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arXiv · 2410.05157

Steepest-Entropy-Ascent Framework for Predicting Arsenic Adsorption on Graphene Oxide Surfaces -- A Case Study

Adriana Saldana-Robles·Cesar Damian·Michael R. von Spakovsky·William T. Reynolds Jr
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Abstract

Water contamination by arsenic(V) constitutes a major public-health concern, underscoring the need for models that capture both equilibrium and transient adsorption behaviour. A framework that can do so is the steepest-entropy-ascent quantum thermodynamic (SEAQT) framework, which is used here to describe the uptake of As(V) on graphene oxide (GO) across pollutant concentrations of 25-350 mg/L. A non-equilibrium equation of motion derived from the steepest-entropy-ascent principle for a five-component system (water, arsenic, two GO functional groups, and protons is solved with an energy eigenstructure generated by a Replica-Exchange Wang-Landau algorithm and then extrapolated to relevant contaminant concentrations via an artificial neural network. Without recourse to empirical rate laws, the model predicts the time-dependent adsorption capacity, the stable-equilibrium arsenic concentration, and the pH dependence of removal efficiency. Equilibrium capacities are reproduced within 5 % of experimental isotherms, and the characteristic adsorption time aligns with the reported kinetics. These results indicate that SEAQT framework provides a thermodynamically consistent, fully predictive tool for designing and optimising adsorbent-based water-treatment technologies.

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Adriana Saldana-Robles, Cesar Damian, Michael R. von Spakovsky, William T. Reynolds Jr. 2025-06-11. Steepest-Entropy-Ascent Framework for Predicting Arsenic Adsorption on Graphene Oxide Surfaces -- A Case Study. https://arxiv.org/abs/2410.05157

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