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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.05157 |
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| _version_ | 1866908402623447040 |
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| author | Saldana-Robles, Adriana Damian, Cesar von Spakovsky, Michael R. Reynolds Jr, William T. |
| author_facet | Saldana-Robles, Adriana Damian, Cesar von Spakovsky, Michael R. Reynolds Jr, William T. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_05157 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Steepest-Entropy-Ascent Framework for Predicting Arsenic Adsorption on Graphene Oxide Surfaces -- A Case Study Saldana-Robles, Adriana Damian, Cesar von Spakovsky, Michael R. Reynolds Jr, William T. Chemical Physics 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. |
| title | Steepest-Entropy-Ascent Framework for Predicting Arsenic Adsorption on Graphene Oxide Surfaces -- A Case Study |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2410.05157 |