有效地探索过渡金属装饰的MXene用于一氧化碳探测,使用集成主动学习和密度函数理论
Kajjana Boonpalit1, Jiramet Kinchagawat1, Chanatkran Prommin1
1School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong 21210, Thailand.
本研究确定了最佳的过渡金属装饰的MXene材料,用于增强一氧化碳 (CO) 气体传感. Sc@Zr3C2O2和Y@Zr3C2O2在实时CO检测方面显示出有前途的性能.
科学领域:
- 材料科学 材料科学 材料科学
- 化学传感器 化学传感器
- 计算化学的计算化学
背景情况:
- 为了化学安全,迫切需要实时检测一氧化碳 (CO).
- 由于其特性,MXene (2D材料) 对气体传感具有前景.
- 纯净的MXene与CO的相互作用有限;过渡金属装饰是关键.
研究的目的:
- 450种过渡金属装饰的MXene (TM@MXene) 组合用于CO传感的高通量选.
- 使用一个集成的积极学习 (AL) 和密度函数理论 (DFT) 管道.
- 确定最佳的TM@MXene候选人,以敏感和快速检测CO.
主要方法:
- 使用高通量选方法.
- 集成主动学习 (AL) 使用晶体图卷积神经网络 (CGCNN) 作为替代模型.
- 用密度函数理论 (DFT) 进行计算分析.
主要成果:
- 成功地加速了使用AL的CO传感器候选人的选过程.
- 确定了Sc@Zr3C2O2和Y@Zr3C2O2作为表现最佳的TM@MXene候选物.
- 根据恢复时间,表面稳定性,电荷转移和CO灵敏度评估候选物.
结论:
- Sc@Zr3C2O2和Y@Zr3C2O2显示了实际CO传感应用的巨大潜力.
- 积极学习框架有效地导航大型化学空间进行材料发现.
- 在组合化学中,AL框架可适应微调材料特性.
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