通过积极学习潜力分子动力学模拟来解开MXenes在水系统中的氧化行为
Pengfei Hou1, Yumiao Tian1, Yu Xie1
1Key Laboratory of Physics and Technology for Advanced Batteries (Ministry of Education), College of Physics, Jilin University, 130012, Changchun, P. R. China.
Angewandte Chemie (International ed. in English)
|June 14, 2023
概括
研究人员开发了一种低成本的计算方法来研究MXene在水中的降解. 他们发现质子和氧化物显著减缓MXene氧化,揭示了材料稳定性的洞察力.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 纳米技术 纳米技术
背景情况:
- MXenes是具有显著应用潜力的二维材料.
- 在潮湿条件下MXene的降解阻碍了实际使用.
- 了解MXene在水性环境中的氧化是至关重要的.
研究的目的:
- 为水性MXene系统开发一个具有成本效益的计算模型.
- 为了研究大型水性MXene系统的氧化动力学.
- 为了阐明MXene氧化发生的原子层机制.
主要方法:
- 结合深度神经网络与积极学习方案.
- 开发了一个神经网络潜力 (NNP),用于初始精度.
- 在纳秒时间尺度上模拟超大水性MXene系统的氧化.
主要成果:
- 原子层次的MXene氧化过程的可视化.
- 随着时间的推移,观察到MXene氧化程度的指数式衰变.
- 识别了自由质子和氧化物作为进一步氧化抑制剂.
结论:
- 这项研究提供了首次对超大尺寸的水性MXene氧化物的动力探索.
- 计算结果与MXene氧化速率的实验测量结果一致.
- 结果为开发增强MXene稳定性的策略铺平了道路.
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