Deep Potential for Interaction between Hydrated Cs+ and Graphene.
Yangjun Qin1,2, Liuhua Mu3, Xiao Wan4
1School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
Langmuir : the ACS Journal of Surfaces and Colloids
|April 29, 2025
Summary
A new deep neural network model accurately predicts interactions between hydrated cesium ions (Cs+) and graphene. This research enhances understanding of graphene membrane adsorption for applications like radionuclide removal.
Area of Science:
- Materials Science
- Computational Chemistry
- Physical Chemistry
Background:
- Hydrated cation-π interactions significantly impact graphene-based membrane performance.
- Limited understanding of cesium ion (Cs+) interactions with graphene hinders adsorption studies.
Purpose of the Study:
- To develop a deep neural network potential model for predicting Cs+-graphene interactions.
- To investigate the adsorption behavior of hydrated Cs+ on graphene surfaces.
Main Methods:
- Developed a deep neural network potential function with DFT-level accuracy.
- Utilized the deep potential to simulate graphene surface solution properties.
- Calculated adsorption energy and charge for varying water molecule counts.
Main Results:
- The deep potential accurately predicts Cs+-graphene interactions.
- Water molecules were found to weaken the interaction between Cs+ and graphene.
- Simulations revealed insights into water density distribution and ion dynamics.
Conclusions:
- The developed deep potential is a powerful tool for studying hydrated cation adsorption on graphene.
- This approach offers novel solutions for radionuclide management using graphene membranes.
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