsorbent-based technologies for arsenic removal.
- The study highlights the potential of computational methods in addressing environmental contamination challenges.
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